PAM tools gate connections; databases also need SQL review, schema change workflow, and statement-level approval. Why most teams use PAM with Bytebase.

Bytebase vs. PAM: Why Database Teams Need More Than Privileged Access Management

Bytebase vs. Atlas compared across migration approach, CI/CD, access control, and pricing. Updated for Atlas v1.1 and Bytebase 3.16.

Bytebase vs. Atlas: a side-by-side comparison for database schema migration

A detailed comparison of Convex and Supabase covering architecture, real-time capabilities, data model, pricing, and schema management to help you choose.

Convex vs Supabase: Which Backend Platform Should You Choose?

Compare Snowflake Dynamic Data Masking with database views and Bytebase fleet-wide masking — edition gate, policy management, audit trail.

Snowflake Dynamic Data Masking (DDM) and Alternatives

An extensive comparison between Supabase and MongoDB on performance, usability, operability, ecosystem and more.

Supabase vs. MongoDB: a Complete Comparison in 2025

When evaluating database management tools, DataGrip and Bytebase represent two different philosophies: individual developer productivity versus team collaboration and governance.

Bytebase vs. DataGrip: a side-by-side comparison for database management

Supabase pricing starts free. We break down real costs for Pro ($25/mo) and Team ($599/mo) plans, explain overages, and compare each service head-to-head with AWS.

Supabase vs AWS: Feature and Pricing Comparison (2026)

Is Supabase free? Compare Supabase pricing vs AWS RDS and Aurora across free tier, Pro, and production plans. Includes Supabase free tier limits, storage costs, and compute pricing for 2026.

Supabase Pricing vs AWS RDS: Is Supabase Free? Full Cost Comparison (2026)

Evaluate Prisma and TypeORM, and help you to choose the right TypeScript ORM for 2025

Prisma vs TypeORM: The Better TypeScript ORM in 2025

An extensive comparison between DynamoDB and Cassandra on performance, usability, operability, ecosystem and more.

DynamoDB vs. Cassandra: a Complete Comparison in 2025

An extensive comparison between DynamoDB and MongoDB on performance, usability, operability, ecosystem and more.

RDS vs. DynamoDB: a Complete Comparison in 2025

How gh-ost and pt-online-schema-change compare on architecture, cut-over, foreign keys, and runtime control — for teams choosing a MySQL online schema migration tool.

gh-ost vs pt-online-schema-change

GitHub and Bitbucket are both tools for code collaboration. This article compares the features and pricing of GitHub and Bitbucket.

GitHub vs Bitbucket:a Complete Comparison in 2025

Drizzle ORM vs Prisma comparison: type safety, performance, bundle size, serverless support, migrations, and ecosystem. Find out which TypeScript ORM is right for your project in 2026.

Drizzle ORM vs Prisma: Which TypeScript ORM Should You Use in 2026?

An engineering perspective to evaluate Google AlloyDB and Cloud SQL, and help you to choose the right Google Cloud database for 2025

AlloyDB vs. Cloud SQL: engineering guide to choose the right Google Cloud database for 2025

A simple comparison of AWS Aurora versus RDS pricing in 2025, helping you understand key differences and choose the right database service for your needs and budget.

AWS Aurora vs. RDS Pricing: A Detailed Comparison 2025

An extensive comparison between GitLab CI and GitHub Actions on features, performance, and more.

GitLab CI vs. GitHub Actions: a Complete Comparison in 2025

An extensive comparison between Oracle and PostgreSQL on data model, query language, scalability, performance, and more.

Oracle vs. PostgreSQL: a Complete Comparison in 2025

An extensive comparison between MongoDB and MySQL on data model, query language, scalability, performance, and more.

MongoDB vs MySQL: a Complete Comparison in 2025

An extensive comparison between MongoDB and Redis on performance, usability, operability, ecosystem and more.

MongoDB vs Redis: a Complete Comparison in 2025

DynamoDB and PostgreSQL are both popular databases, but they are different. This post compares them based on their key differences, strengths, and ideal use cases.

DynamoDB vs PostgreSQL: A Concise Comparison

Supabase vs. Firebase compared across database, auth, real-time, serverless functions, AI, local dev, self-hosting, and pricing — updated for 2026 with Firebase Data Connect, Supabase Branching, and the latest product shifts.

Supabase vs. Firebase: a Complete Comparison in 2026

An extensive comparison between Snowflake and Redshift on features, architecture, development workflow, operability, pricing and more.

Snowflake vs. Redshift: a Complete Comparison in 2025

DynamoDB vs MongoDB: compare AWS DynamoDB and MongoDB on data model, query capabilities, scalability, pricing, global distribution, and security. Includes use case recommendations and real-world examples.

DynamoDB vs MongoDB: Which NoSQL Database Should You Choose? (2026)

A Strategic Comparison Between DevOps and Platform Engineering

Platform Engineering vs. DevOps: A Strategic Comparison in 2025

An extensive comparison between CockroachDB and PostgreSQL on license, architecture, performance, scalability, compatibility, deployment options.

CockroachDB vs. Postgres: a Complete Comparison in 2025

An extensive comparison between TiDB and MySQL on license, architecture, performance, scalability, online ddl, compatibility, deployment options.

TiDB vs. MySQL: a Complete Comparison in 2025

DevOps and DevSecOps are methodologies aimed at improving software development and delivery, but they differ in their focus and integration of security.

DevOps vs. DevSecOps: What's the Difference

MySQL vs SQL Server: compare features, performance, pricing, AI capabilities, cloud pricing, and use cases. Updated for MySQL 9.2 and SQL Server 2025 with detailed licensing and TCO analysis.

MySQL vs SQL Server: Key Differences and Which to Choose (2026)

An extensive comparison between PostgreSQL 18 and SQL Server 2025 — features, architecture, query optimization, development workflow, pricing, and a decision framework for picking the right one in 2026.

Postgres vs. SQL Server: a Complete Comparison in 2026

An extensive comparison between pgAdmin and DBeaver on features, user experience, database support, extensibility, performance, and more.

pgAdmin vs. DBeaver: a Complete Comparison in 2025

An extensive comparison between TiDB and CockroachDB on license, architecture, compatibility, scalability

TiDB vs. CockroachDB: the Distributed Clash between MySQL and PostgreSQL

Bytebase vs StrongDM in 2026: a side-by-side comparison for database just-in-time (JIT) access control, covering permission models, SQL review, data masking, approval flow, audit log, and pricing.

Bytebase vs. StrongDM: a side-by-side comparison for database access control and JIT

An extensive comparison between GitHub and GitLab on history, deployment model, tech stack, repository management, code review, code search, ci/cd, security, project management, ai, pricing

GitHub vs. GitLab: a Complete Comparison in 2025

While MariaDB was initially a fork of MySQL, the two have evolved over time. This post will explore a detailed comparison between MySQL and MariaDB, focusing on key aspects such as performance, licensing, compatibility, and feature set

MySQL vs. MariaDB: a Complete Comparison in 2025

Neon and Supabase are the 2 new generation Postgres service providers. This is an extensive comparison between Neon and Supabase on architecture, compatibility, agentic workload, developer workflow, scalability, integration, compliance, pricing and more.

Neon vs. Supabase: Which One Should I Choose

Compare Flyway vs Liquibase side-by-side: features, pricing, supported databases, SQL auto check, rollback, and schema drift detection. Updated for Flyway 2026 and Liquibase 5.0.

Flyway vs. Liquibase: The Definitive Comparison in 2026

Bytebase vs. CloudBeaver compared across product scope, supported databases, change review, access control, data masking, and pricing. Updated for Bytebase 3.16 and CloudBeaver 25.

Bytebase vs. CloudBeaver: a side-by-side comparison for web-based database management

Bytebase vs Metabase in 2026: a side-by-side comparison of database tooling for developer teams and data teams, covering pricing, features, SQL Review, change workflow, and BI.

Bytebase vs. Metabase: a side-by-side comparison for developer and data teams

Bytebase vs. DBeaver compared across product scope, supported databases, change workflow, access control, data masking, and pricing. Updated for Bytebase 3.16 and DBeaver 25.

Bytebase vs. DBeaver: a side-by-side comparison for database management

An engineering perspective to evaluate Amazon Aurora and RDS, and help you to choose the right AWS database for 2025

Aurora vs. RDS: engineering guide to choose the right AWS database for 2025

An extensive comparison between Postgres and MongoDB on performance, reliability, scalability, usability, operability, ecosystem and more.

Postgres vs. MongoDB: a Complete Comparison in 2025

PostgreSQL vs MySQL: in-depth comparison of performance, features, security, extensibility, JSON support, replication, and ecosystem. Updated for Postgres 18 and MySQL 9.x with real-world scaling references.

PostgreSQL vs MySQL: Which Database Should You Choose in 2026?

An extensive comparison between PlanetScale and Neon on architecture, compatibility, developer workflow, scalability, operability, integration, compliance, pricing and more.

PlanetScale vs. Neon: the Continued Saga between MySQL and PostgreSQL

Bytebase vs. Flyway compared across migration workflow, SQL review, access control, pricing, and CI/CD. Updated for Flyway Desktop and Bytebase 3.16.

Bytebase vs. Flyway: a side-by-side comparison for database schema migration

Bytebase vs. Liquibase compared across migration workflow, SQL review, access control, pricing, and CI/CD. Updated for Liquibase 5.0 Secure and Bytebase 3.16.

Bytebase vs. Liquibase: a side-by-side comparison for database schema migration

When evaluating database management tools, Navicat and Bytebase represent two different philosophies: individual desktop productivity versus team collaboration and governance.

Bytebase vs. Navicat: a side-by-side comparison for database management

<HintBlock type="info">

This post is maintained by Bytebase, an open-source database DevSecOps tool which supports both Supabase and Firebase Data Connect. We update the post every year.

</HintBlock>

| Update History | Comment |
| -------------- | ----------------------------------------------------------------------------------------------- |
| 2025/04/19 | Initial version. |
| 2026/04/19 | Firebase Data Connect (PostgreSQL backend), Supabase Branching GA, AI tooling, updated pricing. |

## Introduction

Supabase and Firebase are two leading **Backend-as-a-Service (BaaS) platforms** that let developers ship applications without managing backend infrastructure. They used to sit on opposite sides of the relational / NoSQL divide — that line has moved.

**Firebase** began in 2011 as a real-time NoSQL database and was acquired by Google in 2014. In 2024 it launched **Firebase Data Connect**, a managed PostgreSQL backend with a GraphQL-like query layer — its first SQL offering, directly answering the comparison below.

**Supabase**, launched in 2020, emerged as an **open-source alternative** to Firebase, built on **PostgreSQL** with SQL support and self-hosting. Over 2025–2026 it shipped **Branching** (Git-based preview environments), physical read replicas, background Edge Functions, and MCP server integration for AI agents.

The 2026 comparison is less "SQL vs. NoSQL" and more "open-source PostgreSQL platform vs. Google-ecosystem BaaS that now includes PostgreSQL."

## Quick Comparison Table

| Feature | Supabase | Firebase |
| ------------------------ | ------------------------------------------------------------------------ | ---------------------------------------------------------------------------------- |
| **Philosophy** | Open-source, standards-based, Postgres-first | Proprietary, fully-managed, Google ecosystem |
| **Database** | PostgreSQL (relational) | Firestore (NoSQL) + Realtime DB (NoSQL) + **Data Connect** (PostgreSQL via Cloud SQL) |
| **Query** | Full SQL, joins, CTEs, window functions | Firestore: document queries + vector search + aggregations; Data Connect: GraphQL over SQL |
| **Auth** | Email, social, phone, MFA, passkeys, anonymous | Email, social, phone, MFA, passkeys, anonymous |
| **Real-time** | Postgres logical replication + Phoenix Channels (Realtime v2) | Purpose-built Firestore/Realtime DB streams |
| **Offline support** | Client SDKs (basic) | Mature offline persistence in Firestore / Realtime DB |
| **Functions** | Edge Functions (Deno, TypeScript), background tasks, WebSocket | Cloud Functions (Node.js, Python, Go, Java, .NET), App Hosting (Next.js, Angular) |
| **Storage** | S3-compatible with RLS, Smart CDN | Google Cloud Storage with Security Rules |
| **AI / ML** | pgvector, pg_mooncake (Iceberg), MCP server, OpenAI/Hugging Face integrations | Firebase AI Logic, Genkit, Gemini API, Firestore vector search, Crashlytics AI |
| **Preview environments** | Supabase Branching (per-PR preview) | Hosting preview channels |
| **Local development** | `supabase start` (full local stack via Docker) | Firebase Emulator Suite (Firestore, Auth, Functions, etc.) |
| **Self-hosting** | Yes (open-source) | No |
| **Pricing model** | Predictable tiered ($0 / $25 / $599 / custom) | Usage-based pay-as-you-go (Blaze) on top of Spark free tier |
| **Best for** | SQL-first apps, open-source control, self-hosting, AI on Postgres | Mobile / web apps in Google ecosystem, real-time sync, rapid prototyping |

## Core Philosophy

**Supabase** embraces open-source and Postgres as the single source of truth. Auth, storage, edge functions, and real-time all read from and enforce policies in the same Postgres database. You can walk away with a `pg_dump` at any point.

**Firebase** is Google-first and service-oriented — each capability (Firestore, Auth, Functions, Hosting, Data Connect) is a managed service with its own SDK. Data Connect narrowed the gap in 2024 by adding Postgres, but the operational model is still "Google manages it, you consume APIs."

## Database Capabilities

**Supabase** uses a single Postgres instance per project:

- Full ACID, joins, CTEs, stored procedures, triggers
- PostgreSQL extensions (pgvector, PostGIS, pgmq, pg_cron, pg_mooncake for Iceberg)
- Physical read replicas (GA)
- Row Level Security as the primary authorization mechanism

**Firebase** now offers three database options:

- **Firestore** — NoSQL document store with collections, strong offline support, and recent additions including vector search and aggregation queries (`count()`, `sum()`, `avg()`)
- **Realtime Database** — lightweight JSON tree, optimized for mobile real-time sync
- **Firebase Data Connect** — managed PostgreSQL (Cloud SQL under the hood) with a GraphQL-like query layer; addresses the historical "Firebase can't do joins" complaint

For most new projects, the choice inside Firebase is between Firestore (if you want the classic real-time mobile experience) and Data Connect (if you want relational). Supabase remains the simpler story if you want one Postgres database, no SDK-specific schemas, and full SQL access.

For a deeper look at managed Postgres trade-offs, see [Neon vs. Supabase](/blog/neon-vs-supabase).

## Authentication and Security

Both platforms cover the mainstream cases — email/password, OAuth social providers, phone, MFA, and passkeys (GA on both in 2025).

**Supabase**:

- Uses **Postgres Row Level Security (RLS)** — access rules live in SQL and apply identically whether the client is a browser, a mobile app, or a backend service
- Service role key for trusted server-side code; anon key for public access
- Ideal for teams with SQL expertise that want one authorization model across the stack

**Firebase**:

- Firestore / Realtime DB use **Firebase Security Rules** (JavaScript-like DSL)
- Data Connect uses **IAM + per-query authorization** defined in the GraphQL schema
- Cloud Functions can act as gatekeepers for write paths
- Ideal for front-end-heavy teams already in the Google ecosystem

The philosophical difference: Supabase enforces auth inside the database engine; Firebase enforces it per service. Both work — the question is whether you want one policy language or several.

## Real-time and Offline

**Firebase** remains the gold standard for mobile real-time:

- Firestore and Realtime Database sync automatically across clients
- Mature offline persistence — writes queue locally and replay on reconnect
- Automatic conflict resolution, retry, and exponential backoff

**Supabase Realtime (v2)** leverages Postgres logical replication + Phoenix Channels:

- Subscribe to INSERT / UPDATE / DELETE events at the table or row level
- Presence tracking for multiplayer apps
- Broadcast channel for ephemeral messages
- Offline support in client SDKs is improving but still less mature than Firestore

If offline-first mobile is the main use case, Firebase still wins. For collaborative web apps and server-driven change streams, Supabase is a strong fit.

## Serverless Functions

**Supabase Edge Functions** (Deno / TypeScript):

- Run at the edge for low latency
- Direct access to the Supabase Postgres database via the service role key
- **Background tasks** (long-running jobs) and **WebSocket** support added in 2025
- Lightweight and database-aware; best for API endpoints and custom auth hooks

**Firebase Cloud Functions & App Hosting**:

- **Cloud Functions** — Node.js, Python, Go, Java, .NET runtimes triggered by HTTP, Firestore, Auth events, Pub/Sub, and more
- **App Hosting** (GA) — first-class hosting for Next.js and Angular apps with server-side rendering, replacing the legacy Firebase Hosting for full-stack JS apps
- Tight GCP integration; good for teams that need broad runtime support

Supabase Edge Functions are lean and TypeScript-native. Firebase gives you more runtime options and a hosted Next.js/Angular platform.

## AI and Machine Learning

The AI story moved significantly in 2025–2026.

**Firebase**:

- **Firebase AI Logic** — SDK for calling Gemini / Imagen directly from client apps without a custom backend
- **Genkit** (GA) — open-source framework for building generative AI features in TypeScript, Go
- **Firebase Studio** — cloud IDE with built-in Gemini assistance (replaces Project IDX)
- **Vertex AI** integration for production ML workloads
- **Firestore vector search** — native similarity search for embeddings
- **Crashlytics AI Insights** — Gemini-generated crash analysis

**Supabase**:

- **pgvector** — the Postgres extension for vector similarity search, maturing with HNSW indexes
- **MCP server** — official Model Context Protocol integration so AI agents (Claude, Cursor, etc.) can query Supabase safely
- **pg_mooncake** — Iceberg tables inside Postgres for analytics / RAG datasets at scale
- **AI assistant** in the Supabase dashboard — generate SQL and RLS policies from natural language
- Integrations with OpenAI, Hugging Face, LangChain, LlamaIndex

**Firebase** wins on end-to-end developer convenience and first-party Gemini access. **Supabase** wins if you want your AI data layer to be Postgres — one database for transactional, semantic, and analytical workloads.

## Local Development and Preview Environments

This is where the 2025–2026 refresh changed the most.

**Supabase**:

- `supabase start` spins up the entire stack (Postgres + Auth + Storage + Edge Functions + Studio UI) locally via Docker
- **Supabase Branching** — every Git branch / PR can create an isolated database + API preview, matching Vercel-style preview environments
- Seeds and migrations travel with the repo; PRs can run schema changes against the branch before merging

**Firebase**:

- **Firebase Emulator Suite** — local emulators for Firestore, Realtime DB, Auth, Functions, Storage, Hosting
- **Hosting preview channels** — short-lived URLs per PR for the hosted frontend
- No native database preview-per-PR for Firestore; Data Connect's PostgreSQL backend can be point-in-time cloned via Cloud SQL

For teams that care about "open this PR, get a fresh database to test against," Supabase Branching is a clear advantage. For emulating the full Firebase service surface locally, the Emulator Suite is mature.

## Self-Hosting

**Supabase** is open-source (Apache 2.0 for most components) and self-hostable. The reference self-hosted stack runs on Docker Compose; production deployments typically use Kubernetes or managed Postgres underneath. Critically, self-hosting means **no data leaves your infrastructure** — relevant for compliance (SOC 2, HIPAA, GDPR data residency).

**Firebase** is closed-source and Google-hosted. There's no self-hosting path. If you need on-prem or air-gapped, Firebase isn't an option.

If you ever need to migrate off Supabase, see [How to Migrate from Supabase to AWS](/blog/how-to-migrate-from-supabase-to-aws).

## Pricing

| | **Supabase** | **Firebase** |
| ------------------ | -------------------------------------------------------------------------- | --------------------------------------------------------------------------- |
| **Free tier** | Free plan — 500MB database, 1GB storage, 50K MAUs, 2 active projects | Spark plan — free daily / monthly quotas per service (no billing attached) |
| **Paid entry** | Pro — $25/month/project (8GB DB, 100GB bandwidth, daily backups, PITR) | Blaze — pay-as-you-go per operation (reads, writes, function invocations) |
| **Team / mid** | Team — $599/month/org (SOC 2, SSO, audit log, priority support) | Blaze with higher usage; no flat mid-tier |
| **Enterprise** | Custom (on-prem / BYO cloud, custom SLAs) | Custom (via Google Cloud sales) |
| **Cost shape** | Predictable flat per-project | Scales linearly with usage; cheap small, can surprise at scale |
| **Analogy** | Fixed-price buffet | À la carte menu |

Small apps are often cheaper on Firebase's Spark + Blaze combination. At scale, Supabase's flat pricing becomes easier to forecast — Firestore read/write charges compound quickly with real-time listeners and fan-out workloads.

## Performance and Reliability

**Firebase**:

- Google's global infrastructure; multi-region replication available on most services
- Automatic horizontal scaling with minimal configuration
- Optimized for global mobile real-time sync

**Supabase**:

- Hosted on AWS in multiple regions
- **Physical read replicas** (GA) for read scaling
- Postgres connection pooling via Supavisor
- Requires some tuning for high-concurrency workloads; pays off with Postgres-native query performance

Firebase is "set and forget" at global scale. Supabase gives you more knobs — connection pooling, indexes, query plans, replica routing — which is an advantage if you have the SQL expertise and a disadvantage if you don't.

## Database Change Management

Schema evolution is where BaaS platforms historically under-invest.

- **Supabase** migrations live in SQL files in the Git repo and are applied via the Supabase CLI (`supabase db push`). Branching makes it natural to review schema changes per PR
- **Firebase Data Connect** schemas live in `.gql` files alongside the app and are deployed via the Firebase CLI
- **Firestore** has no schema; you enforce shape in Security Rules or a Data Connect layer

For teams that want review, approval, rollback, and audit on database changes across environments — regardless of platform — [Bytebase](https://www.bytebase.com/) adds a review-and-approval workflow on top of Supabase (and any other Postgres host).

## When to Choose Supabase

- You want **Postgres as the system of record** with full SQL access
- You need **self-hosting** or data residency control
- You want **per-PR database previews** via Branching
- Your AI stack benefits from pgvector + Postgres in one place
- You prefer flat, predictable pricing over usage-based billing

## When to Choose Firebase

- You're building a **mobile-first app** with real-time sync and offline needs
- You're already in the **Google ecosystem** (Gemini, GCP, Google Analytics)
- You want **rapid prototyping** with minimal infra decisions
- You need **multiple runtimes** for serverless (Python, Go, Java, .NET)
- You want **Firebase AI Logic** for client-side Gemini calls without custom backend

## Conclusion

In 2026 the comparison is narrower than it used to be. Firebase Data Connect gave Firebase a Postgres story; Supabase shipped Branching, read replicas, and MCP. Pick Supabase if you want one open Postgres to power everything, including AI. Pick Firebase if you want Google-grade managed services and a mobile-first real-time experience.

## FAQ

### Is Firebase Data Connect a real alternative to Supabase?

Yes, for the Postgres-and-GraphQL use case. Firebase Data Connect runs on Cloud SQL for PostgreSQL and exposes a GraphQL-like query layer. Where it differs: you don't get direct SQL access in the same way, schema modeling is done in `.gql` files, and self-hosting isn't an option. If you want open-source Postgres with SQL, Supabase is still the better fit; if you want Google-managed Postgres inside the Firebase console, Data Connect is viable.

### Can I use Supabase and Firebase together?

Possible but uncommon. Teams sometimes use Firebase Auth + Cloud Messaging for mobile identity/push while keeping data in Supabase Postgres — the SDKs don't fight each other. More often, teams pick one platform and commit.

### Which is cheaper?

For small apps with low traffic, Firebase's Spark (free) + Blaze (pay-per-op) is often cheaper because you pay nothing until you grow. Supabase's $25 Pro entry is a fixed cost. At scale (millions of real-time listeners, heavy fan-out), Supabase's flat pricing usually wins because Firestore per-operation costs compound. Run projected usage through each pricing calculator before deciding.

### Does Supabase support mobile offline like Firebase?

Partially. Supabase client SDKs have basic offline handling but Firebase's Firestore offline persistence is the mature standard. For offline-first mobile apps, Firestore or Realtime Database still has the edge.

### Can I self-host Firebase?

No. Firebase is closed-source and Google-hosted. If self-hosting is a hard requirement (air-gapped, on-prem, strict data residency), Firebase is off the table — consider Supabase or a different stack entirely.

### How do I handle schema migrations on Supabase and Firebase?

- **Supabase**: SQL migration files in Git, applied with `supabase db push`. Branching creates per-PR preview databases to test migrations before merge
- **Firebase Data Connect**: `.gql` schema files, deployed with `firebase deploy --only dataconnect`
- **Firestore**: no schema, but you can enforce structure in Security Rules or add a Data Connect layer

For cross-environment review, approval, and audit on any Postgres (including Supabase), see [What is Database Change Management](/blog/what-is-database-change-management).

### What about Neon or PlanetScale as alternatives?

Neon is a serverless Postgres platform with branching similar to Supabase's but without the BaaS layer (no built-in auth / storage / edge functions). PlanetScale is MySQL-based, serverless, with strong branching but no Postgres. See [Neon vs. Supabase](/blog/neon-vs-supabase) for a full comparison.

---

Related reading:

- [Neon vs. Supabase](/blog/neon-vs-supabase)
- [Convex vs. Supabase](/blog/convex-vs-supabase)
- [Supabase vs. AWS Database Pricing](/blog/supabase-vs-aws-database-pricing)
- [How to Migrate from Supabase to AWS](/blog/how-to-migrate-from-supabase-to-aws)
- [Supabase vs. MongoDB](/blog/supabase-vs-mongodb)
- [What is Database Change Management](/blog/what-is-database-change-management)

<HintBlock type="info">

This post is maintained by Bytebase, an open-source database DevSecOps tool that can manage both Snowflake and Redshift. We update the post every year.

</HintBlock>

| Update History | Comment |
| -------------- | ---------------- |
| 2025/04/18 | Initial version. |

## Why Comparing Snowflake and Amazon Redshift

When comparing Snowflake and Amazon Redshift, we're examining two cloud-native data warehouse solutions designed for large-scale analytics and business intelligence workloads. Both platforms offer high-performance query capabilities, scalability, and integration with modern data ecosystems.

**Snowflake** represents a cloud-agnostic approach with its unique separation of storage and compute resources, while **Amazon Redshift** is deeply integrated with the AWS ecosystem, offering tight connections to other AWS services.

This comparison reflects the current state of both systems as of 2025, including the latest features and capabilities:

- [Feature Comparison](#feature-comparison)
- [Technical Specifications](#technical-specifications)
- [Development Workflow](#development-workflow)
- [Pricing](#pricing)
- [Conclusion](#conclusion)

## Feature Comparison

### Core Database Features

| Feature | Snowflake | Amazon Redshift |
| --------------------- | ---------------------------------------------------------------------------------- | --------------------------------------------------------------------- |
| **Data Types** | Comprehensive set including structured, semi-structured (JSON, XML, Parquet, Avro) | Standard SQL data types, structured data, limited semi-structured support |
| **Indexing** | Automatic clustering, no manual index management required | Automatic table sort and distribution keys, zone maps |
| **Transactions** | ACID-compliant with automatic concurrency control | ACID-compliant with serializable isolation |
| **Stored Procedures** | JavaScript, SQL, Java, Python, Scala | SQL, Python, stored procedures with transaction support |
| **Views** | Regular, Materialized, Secure | Regular, Late Binding, Materialized |
| **Triggers** | Limited support through tasks and streams | Limited support, primarily through Lambda integration |
| **Partitioning** | Automatic micro-partitioning, clustering keys | Distribution keys, sort keys |
| **Constraints** | Primary key, Foreign key, Unique, Not Null (not enforced) | Primary key, Foreign key, Unique (enforced) |

### Advanced Features

| Feature | Snowflake | Amazon Redshift |
| ---------------------- | ----------------------------------------------------------------------------- | ------------------------------------------------------------------------------ |
| **High Availability** | Built-in redundancy, automatic failover, cross-region replication | Multi-AZ deployments, automatic backups, cross-region snapshots |
| **Scalability** | Independent scaling of compute and storage, instant scaling | Elastic resize, concurrency scaling, RA3 instances with managed storage |
| **Security** | Role-based access control, column-level security, row-level security, encryption | IAM integration, VPC, encryption, column-level access control, dynamic data masking |
| **Cloud Integration** | Multi-cloud (AWS, Azure, GCP), cloud-agnostic | Deep AWS ecosystem integration |
| **AI/ML Capabilities** | Snowpark for ML, vector search, Cortex AI integration | Amazon Redshift ML, integration with SageMaker, vector search capabilities |
| **Extensibility** | External functions, UDFs, stored procedures, Snowpark | UDFs, stored procedures, Lambda integration, Apache Spark integration |

### Snowflake-Specific Features

- **Multi-cloud** support (AWS, Azure, GCP)
- **Zero-copy cloning** for instant data duplication
- **Time Travel** to access historical data
- **Secure data sharing** without data movement
- **Snowpark** for multi-language data processing
- **Fully automated optimization** (no vacuuming or tuning)
- **Unlimited concurrency** with isolated warehouses
- **SnowGrid** for global, cross-cloud connectivity

### Amazon Redshift-Specific Features

- **Tight AWS integration** (S3, Glue, EMR, SageMaker)
- **Spectrum** for querying S3 data without loading it
- **Zero-ETL** for seamless data ingestion from AWS sources
- **Amazon Q** AI-powered SQL assistant
- **Auto table optimization** and maintenance
- **Federated queries** across diverse sources
- **Serverless option** for auto-scaling compute
- **Multi-AZ deployments** for high availability

## Technical Specifications

### Architecture

**Snowflake Architecture (Cloud-native & Flexible)**

- **Three main parts:**

 1. **Storage:** Where all your data lives, stored on cloud platforms like AWS S3, Azure Blob, or Google Cloud Storage.
 1. **Compute:** These are virtual warehouses (basically computer power) that process your queries. You can add or remove them anytime.
 1. **Cloud Services:** Handles everything else — user logins, tracking metadata, optimizing your queries, etc.

- **Key Features:**

 - Data is automatically organized and optimized in small pieces called **micro-partitions**.
 - Data is stored in **columns**, which speeds up large analytics queries.
 - **Storage and compute are separated**, so you can scale them independently.
 - **Multiple compute clusters** can run at the same time on the same data — good for teams working in parallel.

**Amazon Redshift Architecture (Classic & AWS-Integrated)**

- **Two main parts:**
 1. **Leader Node:** Like a manager—it plans and coordinates your query.
 1. **Compute Nodes:** Like workers—they store data and do the actual work of running the query.

- **Storage:**
 - Uses **Redshift Managed Storage** (backed by S3) for scalable storage.
 - Data is stored in **columns** with **zone maps** to make searches faster.

- **How it works:**
 - Uses **Massively Parallel Processing (MPP)**: data is split into small chunks and processed in parallel across “slices” on the compute nodes.
 - You can optimize performance using **distribution keys** (to control where data goes) and **sort keys** (to speed up reads).
 - Designed to work closely with other **AWS services** through its internal network.

### Query Processing and Performance

**Snowflake Query Processing:**

- **How it works:**
 - **Virtual Warehouses** – Like "brain teams" that process queries (you can resize them anytime).
 - **Auto-Scaling** – Adds more power if a query is complex.
 - **Smart Caching** – Remembers results for repeated queries (no extra work needed).
 - **Self-Optimizing** – Automatically adjusts for fastest performance.

- **Why it’s easy:**
 - No manual tuning – Snowflake handles optimizations.
 - Isolated workloads – Different teams (warehouses) won’t slow each other down.

**Amazon Redshift Query Processing:**

- **How it works:**
 - **Leader Node** – The "boss" that plans and distributes work.
 - **Compute Nodes** – Workers that execute queries in parallel.
 - **Concurrency Scaling** – Adds temporary workers during busy times.
 - **AQUA (Advanced Query Accelerator)** – Special hardware for super-fast queries.

- **Why it’s powerful (but needs attention):**
 - Manual tuning helps (e.g., setting distribution keys).
 - Works best when optimized for AWS.

### Data Storage and Management

**Snowflake Data Storage (Like a Smart, Self-Organizing Warehouse)**

- **Auto-Partitioning** – Splits data into tiny, optimized chunks ("micro-partitions").
- **Columnar Storage** – Stores data vertically (like a spreadsheet) for fast queries.
- **Time Travel** – Lets you restore data from any point in time (like undo history).
- **Zero-Copy Cloning** – Instantly duplicates data without extra storage costs.
- **Handles All Data Types** – Works with tables (structured) and JSON/Parquet (semi-structured).
- **Always Encrypted** – Secures data by default.

Best for: Users who want hands-off, auto-optimized storage.

**Amazon Redshift Data Storage (Like a High-Speed Factory Floor)**

- **Redshift Managed Storage (RMS)** – Uses S3 for scalable storage behind the scenes.
- **Columnar + Compression** – Stores data efficiently for fast scans.
- **Backups & Snapshots** – Automatic backups with point-in-time recovery.
- **Distribution Styles** – Lets you control how data is spread (for performance tuning).
- **Sort Keys** – Physically orders data to speed up filtered queries.
- **Auto-Maintenance** – Runs "vacuum" and "analyze" to keep performance sharp.
- **S3 Integration** – Easily extends storage to AWS S3.

Best for: AWS-centric teams who want control over data layout.

## Development Workflow

| Feature | **Snowflake** (Flexible, Cloud-Agnostic) | **Amazon Redshift** (AWS-Native, Integrated) |
|-----------------------|----------------------------------------------------------------------|-------------------------------------------------------------------------|
| **Web Interface** | Snowsight: modern, intuitive UI | Query Editor v2: functional but less advanced |
| **Dev Tools Support** | Works with VS Code, SnowSQL CLI, Snowpark (SQL, Python, Java) | Optimized for AWS tools (Glue, CodeCommit, etc.) |
| **Schema Management** | SQL or Snowpark-based | SQL and AWS Glue for catalog integration |
| **Version Control** | No native Git; manual Git file management or partner integrations | Native with CodeCommit or any Git tool |
| **Deployments** | Tasks + third-party CI/CD (e.g., GitHub Actions) | Fully automatable with CloudFormation + CodePipeline |
| **Testing** | Custom or third-party frameworks | AWS-native DevOps tools or external testing integrations |
| **CI/CD** | Tool-agnostic; integrates with various CI/CD platforms | Deep integration with AWS CI/CD tools like CodeBuild, CodePipeline |

## Pricing

| | **Snowflake** (Pay-as-you-go, Flexible but Complex) | **Amazon Redshift** (AWS-Integrated, Discount Options) |
|-----------------------|------------------------------------------------------------------|------------------------------------------------------------------------|
| **Pricing** | 4 tiers: Standard → Enterprise → Business Critical → VPS | On-demand, Reserved Instances (1–3 years), or Serverless |
| **Compute Pricing** | Per-second billing for virtual warehouses (scales up/down) | Hourly (node-based) or Serverless (pay per query) |
| **Storage Pricing** | Monthly per TB (compressed) | Redshift Managed Storage (RMS) per GB |
| **Cloud Services** | Included in compute costs | Spectrum (query S3), Concurrency Scaling (beyond free tier incurs cost)|
| **Best For** | Bursty workloads, multi-cloud setups, flexible scaling | Steady AWS-native workloads, long-term cost optimization |


## Conclusion

When it comes to choosing between Snowflake and Amazon Redshift, Snowflake excels for multi-cloud flexibility, hands-off management, and advanced features like data sharing, while Redshift is ideal for AWS-centric environments with cost-efficient steady workloads and deep AWS integrations.

## References

1. [Snowflake Official Documentation](https://docs.snowflake.com/)
2. [Amazon Redshift Documentation](https://docs.aws.amazon.com/redshift/)
3. [Snowflake Editions and Pricing](https://www.snowflake.com/pricing/)
4. [Amazon Redshift Pricing](https://aws.amazon.com/redshift/pricing/)
5. [Snowflake Architecture Overview](https://docs.snowflake.com/en/user-guide/intro-key-concepts)
6. [Amazon Redshift Architecture](https://docs.aws.amazon.com/redshift/latest/dg/c_high_level_system_architecture.html)

<HintBlock type="info">

This post is maintained by Bytebase, an open-source database DevSecOps tool that can manage both Amazon DynamoDB and MongoDB. We update the post every year.

</HintBlock>

| Update History | Comment |
| -------------- | ---------------- |
| 2025/04/17 | Initial version. |

NoSQL databases like Amazon DynamoDB and MongoDB overcome the limitations of traditional relational databases in handling scale, flexibility, and performance. While both are document-oriented, they differ in architecture, deployment, and other aspects.

## Amazon DynamoDB Overview

DynamoDB, launched by AWS in 2012, evolved from [Amazon's 2007 Dynamo paper](https://dl.acm.org/doi/10.1145/1323293.1294281). DynamoDB is a fully managed, serverless, key-value and document database that delivers single-digit millisecond performance at any scale. As an AWS service, it eliminates the operational burden of managing database infrastructure, allowing developers to focus on application development.

## MongoDB Overview

MongoDB, created in 2007 by 10gen (now MongoDB Inc.), emerged from the founders' struggles with relational databases. Released in 2009 as open-source, it became a leading NoSQL database for flexible, scalable applications.

MongoDB is a document-oriented NoSQL database that stores data in flexible, JSON-like documents. It offers a rich query language, high availability through replica sets, and horizontal scalability through sharding.

## Comparison Table

| Feature | Amazon DynamoDB | MongoDB |
| ------------------------- | -------------------------------------------------------------------------- | --------------------------------------------------------------------------------- |
| **Type** | Fully managed NoSQL database service | Document-oriented NoSQL database |
| **Deployment** | AWS cloud only | Self-hosted, MongoDB Atlas (cloud), or other cloud providers |
| **Data Model** | Key-value and document | Document-oriented |
| **Schema** | Schema-flexible with required primary key | Fully schema-flexible |
| **Document Size Limit** | 400KB per item | 16MB per document |
| **Query Language** | Limited query expressions with PartiQL support | Rich query language with MongoDB Query API |
| **Indexing** | Primary key, Local Secondary Indexes (LSI), Global Secondary Indexes (GSI) | Multiple index types (single field, compound, multikey, geospatial, text, hashed) |
| **Transactions** | ACID transactions across multiple items | ACID transactions across multiple documents |
| **Consistency** | Configurable: eventual or strong consistency | Configurable consistency levels |
| **Scaling** | Automatic, unlimited horizontal scaling | Manual or automated sharding |
| **Global Distribution** | Global Tables for multi-region replication | Global Clusters in Atlas |
| **Pricing Model** | Pay-per-request or provisioned capacity | Instance-based pricing in Atlas, free Community Edition |
| **Backup & Recovery** | Continuous backups with point-in-time recovery | Continuous backups in Atlas, manual in Community Edition |
| **Security** | AWS IAM integration, encryption at rest and in transit | Role-based access control, field-level encryption, TLS/SSL |
| **Performance** | Single-digit millisecond response times | Performance varies based on configuration |
| **Monitoring** | CloudWatch integration | MongoDB Atlas monitoring, self-monitoring tools |
| **Use Cases** | High-scale applications with simple access patterns | Applications requiring complex queries and flexible schema |
| **Ecosystem Integration** | Deep AWS service integration | Broad language support and third-party integrations |
| **Vendor Lock-in** | High (AWS-specific) | Moderate (portable between environments) |

## Key Comparison Points

### Data Model and Schema Flexibility

**DynamoDB**:

- Supports key-value and document models
- Requires defining primary key structure at table creation
- Flexible schema for non-key attributes
- 400KB maximum item size

 ```json
 {
 "product_id": "P100",
 "name": "Wireless Headphones",
 "price": 99.99,
 "attributes": {
 "color": "black",
 "weight": "250g"
 }
 // Cannot efficiently store embedded reviews (400KB limit)
 }
 ```

**MongoDB**:

- Document-oriented
- Fully flexible schema
- Supports nested documents and arrays
- 16MB maximum document size

 ```json
 {
 "_id": "P100",
 "name": "Wireless Headphones",
 "price": 99.99,
 "attributes": {
 "color": "black",
 "weight": "250g"
 },
 "reviews": [
 // Nested array
 { "user": "Alice", "rating": 5 },
 { "user": "Bob", "rating": 4 }
 ]
 // 16MB max allows complex hierarchies
 }
 ```

**Analysis**: MongoDB offers greater schema flexibility and document complexity, making it better suited for applications with evolving data structures or complex hierarchical data. DynamoDB's more rigid primary key requirements provide performance benefits but less flexibility. BSON is a binary serialization format that is more efficient for MongoDB.

### Deployment and Management

**DynamoDB**:

- Fully managed AWS service
- Serverless architecture
- No infrastructure to manage
- AWS-only deployment

**MongoDB**:

- Self-hosted option (MongoDB Community Server)
- Managed service option (MongoDB Atlas)
- Available on multiple cloud providers
- Requires more operational expertise for self-hosted deployments

**Analysis**: DynamoDB eliminates operational overhead but ties you to AWS. MongoDB offers more deployment flexibility but may require more management effort, especially for self-hosted deployments.

### Scalability and Performance

**DynamoDB**:

- **Auto-scaling:** Handles traffic spikes automatically
- **Predictable speed:** Always fast (single-digit ms responses)
- **Zero maintenance:** No partitions or shards to manage
- **Pay options:** Choose fixed capacity or pay-per-use

**MongoDB**:

- **Flexible scaling:** Can grow vertically or horizontally
- **Performance tuning:** Depends on your setup (indexes, shards)
- **More control:** You decide how to split data (shard key strategy)
- **Manual effort:** Needs planning for best performance

**Analysis**: DynamoDB is better for apps needing instant and maintenance-free scaling, while MongoDB is better for complex systems where you want fine-grained control.

### Query Capabilities

**DynamoDB**:

- Optimized for **fast key-value lookups** (primary key access)
- Basic filtering (limited to indexed attributes)
- Supports PartiQL for simple SQL-like queries
- Minimal native aggregation support

**MongoDB**:

- **Expressive query language** with support for complex conditions
- **Full aggregation pipeline**(grouping, joins, transformations)
- Built-in **text search** and **geospatial queries**

**Analysis**: MongoDB’s rich querying features make it ideal for applications needing advanced search, analytics, or hierarchical data. DynamoDB excels in predictable, high-speed key access but lacks flexibility for complex operations.

### Consistency and Transactions

**DynamoDB**:

- **Consistency Options:** Choose between eventual (faster) or strong (most up-to-date) reads.

- **Transactions:** Guarantees ACID compliance for operations across multiple items (e.g., updating inventory and orders together).

- **AWS Integration:** Works seamlessly with Lambda, EventBridge, and other AWS services.

**MongoDB**:

- **Read Control:** Adjustable read isolation levels for different use cases.

- **Transactions:** Supports ACID across multiple documents (e.g., transferring money between accounts).

- **Real-Time Updates:** Change streams allow event-driven applications (e.g., notifications on data changes).

**Analysis**: DynamoDB is best for AWS-based apps needing fast, simple transactions. MongoDB is better for complex document workflows requiring fine-tuned control.

### Global Distribution

**DynamoDB**:

- Global Tables for multi-region replication
- Active-active setup (read/write in any region)
- Conflict resolution handled by AWS

**MongoDB Atlas**:

- Deploy across multiple cloud providers (AWS, Azure, GCP)
- Zone sharding for data locality (keep data near users)
- Tunable replication for performance vs. cost

**Analysis**: DynamoDB's Global Tables offer plug-and-play multi-region scaling on AWS. MongoDB Atlas supports cross-cloud deployments with granular geo-control (GDPR, etc.).

### Pricing Model

**DynamoDB - Pay What You Use**:

- **Compute:** Pay per request (on-demand) or reserve capacity
- **Storage:** $0.25/GB/month
- **Bonus:** Scales to $0 when idle (on-demand mode)
- **Watch Out:** Data transfer/global replication fees

**MongoDB Atlas - Predictable Pricing**:

- **Compute:** Fixed-rate vCPU/RAM clusters
- **Storage:** 0.08–0.30/GB/month (depends on tier)
- **Always On:** Minimum costs apply 24/7
- **Extras:** Backup storage, cross-cloud replication

**Analysis**: DynamoDB wins for unpredictable workloads (viral apps, event-driven systems); MongoDB better for steady traffic(SaaS apps, analytics databases).

### Security Features

**DynamoDB**:

- **Access Control:** AWS IAM for fine-grained permissions
- **Network Security:** VPC endpoints for private access
- **Encryption:** Always encrypted (at rest/in transit)
- **Audit Logs:** Integrated with AWS CloudTrail

**MongoDB**:

- **Access Control:** Role-based (user/database-level)
- **Data Protection:** Field-level encryption
- **Network Security:** TLS/SSL for all connections
- **Audit Logs:** Built-in database auditing

**Analysis**: Both databases offer comprehensive security features, with DynamoDB leveraging AWS's security ecosystem and MongoDB providing more database-specific security controls.

## Use Case Suitability

### When to Choose DynamoDB

DynamoDB is particularly well-suited for:

1. **High-Scale Applications**: Applications requiring unlimited scale with consistent performance.
2. **Serverless Architectures**: Applications built on AWS Lambda and other serverless technologies.
3. **Simple Access Patterns**: Applications with straightforward, key-based data access patterns.
4. **AWS-Integrated Applications**: Applications heavily integrated with other AWS services.
5. **IoT and Time-Series Data**: Applications collecting and analyzing large volumes of time-series data.
6. **Variable Workloads**: Applications with unpredictable or highly variable traffic patterns.

**Real-World Examples**:

- [Lyft's ride tracking system](https://aws.amazon.com/solutions/case-studies/lyft/)
- [Snapchat's messaging infrastructure](https://aws.amazon.com/solutions/case-studies/snap-dynamodb/)

### When to Choose MongoDB

MongoDB is particularly well-suited for:

1. **Complex Data Structures**: Applications with complex, hierarchical data models.
2. **Evolving Schemas**: Applications where data structure changes frequently.
3. **Complex Queries**: Applications requiring rich query capabilities and aggregations.
4. **Content Management**: Systems managing varied content types with different attributes.
5. **Multi-Cloud or Hybrid Deployments**: Applications requiring database portability.
6. **Prototyping and Rapid Development**: Projects needing quick iteration and flexibility.

**Real-World Examples**:

- [Forbes' content management system](https://www.mongodb.com/blog/post/forbes-cloud-migration-helps-worlds-biggest-media-brand-continue-standard-digital-innovation)
- [SEGA's game analytics platform](https://www.mongodb.com/solutions/customer-case-studies/sega)

## Conclusion

DynamoDB excels in AWS environments requiring predictable performance with minimal management overhead and automatic scaling, making it ideal for serverless architectures and applications with straightforward access patterns. MongoDB offers greater flexibility with its rich query capabilities, deployment options, and document model, better serving complex data relationships and queries while avoiding vendor lock-in.

## FAQ

### Is DynamoDB better than MongoDB?

DynamoDB is better for applications that need automatic scaling, single-digit millisecond performance, and tight AWS integration with minimal operational overhead. MongoDB is better for applications requiring complex queries, flexible schemas, aggregation pipelines, or multi-cloud deployments.

### Is DynamoDB faster than MongoDB?

DynamoDB provides consistent single-digit millisecond response times at any scale due to its fully managed, distributed architecture. MongoDB performance varies based on your configuration, indexing strategy, and hardware. For simple key-value lookups, DynamoDB is generally faster; for complex queries and aggregations, MongoDB is more capable.

### Can MongoDB replace DynamoDB?

MongoDB can replace DynamoDB for many use cases, especially when you need complex queries, aggregation pipelines, or multi-cloud portability. However, MongoDB cannot match DynamoDB's automatic, unlimited horizontal scaling or its tight integration with AWS Lambda and other AWS services.

### Is DynamoDB cheaper than MongoDB?

DynamoDB uses pay-per-request pricing ($0.25/GB storage) that scales to zero when idle, making it cost-effective for variable workloads. MongoDB Atlas uses fixed-rate cluster pricing that applies 24/7. For unpredictable or bursty traffic, DynamoDB is often cheaper; for steady, predictable workloads, MongoDB Atlas can be more cost-efficient.

### Does DynamoDB support SQL?

DynamoDB supports PartiQL, which provides SQL-compatible query syntax for simple queries. However, it does not support full SQL capabilities like JOINs, subqueries, or complex aggregations. MongoDB also does not use SQL but has its own rich query language (MongoDB Query API) with full aggregation pipeline support.


This is the series of articles about platform engineering:

1. Platform Engineering vs. DevOps (this one)
1. [Platform Engineering Tools: Essential Guide](/blog/platform-engineering-tools-essential-guide/)
1. [Platform Engineering Tools: Advanced Guide](/blog/platform-engineering-tools-advanced-guide/)

## Introduction

As technology evolves rapidly, organizations are constantly looking to streamline software development and delivery. Two popular approaches — **DevOps and Platform Engineering** — offer solutions to modern software challenges, but differ in focus.

DevOps emerged as a cultural and technical movement to **bridge the gap between development and operations**. By promoting collaboration, automation, and shared responsibility, DevOps helped teams deliver software faster and more reliably.

However, as systems grew more complex, **scaling DevOps across large organizations became difficult**. Developers often found themselves bogged down with operational tasks, diverting focus from core development. Platform Engineering has since emerged as a response to these challenges. It builds on DevOps principles by **creating internal platforms that offer standardized tools, self-service capabilities, and abstractions that reduce complexity for developers**, allowing them to focus on building products.

This article compares DevOps and Platform Engineering across several dimensions — helping teams decide which approach, or combination, best fits their needs.

## A Restaurant Analogy

Let's use restaurant analogy that illustrates their relationship and approaches.

![restaurant](/content/blog/platform-engineering-vs-devops/restaurant-analogy.webp)

**DevOps is like a farm-to-table restaurant.** Each development team runs the entire operation—growing ingredients, cooking, serving, and cleaning. This gives full control and flexibility, ideal for small teams. But as the restaurant scales, chefs (developers) spend too much time on non-cooking tasks, training becomes complex, and consistency suffers.

**Platform Engineering is like a restaurant group with shared services.** A central team handles sourcing, equipment, systems, and training, so chefs can focus on creating dishes. This setup enables faster scaling, consistent quality, and easier onboarding, while still allowing each restaurant (team) to keep its unique flavor.

## At-a-Glance Comparison

| Aspect                   | DevOps                                                                                          | Platform Engineering                                                                                             |
| ------------------------ | ----------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------- |
| **Core Philosophy**      | "You build it, you run it"                                                                      | "We build it, you run your code on it"                                                                           |
| **Team Structure**       | Cross-functional teams with both dev and ops skills                                             | Dedicated platform team serving multiple development teams                                                       |
| **Responsibility Model** | Distributed responsibility across teams                                                         | Centralized platform responsibility, distributed application responsibility                                      |
| **Developer Experience** | Higher cognitive load - developers need to understand operations                                | Lower cognitive load - platform abstracts operational complexity                                                 |
| **Standardization**      | Can vary between teams                                                                          | High through centralized platform                                                                                |
| **Scalability**          | Challenging to scale consistently across large organizations                                    | Designed specifically to scale practices across large organizations                                              |
| **Skill Requirements**   | Broad skill sets across development and operations                                              | More specialized skills with platform team focusing on infrastructure                                            |
| **Primary Challenge**    | Cultural resistance and skill gaps                                                              | Building a platform that meets diverse needs                                                                     |
| **Suitable Use Cases**   | Startups, small teams, highly skilled engineers, homogeneous tech stacks, rapid experimentation | Large enterprises, regulated industries, complex environments, high-growth companies, microservice architectures |

## Key Comparison Dimensions

### Philosophical Foundations

**DevOps** is a cultural and organizational philosophy that emphasizes collaboration, shared responsibility, and breaking down silos between development and operations. Its mantra of **"you build it, you run it"** places the responsibility for both development and operations on the same team.

**Platform Engineering** rather than expecting every developer to become proficient in operational concerns, it creates **a dedicated team responsible for building and maintaining an internal platform** that makes operational tasks accessible through self-service interfaces.

### Organizational Structure and Team Dynamics

**DevOps** each development team manages their own operations, including deployment and infrastructure.

**Platform Engineering** centralizes these responsibilities in a dedicated team, allowing developers to focus on writing code while the platform team handles infrastructure and tools.

### Developer Experience and Cognitive Load

In a **DevOps** environment, developers are expected to understand and manage operational concerns alongside their development work. This can lead to **a high cognitive load**, particularly in complex environments with multiple technologies and services.

**Platform Engineering** explicitly aims to reduce this cognitive load by providing **abstraction layers** that shield developers from unnecessary complexity. Through self-service interfaces and standardized workflows, developers can deploy and manage applications without needing to understand all the underlying infrastructure details.

### Standardization vs. Flexibility

**DevOps** implementations often vary significantly across teams within the same organization, as each team adopts tools and practices that suit their specific needs. This flexibility can foster innovation but may lead to inconsistencies.

**Platform Engineering** introduces more standardization through the internal developer platform, which provides opinionated solutions for common tasks. This standardization can improve efficiency, security, and governance, but risks constraining teams if not balanced with appropriate flexibility.

### Scaling Across the Organization

While **DevOps** principles can work well for individual teams or small organizations, they can become challenging to implement consistently as the organization grows.

**Platform Engineering** provides a more structured approach to scaling DevOps practices. By centralizing platform development and maintenance, organizations can ensure consistent implementation of best practices across teams.

### Suitable Use Cases

**DevOps Excels In:**

- **Startups and Small Teams**: Where agility and flexibility are paramount, and the overhead of building a dedicated platform may not be justified.
- **Highly Skilled Engineering Teams**: Organizations with teams of highly skilled, full-stack engineers who have both the aptitude and interest to handle operational concerns.
- **Homogeneous Technology Stacks**: When an organization uses a relatively consistent technology stack across teams, reducing the complexity of operations.
- **Rapid Experimentation Environments**: Where teams need maximum flexibility to try new approaches and technologies without standardization constraints.
- **Early Product Development**: When products are still evolving rapidly and standardization might be premature.

**Platform Engineering Shines In:**

- **Large Enterprises with Diverse Teams**: Organizations with hundreds or thousands of developers with varying skill levels benefit from standardized platforms.
- **Regulated Industries**: Healthcare, finance, and government organizations where compliance controls and security practices must be consistently applied.
- **Complex, Heterogeneous Environments**: Organizations using multiple cloud providers, on-premises infrastructure, and diverse technologies.
- **High-Growth Companies**: Organizations scaling rapidly that need to onboard many new developers quickly.
- **Microservice Architectures**: When managing hundreds of services across multiple teams, centralized platform capabilities become essential.

## Conclusion

DevOps and Platform Engineering aren’t competing approaches but complementary, with many organizations blending both for cultural and structural benefits. Success lies in focusing on outcomes — speed, reliability, developer experience — rather than rigid methodologies.

Technology leaders should consider their specific organizational context — including size, industry, and regulatory requirements — when finding the right balance between these approaches, as there's no universal solution.


<HintBlock type="info">

This post is maintained by Bytebase, an open-source database DevSecOps tool that can manage both CockroachDB and PostgreSQL. We update the post every year.

</HintBlock>

| Update History | Comment |
| -------------- | ---------------- |
| 2025/04/15 | Initial version. |

## CockroachDB Overview

CockroachDB is a distributed SQL database designed to create a scalable, consistent, and survivable database. Developed by Cockroach Labs, it aims to combine the familiarity of SQL with the horizontal scalability of NoSQL databases.

### License Model

CockroachDB has recently [moved to a fully proprietary licensing model](https://www.cockroachlabs.com/enterprise-license-update/) under the CockroachDB Software License. The licensing options include:

- **Enterprise**: A paid license that provides access to all features, requiring annual renewal
- **Enterprise Free**: Available for businesses with less than $10M in annual revenue
- **Enterprise Trial**: A 30-day self-service trial license

All CockroachDB Cloud deployments automatically include an Enterprise license. For developers, there's a 7-day grace period for clusters without a license key, and single-node clusters don't require a license key at all.

### Architecture and Design Philosophy

CockroachDB's architecture is built around a layered approach:

![cockroachdb-arch](/content/blog/tidb-vs-cockroachdb/cockroach-arch.webp)

1. **SQL Layer**: Translates client SQL queries to key-value operations
2. **Transactional Layer**: Enables atomic changes to multiple key-value entries
3. **Distribution Layer**: Presents replicated key-value ranges as a single entity
4. **Replication Layer**: Consistently replicates key-value ranges across nodes
5. **Storage Layer**: Reads and writes key-value data on disk

This design allows CockroachDB to distribute data automatically across nodes in chunks called "ranges," with each range replicated to at least three nodes by default for survivability.

### Performance Characteristics

CockroachDB delivers predictable throughput and latency at scale on commodity hardware. It can process [single-row reads in 1ms and single-row writes in 2ms within a single availability zone](https://www.cockroachlabs.com/docs/stable/frequently-asked-questions#when-is-cockroachdb-a-good-choice). In [benchmark tests using TPC-C](https://www.cockroachlabs.com/docs/stable/performance), CockroachDB v21.1 processed 1.68M tpmC with 140,000 warehouses at 95% efficiency.

A key feature of CockroachDB is that it achieves this performance while maintaining SERIALIZABLE isolation, the strongest isolation level in the SQL standard.

### Scalability Features

CockroachDB excels in scalability with:

- Native, automatic horizontal scaling
- No theoretical scaling limit (demonstrated linear scaling up to 256+ nodes)
- Multi-active availability where all nodes can accept reads and writes
- Automatic data distribution and rebalancing
- Built-in support for multi-region deployments
- Consensus-based replication using the Raft protocol

### SQL Compatibility

CockroachDB is wire-compatible with PostgreSQL and supports most PostgreSQL data types and SQL features. However, it has some limitations compared to vanilla PostgreSQL:

- PostgreSQL compatibility [maintained by CockroachDB](https://www.cockroachlabs.com/docs/stable/postgresql-compatibility)
- [Third-party PostgreSQL Compatibility Index](https://github.com/secp256k1-sha256/postgres-compatibility-index), CockroachDB only has achieved a score of 40.21%.

### Deployment Options

CockroachDB offers several deployment options:

- **CockroachDB Cloud**:
 - **Standard**: A fully managed, multi-tenant deployment in a single region (AWS or GCP)
 - **Advanced**: A fully managed, single-tenant deployment with enhanced features
- **Self-Hosted**: Requires an Enterprise license for production use

## CockroachDB vs. PostgreSQL

### License and Cost

The most significant difference is in licensing: PostgreSQL is truly open-source and free forever, while CockroachDB has moved to a proprietary license model with various paid tiers. This has major implications for production deployments and total cost of ownership.

### Architecture and Scalability

CockroachDB was built from the ground up as a distributed SQL database, offering native horizontal scaling and global distribution capabilities. PostgreSQL, as a traditional RDBMS, excels in single-node performance but requires additional configuration and tools to achieve similar distributed capabilities.

### Performance Characteristics

Both databases offer strong consistency and ACID compliance. CockroachDB achieves this in a distributed environment using the Raft consensus protocol, while PostgreSQL uses MVCC within a single-node architecture. CockroachDB's performance is optimized for distributed scenarios, while PostgreSQL often has better performance for complex queries on a single node.

### SQL Compatibility and Ecosystem

PostgreSQL has a more mature and feature-rich SQL implementation with a wider range of extensions and procedural languages. CockroachDB, while PostgreSQL-compatible, has a more limited set of extensions and features, focusing instead on distributed capabilities.

## Comparison Table

| Aspect | CockroachDB | PostgreSQL |
| ------------------------ | -------------------------------------------- | -------------------------------------- |
| **License** | Proprietary (CockroachDB Software License) | Open Source (PostgreSQL License) |
| **Architecture** | Distributed SQL database | Traditional monolithic RDBMS |
| **Horizontal Scaling** | Native, automatic | Manual (partitioning, read replicas) |
| **Multi-Region Support** | Built-in | Possible but requires additional tools |
| **High Availability** | Multi-active (all nodes accept reads/writes) | Primary-replica architecture |
| **SQL Compatibility** | PostgreSQL-compatible | Native PostgreSQL |
| **Extensions** | Limited | Rich ecosystem |
| **Cost** | Paid license for production use | Free for all uses |
| **Cloud Options** | CockroachDB Cloud | Multiple providers |
| **Use Cases** | Distributed, global applications | Wide range of applications |

If you're considering a distributed database solution like CockroachDB, it's certainly a good problem to have—it indicates your business has achieved meaningful scale. However, a few considerations are worth noting: CockroachDB isn't fully compatible with vanilla PostgreSQL, as discussed in this [HN thread](https://news.ycombinator.com/item?id=25439878). Moreover, the benefits of horizontal scalability come with trade-offs in complexity and cost.

<HintBlock type="info">

This post is maintained by Bytebase, an open-source database DevSecOps tool that can manage both TiDB and MySQL. We update the post every year.

</HintBlock>

| Update History | Comment |
| -------------- | ---------------- |
| 2025/04/13 | Initial version. |

## TiDB Overview

TiDB (pronounced "tie-dee-bee," with "Ti" standing for Titanium) is an open-source NewSQL database that supports Hybrid Transactional and Analytical Processing (HTAP) workloads. Developed and maintained primarily by PingCAP, TiDB was designed to be MySQL compatible while addressing scalability challenges faced by traditional relational databases.

### History and Development

TiDB's development began with inspiration from Google's Spanner and F1 papers, aiming to create a distributed database that maintained SQL compatibility. The first general availability (GA) release of TiDB 1.0 was launched on October 16, 2017. Since then, TiDB has seen regular major releases. The most recent one is TiDB 8.5 GA on December 19, 2024.

### Architecture

TiDB's architecture consists of several key components that work together to provide a distributed database solution:

![tidb-arch](/content/blog/tidb-vs-mysql/tidb-arch.webp)

1. **TiDB Server**: The stateless SQL processing layer that receives SQL requests, parses and optimizes them, and generates distributed execution plans. This layer exposes MySQL-compatible endpoints, allowing applications to interact with TiDB as if it were a MySQL server.

2. **TiKV Server**: The distributed key-value storage layer that serves as the backbone of TiDB's data persistence. It stores data in a distributed manner across multiple nodes, ensuring high availability and fault tolerance through replication using the Raft consensus algorithm.

3. **Placement Driver (PD)**: The metadata management and scheduling layer. PD manages the metadata of the entire cluster, including topology and placement information of data. It also provides the TiDB Dashboard management UI and manages transaction identifiers.

4. **TiFlash**: A columnar storage engine designed specifically for analytical queries. TiFlash optimizes performance for hybrid transactional and analytical processing by caching data in a format suited for read-heavy analytical workloads.

This distributed architecture allows TiDB to scale horizontally by adding more nodes to the cluster, automatically sharding data into smaller pieces called "Regions" (approximately 100MB each) that are distributed across the storage nodes.

### Key Features

TiDB offers several distinctive features that set it apart:

- **Horizontal Scalability**: TiDB can expand both SQL processing and storage capacity by adding new nodes, with automatic data sharding and rebalancing.

- **MySQL Compatibility**: TiDB acts like a MySQL 8.0 server to applications, allowing users to continue using existing MySQL client libraries and tools.

- **Distributed Transactions**: TiDB supports ACID-compliant transactions across its distributed architecture, ensuring data consistency.

- **Cloud Native Design**: TiDB is built for cloud environments, with TiKV having graduated from the Cloud Native Computing Foundation (CNCF) in September 2020.

- **Real-time HTAP**: With both row-based (TiKV) and column-based (TiFlash) storage engines, TiDB efficiently handles both transactional and analytical workloads.

- **High Availability**: Using the Raft consensus algorithm, TiDB ensures data availability and automatic recovery from node failures.

## TiDB vs. MySQL

While TiDB and MySQL share SQL compatibility, they differ significantly in their design philosophy, architecture, and capabilities. Let's explore these differences across several key dimensions.

### License

**TiDB** is licensed under Apache 2.0, a permissive open-source license that allows for commercial use, modification, distribution, and private use without requiring derivative works to be open-sourced. This gives organizations flexibility in how they use and extend TiDB.

**MySQL**, on the other hand, uses a dual-licensing model:

- The Community Edition is available under the GNU General Public License (GPL) version 2, which requires derivative works to be open-sourced under the same license.
- The Enterprise Edition is available under a proprietary license from Oracle, offering additional features and support but requiring payment.

### Architecture

The architectural differences between TiDB and MySQL represent fundamentally different approaches to database design:

**TiDB** employs a distributed architecture with separate components for SQL processing, storage, and scheduling. This separation allows each component to scale independently based on workload requirements. Data is automatically sharded into Regions and distributed across storage nodes, with replication managed by the Raft consensus algorithm.

**MySQL** uses a monolithic architecture where a single server handles both SQL processing and storage. While it offers a pluggable storage engine architecture (allowing different engines like InnoDB, MyISAM, Memory, and Archive for different tables), the server itself remains a single unit. This design focuses on vertical scaling rather than horizontal distribution.

### Performance

**TiDB** is optimized for both OLTP and OLAP workloads (HTAP - Hybrid Transactional and Analytical Processing). Its distributed query processing allows parallel execution across nodes, which can significantly improve performance for complex queries and large datasets.

**MySQL** excels at OLTP workloads on a single node, offering very fast performance for simple queries. Its query cache can further accelerate frequently executed queries. However, performance is limited by single server resources and can degrade with very large datasets or complex analytical queries.

### Scalability

Scalability represents perhaps the most significant difference between these databases:

**TiDB** was designed for horizontal scalability from the ground up. It can scale to petabytes of data across hundreds of nodes with linear performance scaling by adding more TiDB or TiKV nodes. Data is automatically sharded and rebalanced across the cluster, and scaling operations can be performed without downtime.

**MySQL** was primarily designed for vertical scaling (adding more resources to a single server). While read scaling is possible through read replicas, write operations are limited by the capacity of a single server. Sharding at the application level or using third-party tools is required for horizontal scaling, often introducing complexity. [Vitess](https://vitess.io/) is a sharding solution built on top of MySQL.

### Online DDL Operations

Schema changes are a critical aspect of database management. Both MySQL and TiDB offer online DDL capabilities, but with different approaches.

**MySQL's Online DDL Approach**:

MySQL (specifically InnoDB) has [evolved its online DDL capabilities](https://dev.mysql.com/doc/refman/8.4/en/innodb-online-ddl-operations.html) over recent versions, offering different algorithms for schema changes:

- **Multiple DDL Algorithms**:

 - `INSTANT`: The fastest option that only modifies metadata without touching table data
 - `INPLACE`: Rebuilds the table in place but allows concurrent DML in most cases
 - `COPY`: Creates a temporary table (blocks concurrent DML operations)

- **Operation-Specific Support**: Different operations have different levels of online support:

 - Creating or adding secondary indexes: Fully online (INPLACE, concurrent DML allowed)
 - Dropping an index: Fully online (INPLACE, only modifies metadata)
 - Adding a primary key: Partially online (INPLACE but rebuilds table, concurrent DML allowed)
 - Adding columns: Varies (some can be INSTANT, others require INPLACE or COPY)

- **Limitations**: Not all operations can be performed online:

 - Dropping a primary key: Requires table rebuild and blocks concurrent DML
 - Some column type changes: Require the COPY algorithm and block concurrent operations
 - Adding FULLTEXT or SPATIAL indexes: Allow INPLACE but block concurrent DML

- **External Tools**: For operations that can't be performed online natively, MySQL users often rely on external tools like [gh-ost](https://github.com/github/gh-ost), [pt-online-schema-change](https://docs.percona.com/percona-toolkit/pt-online-schema-change.html), or [Facebook's OSC](https://github.com/facebookincubator/OnlineSchemaChange).

**TiDB's Online DDL Approach**:

TiDB implements a distributed, non-blocking approach to [DDL operations](https://docs.pingcap.com/tidb/stable/ddl-introduction/) that allows schema changes to be performed without interrupting ongoing database operations:

- **Asynchronous Schema Change Algorithm**: Based on Google's F1 paper, TiDB's implementation allows online table schema changes without blocking read/write operations.

- **Fast Online DDL**: [Introduced in TiDB 6.5](https://www.pingcap.com/blog/how-tidb-achieves-10x-performance-gains-in-online-ddl/), this feature achieves up to 10x performance improvement in operations like online index creation compared to previous versions.

- **No External Tools Required**: TiDB's approach eliminates the need for external schema change tools (like gh-ost or pt-osc) that are commonly used with MySQL.

- **Optimized Index Creation**: TiDB improves index creation through:

 - Transforming from a transactional batch-write mode to a file-based batch import mode
 - Optimizing data transmission by extracting only required columns before data transfer
 - Implementing parallel index ingesting to enhance efficiency

- **Performance Under Load**: TiDB maintains high performance for DDL operations even under heavy workloads, with benchmarks showing 8-13x faster performance than previous versions with 10K QPS.

Schema changes are often overlooked when evaluating databases. While MySQL provides robust online DDL capabilities—enhanced by external tools such as gh-ost and pt-online-schema-change—TiDB leverages its distributed architecture to manage schema changes more seamlessly and efficiently.

### Compatibility

**TiDB** provides MySQL protocol compatibility, acting like MySQL 8.0 to applications. It supports most MySQL syntax, data types, and functions, allowing existing MySQL applications to work with minimal changes. However, as TiDB was built from scratch rather than forked from MySQL, certain MySQL features (like stored procedure functionality) have limitations ([official compatibility doc](https://docs.pingcap.com/tidb/stable/mysql-compatibility/)).

For organizations heavily invested in MySQL-specific features or tools, these compatibility differences may require evaluation.

### Deployment Options

Both databases offer flexible deployment options:

**TiDB** can be deployed on-premises, in Kubernetes environments using TiDB Operator, or via the TiUP deployment tool for easy cluster management. Cloud deployment is supported on major cloud providers, and PingCAP offers TiDB Cloud as a managed service.

**MySQL** is available for on-premises deployment, as managed services on all major cloud platforms (Amazon RDS/Aurora, Azure Database for MySQL, Google Cloud SQL, Oracle MySQL Cloud Service), and in containerized environments. Various high-availability solutions like MySQL Group Replication, InnoDB Cluster, and NDB Cluster provide additional deployment flexibility.

## Comparison Table

| Feature | TiDB | MySQL |
| ---------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| **License** | Apache 2.0 (permissive open-source) | Dual-licensing: GPL v2 (Community) and Proprietary (Enterprise) |
| **Architecture** | Distributed architecture with separate components: - TiDB Server (SQL layer) - TiKV (distributed storage) - Placement Driver (coordination) - TiFlash (columnar storage) | Monolithic architecture: - Single server with pluggable storage engines - Primary engines: InnoDB |
| **Performance** | - Optimized for HTAP workloads - Distributed query processing - Higher latency for simple queries - Strong for complex queries and large datasets | - Optimized for OLTP workloads - Fast for simple queries - Query cache for frequent queries - Limited by single server resources |
| **Scalability** | - Horizontal scalability by design - Scales to petabytes across hundreds of nodes - Automatic sharding and rebalancing - No downtime for scaling operations | - Primarily vertical scaling - Read scaling through replicas - Limited by single server for writes - Scaling often requires downtime |
| **Online DDL** | - Fully distributed non-blocking approach - Fast Online DDL with 10x performance gains - No external tools required - Maintains performance under heavy load - Optimized for large tables | - Multiple algorithms (INSTANT, INPLACE, COPY) - Operation-specific support levels - Some operations require table rebuilds - External tools often needed for production - Performance can degrade under load |
| **Compatibility** | - MySQL protocol compatible - Supports most MySQL syntax and features - Some MySQL features have limitations - Not a MySQL fork, built from scratch | N/A |
| **Deployment Options** | - On-premises - Kubernetes with TiDB Operator - TiUP deployment tool - Cloud deployment - Docker - TiDB Cloud managed service | - On-premises - Major cloud platforms (RDS, Azure, GCP) - Docker |

Organizations typically start with MySQL due to its simplicity and familiarity, then transition to TiDB when facing scalability challenges. However, this scalability comes with trade-offs, especially around compatibility. To plan effectively for the long term, you should be cautious about features that TiDB does not support such as stored procedures.


DevOps and DevSecOps are methodologies aimed at improving software development and delivery, but they differ in their focus and integration of security.

## Where do DevOps and DevSecOps come from?

- **DevOps** emerged from the need to bridge the gap between software development (Dev) and IT operations (Ops). Its goal is to accelerate development cycles, improve collaboration, and enable continuous integration/continuous delivery (CI/CD).

- **DevSecOps** arose as an extension of DevOps, addressing the growing need of integrating security into fast-paced development cycles. Its goal is to embed security practices early and continuously to reduce risks without slowing down development.

## Key Differences

### Primary Focus

- **DevOps** focuses on the speed and efficiency of software development and delivery.

- **DevSecOps** emphasizes security throughout the development lifecycle.

### Security Integration

- **DevOps** treats security as a separate phase, e.g. penetration testing at the end of a release, along with security reviews.

- **DevSecOps** integrates security into every stage of the development lifecycle, from planning to deployment. Regulatory requirements (e.g., GDPR, HIPAA) are baked into workflows and automated audits replace manual compliance checks.

### Culture and Mindset

- **DevOps** "You build it, you run it". Developers take ownership of their code in production.

- **DevSecOps** "Security is everyone’s responsibility." Security is non-negotiable and foundational. People in every role integrate security thinking into their daily workflows.

### Toolchains and Technologies

- **DevOps Tools:**

  - **CI/CD:** Jenkins, GitLab CI, GitHub Actions
  - **Infrastructure as Code (IaC):** Terraform, CloudFormation, Ansible
  - **Containerization:** Docker, Kubernetes, Helm
  - **Monitoring/Logging:** Prometheus, Grafana, Datadog
  - **VCS:** GitHub, GitLab, Bitbucket

- **DevSecOps Tools** (Layered into the pipeline):
  - **Code Scanning:** SonarQube, Snyk Code, Fortify
  - **Security Testing:** OWASP ZAP, Checkov
  - **Runtime Security:** Wiz, Falco, Aqua Security
  - **Policy & Compliance:** Open Policy Agent (OPA), Drata, Vanta

### Speed vs Security Trade-offs

- **DevOps** prioritizes speed. Faster releases, but risk of late-discovered vulnerabilities.

- **DevSecOps** slightly slower but more secure. Catching issues early avoids larger delays and risks later.

## Summary

| Dimension             | DevOps                                              | DevSecOps                                              |
| --------------------- | --------------------------------------------------- | ------------------------------------------------------ |
| **Focus**             | Speed, automation, and collaboration                | Speed with integrated security throughout the pipeline |
| **Security**          | Added late in the cycle (e.g., during final review) | Embedded in every stage from planning to production    |
| **Mindset**           | “You build it, you run it”                          | “Security is everyone’s responsibility”                |
| **Tools**             | CI/CD, IaC, container orchestration, monitoring     | All DevOps tools + security scanners, audit tools      |
| **Speed vs Security** | Prioritizes speed, may defer security checks        | Balances speed with continuous security validation     |


<HintBlock type="info">

This post is maintained by [Bytebase](https://www.bytebase.com/), an open-source database DevSecOps tool that provides an alternative to both pgAdmin and DBeaver. We update the post every year.

</HintBlock>

| Update History | Comment |
| -------------- | ---------------- |
| 2025/03/25 | Initial version. |

> For the impatient, jump to the [last section](#pgadmin-or-dbeaver) to see the comparison table.

## Introduction

Database administrators and developers often need specialized tools to interact with their databases efficiently. When working with PostgreSQL, two of the most popular options are pgAdmin and DBeaver. While pgAdmin is purpose-built for PostgreSQL, DBeaver offers a multi-database solution that supports PostgreSQL among many other database systems.

At Bytebase, we work extensively with database tools and have hands-on experience with both pgAdmin and DBeaver. In this article, we'll provide an in-depth comparison to help you choose the right tool for your needs.

## Background

![pgadmin](/content/blog/pgadmin-vs-dbeaver/pgadmin.webp)

**pgAdmin** is the official open-source management tool for PostgreSQL. First released in 1998, it's been the go-to administration and development platform specifically designed for PostgreSQL. The current version, pgAdmin 4, is a complete rewrite built using Python and JavaScript/jQuery with Bootstrap, making it available as both a web and desktop application.

![dbeaver](/content/blog/pgadmin-vs-dbeaver/dbeaver.webp)

**DBeaver** is a universal database tool that supports a wide range of database systems. Created in 2013 by Serge Rider, DBeaver is built on the Eclipse platform using Java. It comes in both Community Edition (free and open-source) and Enterprise Edition (commercial) versions, with the latter offering additional features and database support.

## Feature Comparison

### Database Support

**pgAdmin**: As a specialized tool, pgAdmin supports only PostgreSQL and its derivatives (such as AWS Aurora PostgreSQL, Azure Database for PostgreSQL, etc.).

**DBeaver**: Offers support for PostgreSQL and its derivatives, MySQL/MariaDB, Oracle, SQL Server, SQLite, IBM DB2, MongoDB, Cassandra, Redis And 80+ other database engines.

DBeaver Community Edition supports most popular open-source databases, while Enterprise Edition adds support for proprietary databases like Oracle and DB2.

### User Interface

**pgAdmin**:

pgAdmin features a web-based interface available in both standalone and browser-based formats. It deliberately focuses on PostgreSQL-specific features with a navigation hierarchy that faithfully follows PostgreSQL's object model. This design philosophy results in a simpler interface with fewer options, effectively reducing complexity for users who work primarily with PostgreSQL databases.

![pgadmin-ui](/content/blog/pgadmin-vs-dbeaver/pgadmin-ui.webp)

**DBeaver**:

DBeaver employs a traditional desktop application interface that offers greater complexity with numerous options and views. Its highly customizable environment features dockable panels that users can arrange according to their preferences.

One of DBeaver's strengths is its consistent interface that remains uniform regardless of which database type you're working with, making it particularly valuable for those managing multiple database systems.

![dbeaver-ui](/content/blog/pgadmin-vs-dbeaver/dbeaver-ui.webp)

### Query Tool and SQL Editor

**pgAdmin**:

pgAdmin provides a SQL editor optimized for PostgreSQL with dialect-specific syntax highlighting and auto-completion for PostgreSQL objects and functions. It includes practical features such as EXPLAIN visualization, query history, and basic code formatting. This PostgreSQL-specific focus benefits administrators who work exclusively with PostgreSQL databases.

**DBeaver**:

DBeaver offers a versatile SQL editor supporting multiple database platforms with database-agnostic syntax highlighting and intelligent auto-completion.

It includes practical features like multiple result tabs for comparing queries and a visual query builder for graphical query creation. These capabilities make DBeaver particularly efficient for developers who work with various database systems.

### Schema Management

**pgAdmin**:

pgAdmin provides a comprehensive schema browser tailored specifically for PostgreSQL databases. It offers detailed property editors for all PostgreSQL object types and includes specialized tools such as a schema diff utility for comparing database structures, a grant wizard for managing permissions, and visual tools for index creation.

These PostgreSQL-specific features create a streamlined workflow for database administrators focused on PostgreSQL environments.

**DBeaver**:

DBeaver features a universal schema browser that works consistently across different database platforms. Its object editors support tables, views, and other database objects with a standardized interface.

The Enterprise Edition adds schema comparison capabilities, while all versions include schema and data migration tools. DBeaver also provides ER diagrams for visualizing table relationships, making it particularly useful for understanding complex database designs across multiple systems.

### Performance

**pgAdmin**:

- Lighter resource usage for simple operations
- Can be slower for large databases
- Web architecture can introduce latency
- Improved performance in recent versions

**DBeaver**:

- Higher initial resource usage due to Java
- Better performance with large databases
- Caching mechanism for metadata
- Can handle multiple large query results efficiently

### Data Visualization and Editing

**pgAdmin** provides basic data interaction capabilities with a simple grid view for query results, in-place data editing, and limited visualization options. Its straightforward approach prioritizes essential PostgreSQL data management tasks.

**DBeaver** offers significantly more advanced data handling with feature-rich grids supporting grouping, filtering, and custom formatting. It provides multiple view modes including grid, text, JSON, and XML formats, along with visual editing that includes foreign key navigation. DBeaver further extends functionality with comprehensive export options and basic charting capabilities for data visualization.

### Cross-Platform Support

**pgAdmin** delivers flexible access across Windows, macOS, and Linux through both desktop applications and browser-based interfaces. Its web architecture enables access from virtually any device with a browser, while Docker container deployment offers simplified distribution for enterprise environments.

**DBeaver** supports the major desktop platforms (Windows, macOS, Linux) but requires a Java runtime environment. Unlike pgAdmin, it lacks native mobile support and web-based access options, positioning it primarily as a desktop-focused solution.

### Installation and Setup

**pgAdmin** offers dual deployment paths: a straightforward desktop mode with packaged installers for individual users, and a more complex server mode requiring web server configuration for team environments. The Docker deployment option simplifies enterprise setup for centralized access.

**DBeaver** emphasizes simplicity with a straightforward standalone installation process that requires minimal configuration. It offers a portable version that needs no installation and eliminates server setup requirements for basic usage, making it immediately accessible to individual users.

### Community and Support

**pgAdmin**:

pgAdmin benefits from official PostgreSQL community backing, providing users with reliable documentation and established support channels.

Its development cycle tends to be more measured, prioritizing stability and compatibility with PostgreSQL's core functionality over rapid feature expansion. This conservative approach ensures dependable performance for production environments but may result in slower adoption of new capabilities.

**DBeaver**:

DBeaver maintains a larger and more active user community with frequent updates and feature additions. Its broader user base spans multiple database platforms, creating a diverse ecosystem of knowledge sharing.

For organizations requiring guaranteed assistance, DBeaver offers commercial support options not available with pgAdmin, making it suitable for enterprise environments where service level agreements are necessary.

### Pricing

**pgAdmin**:

- Completely free and open-source
- No commercial version

**DBeaver**:

- Community Edition: Free and open-source
- Enterprise Edition: Commercial license (starts at $149 per user)
- Enterprise Edition features include:
 - NoSQL database support
 - Schema comparison
 - Visual query builder
 - Team collaboration features
 - Mock data generation

## pgAdmin or DBeaver

| | pgAdmin | DBeaver |
| ----------------- | ------------------------------------------ | ----------------------------------------------- |
| Focus | PostgreSQL-specific | Universal database tool |
| Interface | Modern web-based UI | Traditional desktop application |
| Database Support | PostgreSQL only | 80+ databases (Community: 25+, Enterprise: 80+) |
| Resource Usage | Lighter for simple operations | Heavier (Java-based) |
| Query Editor | PostgreSQL-optimized | Feature-rich, database-agnostic |
| Schema Management | Comprehensive for PostgreSQL | Universal with visual tools |
| Installation | Desktop app, web server, or Docker | Desktop application |
| Pricing | Free and open-source | Community (free), Enterprise (paid) |
| Best For | PostgreSQL DBAs, simple server deployments | Developers working with multiple databases |

<HintBlock type="info">

For teams looking for a collaborative approach to database management with workflow controls, integrated change review, and enhanced security features, you might want to consider Bytebase, please check out [Bytebase vs. DBeaver](/blog/bytebase-vs-dbeaver/).

</HintBlock>

## References

- [pgAdmin Documentation](https://www.pgadmin.org/docs/)
- [DBeaver Documentation](https://dbeaver.com/docs/wiki/)
- [PostgreSQL Official Website](https://www.postgresql.org/)
- [Top PostgreSQL GUI Clients](/blog/top-postgres-gui-client/)
- [Top Open Source SQL Clients](/blog/top-open-source-sql-clients/)


This is a series of articles between MySQL and PostgreSQL:

- [MySQL vs. Postgres](/blog/postgres-vs-mysql)
- [PlanetScale vs. Neon](/blog/planetscale-vs-neon)
- TiDB vs. CockroachDB (this one)

---

[TiDB](https://www.pingcap.com/) and [CockroachDB](https://www.cockroachlabs.com/) are the two most popular distributed database solutions (NewSQL) in their respective ecosystems.
While teams rarely choose a distributed database on day one, understanding these options becomes crucial when scalability challenges arise.

## Why Compare TiDB and CockroachDB

1. As a responsible architect, it's essential to understand the entire ecosystem before making initial database choices that may impact future scalability options.

1. When teams eventually consider distributed databases, scalability is typically the primary concern, with cost as a secondary factor. If a solution can effectively address these challenges, other obstacles—including switching database dialects—can be overcome.

Below we compare between TiDB and CockroachDB from the following dimensions:

- [License](#license)
- [Compatibility](#compatibility)
- [Architecture](#architecture)
- [Scalability](#scalability)

## License

TiDB is licensed under [Apache License 2.0](https://github.com/pingcap/tidb/blob/master/LICENSE). Its internal storage engine [TiKV](https://github.com/tikv/tikv), which has been donated to CNCF, is also under Apache License 2.0.

CockroachDB [switched to an Enterprise license in 2024](https://www.cockroachlabs.com/enterprise-license-update/), generating backlash in the community ([1](https://news.ycombinator.com/item?id=41256222), [2](https://www.reddit.com/r/devops/comments/1eytts3/cockroachdb_is_changing_license_again_moving_to/)). Its storage engine [Pebble](https://github.com/cockroachdb/pebble) uses BSD-3-Clause License.

## Compatibility

Both products advertise compatibility with their vanilla counterparts (MySQL for TiDB, PostgreSQL for CockroachDB), but this primarily refers to wire-protocol compatibility rather than full feature parity:

- [TiDB MySQL compatibility](https://docs.pingcap.com/tidb/stable/mysql-compatibility/), with its [parser](https://github.com/pingcap/tidb/tree/master/pkg/parser) available for inspection.
- [CockroachDB PostgreSQL compatibility](https://www.cockroachlabs.com/docs/stable/postgresql-compatibility). There is also a [PostgreSQL compatibility index](https://github.com/secp256k1-sha256/postgres-compatibility-index/tree/main) tracking various PostgreSQL implementations including CockroachDB.

## Architecture

TiDB uses a modular architecture with separation between components:

- SQL layer written in Go
- Storage engine (TiKV) written in Rust
- Clear division between compute and storage components

![tidb-arch](/content/blog/tidb-vs-cockroachdb/tidb-arch.webp)

On the other hand, CockroachDB employs a more unified approach:

- Both SQL layer and storage engine (Pebble) written in Go
- Each node bundles SQL processing and storage functionality
- More integrated design philosophy

![cockroachdb-arch](/content/blog/tidb-vs-cockroachdb/cockroach-arch.webp)

Another critical architectural difference is in timestamp generation for transaction management:

- TiDB uses a dedicated timestamp allocator (Placement Driver)
- CockroachDB relies on NTP (Network Time Protocol)

## Scalability

The architectural differences lead to different scalability characteristics:

TiDB:

- Independent scaling of compute (TiDB) and storage (TiKV) layers
- More flexible for workloads with uneven compute/storage demands
- May require more configuration tuning for optimal performance
- Better suited for read-heavy workloads and analytical queries at scale (via TiFlash)

CockroachDB:

- Simpler "add more nodes" scaling model
- More consistent performance across different scales
- Generally easier initial setup and maintenance
- Typically performs better for write-intensive workloads

Neither system is universally "better" for all use cases. You have to extensively test your particular workload patterns.

## Summary

This concludes our MySQL vs. PostgreSQL series. When reaching the distributed database level, the original
SQL dialect becomes less relevant than the scalability and operational characteristics of the distributed system.
Both TiDB and CockroachDB offer compelling solutions for scaling beyond traditional single-node databases, but with different approaches and trade-offs.

The choice between TiDB and CockroachDB should be driven primarily by your specific scaling needs, workload patterns,
and operational preferences rather than strict adherence to the MySQL or PostgreSQL ecosystem.

For teams at this crossroads, it's worth investing time in proof-of-concept testing with both systems using representative workloads before making a final decision.

Of course, needing a distributed database solution is a good problem to have – it means you've grown beyond what conventional databases can handle.



"Bytebase vs StrongDM" is a common search for teams building out database access control, but the two products solve overlapping and different problems. StrongDM is a privileged access management (PAM) platform covering databases, servers, Kubernetes, and cloud services. Bytebase is a database DevSecOps platform covering schema changes, SQL review, access control, and data masking. They meet in the middle at just-in-time (JIT) database access. Most teams that evaluate both end up using them together, not choosing between them. This 2026 comparison covers where they overlap, where they diverge, and how each fits into a production database stack.

## Bytebase vs StrongDM at a glance

|  | StrongDM | Bytebase |
| --- | --- | --- |
| [Primary category](#different-problems-overlapping-capabilities) | Privileged access management (PAM) for infrastructure | Database DevSecOps (change, access, review) |
| [Infrastructure scope](#different-problems-overlapping-capabilities) | Databases, servers, Kubernetes, cloud, web apps | Databases |
| [JIT database access](#access-and-approval) | ✅ Core feature | ✅ Core feature (Enterprise) |
| [Schema change workflow](#schema-change-and-sql-review) | ❌ | ✅ Issue-based with approvals |
| [SQL Review (static + policy)](#schema-change-and-sql-review) | ❌ | ✅ 200+ rules |
| [Dynamic data masking](#data-masking) | ❌ | ✅ Semantic types |
| [Built-in SQL editor](#sql-editor) | ❌ (external client) | ✅ Browser-based |
| [Approval flow](#access-and-approval) | Static approvers per access rule | Risk-based with custom conditions |
| [Audit log scope](#audit-log) | All access + admin actions | All access + change + review + admin |
| [Supported databases](#supported-databases) | 30+ SQL and NoSQL | 20+ SQL and NoSQL |
| [Pricing model](#pricing) | Quote-based, per-seat + per-resource | Free Community · $20/user/mo Pro · Enterprise custom |
| [Open source](#open-source) | ❌ | ✅ MIT + Enterprise License |
| [Self-host](#open-source) | ✅ (Gateway/Relay) | ✅ (Docker, Kubernetes, binary) |

## Different problems, overlapping capabilities

**StrongDM** is a PAM platform. The product's job is to sit in front of every resource a privileged user might need (databases, Linux boxes, Windows boxes, Kubernetes clusters, AWS/GCP/Azure consoles, internal web apps) and broker access to each one. A developer connects to StrongDM once; StrongDM routes and logs every downstream session. JIT access, role assignments, and audit trails are unified across all those resource types.

**Bytebase** is a database DevSecOps platform. The product's job is to sit in front of database changes, queries, and access (databases only) and run the workflow that keeps production databases safe: schema migrations with SQL review, approval flows, dynamic data masking, and JIT query access. It does not manage SSH, Kubernetes, or cloud service access.

The overlap is JIT database access and audit logging. If that is the only capability a team needs, either product can deliver it. Teams typically choose between them based on scope:

- If a team is standing up PAM across all infrastructure (databases plus servers, plus Kubernetes, plus cloud), StrongDM is the natural fit. The database access module comes free with the broader platform.
- If a team already has infrastructure PAM and needs to add database change management, SQL review, and per-table masking, Bytebase is the addition. StrongDM does not cover those.

Most mid-to-large engineering organizations end up with both. StrongDM handles cross-resource JIT and session logging; Bytebase handles the database workflow (migrations, SQL review, masking exceptions, per-environment policy). For a deeper look at the design patterns here, see [database access control best practices](/blog/database-access-control-best-practices/) and [just-in-time database access](/blog/just-in-time-database-access/). For a real-world example, [how note.com implements JIT database access with Bytebase](/blog/note-case-study/) walks through the pattern in production.

## Schema change and SQL review

**Only Bytebase has this.** StrongDM brokers access and logs sessions; it does not inspect the SQL before it runs or manage the schema change itself.

Bytebase's change workflow gates DDL and DML through an issue that runs [SQL Review](/blog/sql-review-tool-for-devs/) (200+ rules: missing primary keys, non-additive schema changes, dangerous DML without a `WHERE`, performance anti-patterns) and a custom approval flow before the change deploys. The full history is recorded for rollback and audit. For the broader category this sits in, see [what is database change management](/blog/what-is-database-change-management/).

If a team uses StrongDM alone, schema changes usually go through a parallel tool (Liquibase, Flyway, Bytebase, homegrown scripts) while StrongDM handles the human session access. Bytebase is where the schema change + approval + SQL review all live in one workflow.

## Access and approval

- **StrongDM** controls access through role assignments and access rules. Rules can be static (explicit grants) or dynamic (tag-based automation that grants access to resources matching criteria). Approval is configured per access rule: named approvers sign off on JIT requests. StrongDM Vault (launched 2025) also manages database credentials centrally, so teams don't store them in secrets tools.
- **Bytebase** uses two-level RBAC (Workspace admin/DBA, Project owner/developer/SQL Editor user) with risk-based approval flows. The risk level of a change or query request (DML, DDL, create database, query, export) is computed against custom rules, and the issue routes to the matching approver chain automatically. Per-environment policy adjusts enforcement (warn in dev, block in prod).

The practical difference: StrongDM's approval is a single checkpoint before a session opens. Bytebase's approval is inside the change workflow, where the approver sees the SQL, the SQL review results, and the environment policy in the same issue.

## Data masking

- **StrongDM**: no dynamic masking. Role-based access can restrict who sees which resource, but the data itself is not transformed. An engineer with approved access to a production database sees the full column values.
- **Bytebase**: dynamic data masking with semantic types (email, phone, credit card). Admins tag columns; Bytebase applies the masking algorithm in SQL Editor and export flows. Exemptions go through approval and are logged in the same audit trail.

For teams that handle regulated data (PCI, HIPAA, GDPR) and need query-time masking, this is a Bytebase-only capability. StrongDM typically pairs with a separate masking tool for this requirement.

## SQL editor

- **StrongDM**: no built-in SQL editor. Engineers connect through StrongDM with their own tool (DBeaver, DataGrip, psql, mysql CLI). StrongDM brokers the connection; the SQL interface is whatever the engineer brought.
- **Bytebase**: built-in browser SQL editor with schema browser, saved sheets, history, and inline masking. Engineers who only need query access can work entirely in the browser.

If your team standardizes on a SQL client and just needs secure access to the database, StrongDM's approach is fine. If you want an auditable, browser-only query path that enforces masking and export policy, Bytebase's editor is the difference.

## Audit log

Both products log comprehensively, but the content differs.

- **StrongDM** logs every session, every query, every SSH command, every RDP action, plus administrator actions (permission changes, resource onboarding). The audit surface spans all infrastructure connected to StrongDM.
- **Bytebase** logs every session, every SQL Editor query, every schema change, every access grant, every approval decision, every masking exemption. The audit surface is database-specific but deeper at the workflow level. For the SOC 2 mapping, see [audit log for SOC 2 compliance](/blog/soc2-audit-logging/).

For a SOC 2 auditor asking "show me admin activity with user + action + timestamp + affected resource" across infrastructure, StrongDM's log covers the surface area. For the same question about database changes and approvals, Bytebase's is deeper.

## Supported databases

- **StrongDM**: 30+ databases. MySQL, PostgreSQL, Oracle (including 26ai support added 2025), SQL Server, ClickHouse, MongoDB, Redis, Redshift, Snowflake, DB2, Sybase.
- **Bytebase**: 20+ databases. MySQL, PostgreSQL, Oracle, SQL Server, ClickHouse, MongoDB, Redis, Redshift, Snowflake, TiDB, Spanner, OceanBase.

Both cover the common set. StrongDM has a longer tail of legacy engines (DB2, Sybase); Bytebase covers newer distributed engines (TiDB, Spanner, OceanBase).

## Pricing

| | StrongDM | Bytebase |
| --- | --- | --- |
| Free / self-host | n/a (no free tier) | **Community: free** (self-host or cloud, up to 20 users, 10 instances) |
| Entry paid | **Team tier: quote-based**, typical annual contract $25k–$75k per Vendr 2026 data | **Pro: $20/user/mo** (cloud, unlimited users, SSO, audit log) |
| Enterprise | **Business / Enterprise tiers: quote-based** | **Custom** (self-host or cloud, dynamic masking, custom approval, SCIM, OIDC/LDAP SSO, 2FA) |

Two honest notes on pricing:

- StrongDM does not publish per-user pricing. Contracts are negotiated on seats plus resources connected. Small teams often start at a higher floor than Bytebase Pro because StrongDM is priced for the PAM-platform scope, not just database access.
- The right pricing comparison depends on what the team actually needs. A team that only needs database JIT and change management over-pays for StrongDM's platform. A team that needs PAM across servers, Kubernetes, and databases under-uses Bytebase's database-specific workflow.

## Open source

- **StrongDM**: closed source. Self-hosting is available via Gateway/Relay infrastructure, but the product code is not public.
- **Bytebase**: open source, MIT-licensed core with a separate Enterprise License for commercial features. Source is on [GitHub](https://github.com/bytebase/bytebase). Self-host with Docker, Kubernetes, or a standalone binary.

## Which should you choose?

This is rarely an either-or decision. The honest framing:

- **Pick StrongDM** if the primary need is PAM across multiple infrastructure types (databases plus servers, plus Kubernetes, plus cloud consoles). Database access comes with the platform.
- **Pick Bytebase** if the primary need is database change management, SQL review, dynamic masking, and per-environment policy with JIT query access included. Open-source option available.
- **Run both** if you have a mid-to-large engineering org with complex infrastructure that also needs database DevSecOps. StrongDM handles cross-resource session access; Bytebase handles the database workflow.

## FAQ

**Is Bytebase a StrongDM alternative?**
Partially. For JIT database access, yes — both products cover that need. For the full StrongDM scope (PAM across databases, servers, Kubernetes, cloud), Bytebase is not an alternative. Bytebase is database-only. Teams that need PAM for non-database infrastructure keep StrongDM.

**Does StrongDM review SQL?**
No. StrongDM brokers database connections and logs the sessions; it does not inspect SQL statements against rules before they run, and it does not manage schema changes. SQL review is a separate tool category. See [SQL Review: from linting to governance](/blog/sql-review-tool-for-devs/) for how SQL review fits into database DevSecOps.

**What's the difference between PAM and database DevSecOps?**
PAM (privileged access management) covers access to infrastructure: who can connect to which database, server, or Kubernetes cluster, for how long, with what approval. Database DevSecOps covers the database workflow specifically: schema changes, SQL review, approval flows for DML/DDL, data masking, and per-environment policy. PAM answers "who can get to the database." Database DevSecOps answers "what can they do once they're there."

**Can StrongDM and Bytebase run together?**
Yes. A common pattern: StrongDM brokers the connection and manages the session audit trail across all infrastructure; Bytebase handles the database-specific workflow (change issues, SQL review, masking) on top of the access StrongDM provides. The products do not conflict.

**What's the best JIT database access tool?**
Depends on scope. If you need JIT across databases and other infrastructure, StrongDM is the standard choice. If you need JIT as part of a database-specific workflow that also includes change management and SQL review, Bytebase is the integrated option. Teams that need both scopes run both.


<HintBlock type="info">

As GitHub and GitLab continue to iterate, we will update this post regularly.

</HintBlock>

![2024-mq-devops](/content/blog/github-vs-gitlab/2024-mq-devops.webp)

In the latest 2024 Gartner Magic Quadrant for DevOps Platforms, GitLab remains the highest-ranked platform. We acknowledge GitLab's leadership, particularly in more advanced features. However, GitHub has narrowed the feature gap and offers a more intuitive interface. Based on our work with engineering teams globally, we observe a growing trend of GitHub's increasing popularity. Today, GitHub is not just a platform for hosting open-source projects; it is increasingly seen as a competitive enterprise developer platform.

Unless you are an Atlassian fanatic, your choice for source code management (SCM) will likely come
down to GitHub or GitLab. At Bytebase, we’ve extensively used both to develop our database-as-code
solutions. Drawing from our hands-on experience, we provide a detailed comparison of GitHub and GitLab
across the following key areas.

- [Summary](#summary)
- [History](#history)
- [Open Source](#open-source)
- [Deployment Model](#deployment-model)
- [Tech Stack](#tech-stack)
- [Repository Management](#repository-management)
- [CI/CD](#cicd)
- [Code Review](#code-review)
- [Code Search](#code-search)
- [Security](#security)
- [Project Management](#project-management)
- [AI](#ai)
- [Pricing](#pricing)
- [GitHub vs GitLab](#github-or-gitlab)

## Summary

| | GitHub | GitLab | Notes |
| --------------------- | ------------------------ | ------------------------------------------------- | -------------------------------------------------------------------------------------------- |
| Cloud | github.com | gitlab.com | |
| Self-host | GitHub Enterprise (paid) | GitLab Community (free), GetLab Enterprise (paid) | |
| Open-source | ❌ | ✅ | |
| Tech Stack | RoR + MySQL | RoR + Go + Postgres | |
| Repository Management | ⭐️⭐️⭐️ | ⭐️⭐️⭐️ | |
| Code Review | ⭐️⭐️ | ⭐️ | Alternatives: [Graphite](https://graphite.dev/), [Gerrit](https://www.gerritcodereview.com/) |
| Code Search | ⭐️⭐️ | ⭐️ | Alternatives: [Sourcegraph](https://sourcegraph.com/) |
| Security | ⭐️⭐️ | ⭐️⭐️ ⭐️ | |
| Project Management | ⭐️ | ⭐️ | Alternatives: [Linear](https://linear.app/), [Jira](https://www.atlassian.com/software/jira) |
| AI | ⭐️⭐️⭐️ | ⭐️ | |

## History

![github](/content/blog/github-vs-gitlab/github.webp)

**GitHub** was launched in [April 2008](https://github.blog/news-insights/the-library/we-launched/). GitHub
quickly gained traction among developers, becoming a hub for open-source projects. In 2018, Microsoft
[acquired](https://news.microsoft.com/2018/06/04/microsoft-to-acquire-github-for-7-5-billion/) GitHub for $7.5 billion.
In 2021, GitHub [introduced Copilot](https://github.blog/news-insights/product-news/introducing-github-copilot-ai-pair-programmer/),
revolutionizing how developers write code. Today, GitHub is the world's largest code collaboration platform,
empowering millions of developers and organizations globally.

---

![gitlab](/content/blog/github-vs-gitlab/gitlab.webp)

**GitLab** was launched in October 2011 as an open-source project, aiming
to provide a self-hosted alternative to existing Git repository management solutions.

![gitlab-yc](/content/blog/github-vs-gitlab/gitlab-yc.webp)

During the [YC 2015 pitch](https://www.youtube.com/watch?v=HmrDjvv_ENQ&t=141s), GitLab positioned itself as an
open-source GitHub alternative. In 2021, GitLab goes public on the NASDAQ under the ticker `GTLB`. Over time,
GitLab evolved from a DevOps platform to a comprehensive DevSecOps solution, integrating security into the software development lifecycle (SDLC).
In May 2023, GitLab [introduced AI-powered features](https://about.gitlab.com/press/releases/2023-05-22-gitlab-16-announces-ai-powered-devsecops-platform/)
with the release of GitLab 16, followed by the beta release of GitLab Duo Chat in November 2023.

## Open Source

GitLab is fully open-source. However, while GitHub is the go-to platform for hosting and promoting open-source projects, GitHub itself is not open-source. Nevertheless, GitHub has contributed significantly to the open-source ecosystem, developing impactful projects like [gh-ost](https://github.com/github/gh-ost), a MySQL online schema migration tool.

<HintBlock type="info">

Bytebase has integrated [gh-ost](https://docs.bytebase.com/change-database/online-schema-migration-for-mysql/), offering a more user-friendly interface with the built-in approval workflow.

</HintBlock>

## Deployment Model

GitHub began as a SaaS platform and later introduced self-hosted options through GitHub Enterprise.

GitLab started as a self-hosted solution and subsequently offered a SaaS option.

Despite both platforms offering SaaS and self-hosted options, there's a perception that GitHub is primarily SaaS-focused, while GitLab is more associated with self-hosting. GitHub has enhanced its self-hosted offerings with features like CodeQL.

<HintBlock type="info">

You can self-host GitLab Community Edition for free, whereas self-hosting GitHub requires the Enterprise version.

</HintBlock>

## Tech Stack

GitHub primarily uses Ruby on Rails for its core application, with MySQL serving as its main database.

GitLab also relies on Ruby on Rails but uses PostgreSQL as its core database. In 2018, GitLab introduced `Gitaly`,
a Git RPC service written in Go. For more details on GitLab’s tech stack, refer to the [installation requirements](https://docs.gitlab.com/ee/install/requirements.html).

## Repository Management

The core feature sets are comparable. However, GitHub leads the way in code collaboration, pioneering lightweight forks and pull requests (PRs).

GitHub co-founder Chris Wanstrath [wrote a detailed thread](https://x.com/defunkt/status/1623393340967501824) on the origins of pull requests.

GitLab later introduced merge requests (MRs), its equivalent to GitHub's PRs, along with fork functionality.

## CI/CD

GitLab CI pioneered CI/CD space:

- It's the first product with the built-in integration with the code repository.
- It's the first product introducing Auto DevOps and integrated security (DevSecOps).

Feature-wise, GitLab excels with built-in support for advanced features like [multi-project](https://docs.gitlab.com/ee/ci/pipelines/downstream_pipelines.html) pipelines, [canary deployments](https://docs.gitlab.com/ee/user/project/canary_deployments.html).

In contrast, GitHub Actions stands out with its extensive third-party ecosystem and user-friendly interface. Additionally, GitHub offers [more runtime environments](https://docs.github.com/en/actions/sharing-automations/creating-actions/about-custom-actions), supporting not only Shell and Docker images, but also JavaScript, which GitLab [does not natively support](https://docs.gitlab.com/runner/executors/index.html).

## Code Review

GitHub and GitLab both offer standard code review features. GitHub excels in third-party integration through GitHub Apps and GitHub Actions.

In GitLab, third-party tools can only post comments below merge requests. In GitHub, third-party tools can post inline comments directly within pull requests, enhancing review workflows. For instance, Bytebase's [SQL Review GitHub Actions](https://www.bytebase.com/docs/gitops/sql-review-ci/) can lint SQL and post inline comments within the code:

![github-pr-inline](/content/blog/github-vs-gitlab/github-pr-inline.webp)

If you're not satisfied with the built-in code review interface, you can explore [Graphite](https://graphite.dev/) (inspired by Meta's `stacked diffs`) and [Gerrit](https://www.gerritcodereview.com/) (Google's open-source version).

## Code Search

GitHub offers superior code search capabilities and has a much larger pool of source code to ~copy~search from.
However, neither platform provides an optimal code search experience, so it's worth exploring [Sourcegraph](https://sourcegraph.com)
for a more powerful solution.

## Security

GitLab pioneered the DevSecOps concept, being the first platform to fully integrate security into the development pipeline. In the latest Gartner® Magic Quadrant™ for Application Security Testing, it also ranks higher than GitHub.

![2023-magic-quadrant](/content/blog/github-vs-gitlab/2023-mq-ast.webp)

GitHub continues to excel in usability. In 2019, it acquired Semmle, the creator of CodeQL, and Dependabot. These two features have since become the foundation of GitHub's security portfolio.

## Project Management

Both GitHub and GitLab offer project management features, such as `projects` and `issues`. However, many users still prefer specialized project management tools, like [Linear](https://linear.app/) or the mighty [Jira](https://www.atlassian.com/software/jira).

## AI

GitHub has Copilot

![2024-mq-ai-code](/content/blog/github-vs-gitlab/2024-mq-ai-code.webp)

## Pricing

![pricing-github](/content/blog/github-vs-gitlab/pricing-github.webp)
![pricing-gitlab](/content/blog/github-vs-gitlab/pricing-gitlab.webp)

Both GitHub and GitLab offer three tiers, starting with a free plan. GitHub [provides a free tier for Copilot](https://github.blog/news-insights/product-news/github-copilot-in-vscode-free/), while GitLab Duo is available at an additional cost.

## GitHub or GitLab

GitLab offers a free self-hosted tier and advanced features without relying on third-party integrations.

GitHub, in contrast, provides an intuitive interface, integrated AI capabilities, and a rich third-party ecosystem.

Unless your organization has a stringent requirement for a self-hosted option or lacks the budget for GitHub Enterprise, we recommend starting with GitHub. Your developers are likely already familiar with GitHub, as many use it for personal projects or exploration in their spare time.

Happy hacking!

## References

- [Chris Wanstrath (GitHub co-founder) thread on starting GitHub](https://x.com/defunkt/status/1715128542391153068)
- [A Brief History of the Pull Request](https://rdnlsmith.com/posts/2023/004/pull-request-origins/)
- [History of GitLab](https://handbook.gitlab.com/handbook/company/history/)
- [The road to Gitaly v1.0](https://about.gitlab.com/blog/2018/09/12/the-road-to-gitaly-1-0/)
- [Database as Code](/blog/database-as-code/)

<HintBlock type="info">

This post is maintained by Bytebase, an open-source database DevSecOps tool that can manage both MySQL and MariaDB. We update the post every year.

</HintBlock>

| Update History | Comment |
| -------------- | ------------------------------------------------------------------------------- |
| 2024/09/29 | Initial version. |
| 2024/11/05 | It was noted that MariaDB has had Atomic DDL since MariaDB 10.6. |
| | It was noted that MariaDB has equal or better set of JSON functions that MySQL. |
| 2025/02/28 | Update TOC |

MySQL turns 30 in 2025. While MariaDB was initially a fork of MySQL, the two have evolved over time, each with its own strengths and development priorities. Recently, [MariaDB is acquired by K1](https://mariadb.com/newsroom/press-releases/k1-acquires-a-leading-database-software-company-mariadb-and-appoints-new-ceo/), marking the new journey for MariaDB.

At Bytebase, we work with both databases extensively since the Bytebase product needs to integrate
with both databases. Our founders also build [Google Cloud SQL](https://cloud.google.com/sql). Drawing from our operational experience, we outline below the respective strengths of MySQL and MariaDB:

- [Popularity](#popularity)
- [License](#license)
- [Features where MySQL Shines](#features-where-mysql-shines)
- [Features where MariaDB Shines](#features-where-mariadb-shines)

_Unless otherwise specified, the comparison below is between the latest stable release, MySQL 8.0 vs. MariaDB 11.7_.

## Popularity

MySQL as the world's most popular open source database, is still overwhelmingly more popular than MariaDB.

![db-engines](/content/blog/mysql-vs-mariadb/db-engines.webp)
![google-trends](/content/blog/mysql-vs-mariadb/google-trends.webp)

MariaDB itself is more popular in Europe than the rest of the world.

![google-trends](/content/blog/mysql-vs-mariadb/google-trends-region.webp)

## License

MySQL employs dual licensing model:

- **GPL (General Public License) Version 2**: MySQL’s open-source version is licensed under the GNU GPL v2. This means that if you use MySQL in a project that is also open-source and licensed under the GPL, you can use MySQL without purchasing a commercial license.

- **Proprietary License**: If you want to embed or distribute MySQL in a closed-source or proprietary product, you need to obtain a commercial license from Oracle, the company that owns MySQL. This is often used by companies that don't want to comply with the GPL terms.

MariaDB is licensed under GPLv2. It remains strictly open-source with no proprietary version.

It may seem at first glance that MariaDB is more "strict" because it lacks the commercial licensing option that MySQL offers, but in reality, the interpretation is a bit different.

- MySQL offers more commercial flexibility by providing a paid option for proprietary use, making it more appealing to companies wanting to develop closed-source software while avoiding the GPL’s obligations.
- MariaDB, on the other hand, is more open-source focused and does not offer a proprietary version, which can be seen as either more "strict" or more "free" depending on your perspective. MariaDB prioritizes openness and community control, ensuring that its code and features remain accessible to all without the possibility of being locked behind a commercial paywall.

## Features where MySQL Shines

### Native Data Dictionary

MySQL 8.0 introduced a Native Data Dictionary, which is used to store metadata (like table definitions and schema information) in transactional tables within the InnoDB engine. This eliminates the need for traditional `.frm` files and improves the management of metadata, especially during upgrades and changes.

MariaDB still relies on the older `.frm` file-based system for table definitions and metadata, lacking this modern approach.

### JSON

MySQL has developed robust, native JSON support starting with MySQL 5.7. This includes JSON data types, JSON functions, and indexing mechanisms to efficiently store, query, and manipulate JSON data.

MariaDB can store JSON data using standard text fields or its dynamic columns feature, but doesn't support MySQL's `->` or `->>` syntax. MariaDB was original behind in JSON functions on offer, but has since offering a comparable set of functions.

### InnoDB Cluster and Group Replication:

MySQL has implemented InnoDB Cluster with Group Replication for high availability and fault tolerance. Group Replication allows for distributed, multi-master replication with automatic failover, making it easier to set up highly available, self-healing clusters.

MariaDB has its own replication features like Galera Cluster, but it does not have the same native, tightly integrated Group Replication feature that MySQL offers, especially for InnoDB.

### Ecosystem

Due to the wide adoption, all major cloud database providers support MySQL.

| Cloud Provider | MySQL | MariaDB |
| -------------- | ----- | ------- |
| AWS | ✅ | ✅ |
| Azure | ✅ | ✅ |
| GCP | ✅ | ❌ |
| Alibaba Cloud | ✅ | ✅ |
| DigitalOcean | ✅ | ❌ |
| Aiven | ✅ | ❌ |

Also database tools prioritize MySQL support. Take online scheme migration tool as an example. While
[pt-online-schema-change](https://docs.percona.com/percona-toolkit/pt-online-schema-change.html) supports
both MySQL and MariaDB, [gh-ost](https://github.com/github/gh-ost) doesn't support MariaDB [officially](https://github.com/github/gh-ost/issues/725).

## Features where MariaDB Shines

### Storage Engines

Though MySQL has a pluggable storage engine architecture, InnoDB is the only viable choice. For MaiaDB, besides the default InnoDB, it also has:

- **Aria**: A crash-safe alternative to MyISAM that offers better performance in read-heavy workloads.
- **MyRocks**: A storage engine optimized for SSDs, providing better compression and write optimization, particularly for large datasets.

### System-versioned Tables (Temporal Tables)

MariaDB introduced system-versioned tables (often referred to as temporal tables) in MariaDB 10.3. This feature allows you to track the historical changes of data in a table automatically by keeping a history of row versions over time. It’s particularly useful for auditing, data recovery, and version control of data in relational databases.

### Flashback

MariaDB supports a flashback feature using temporal tables, which allows users to revert tables to a previous state or view historical data without needing to implement complex backup and recovery mechanisms.

### Thread Pooling

MariaDB offers thread pooling as part of its open-source distribution, which helps in managing large numbers of concurrent connections by reducing the overhead of spawning and managing multiple threads. This improves performance in high-concurrency environments.

MySQL offers thread pooling, but it is only available in the Enterprise Edition, not the community version.

### Oracle Compatibility

MariaDB is more Oracle-compatible than MySQL due to the introduction of some Oracle-like features:

- Sequences: MariaDB supports sequence objects, which are more in line with Oracle's sequences (MySQL relies on auto-increment columns).

- PL/SQL Syntax: MariaDB has added PL/SQL-like syntax as of MariaDB 10.3. This allows for easier migration of Oracle’s PL/SQL code into MariaDB without significant changes.

### CREATE OR REPLACE PROCEDURE

MariaDB supports CREATE OR REPLACE for stored procedures starting from MariaDB 10.1.

MySQL does not support CREATE OR REPLACE for stored procedures. In MySQL, you need to first DROP the procedure before creating a new one. This is not ideal:

- **Temporary Loss of Functionality**: During the time the procedure is dropped, it is no longer available for use by applications or queries. This can lead to errors or downtime in production environments, especially if the procedure is accessed frequently.
- **Loss of Permission**: When you DROP a stored procedure, any specific permissions or grants (such as GRANT EXECUTE) associated with that procedure are removed. After recreating the procedure, you must manually reassign those permissions.

### Dynamic Columns

MariaDB has a unique Dynamic Columns feature, allowing you to store non-relational data (such as JSON-like structures) in a relational table without needing to define the structure beforehand. This enables MariaDB to handle flexible data types while still using a relational schema.

MySQL offers JSON data type support (which MariaDB lacks), but Dynamic Columns provide a different level of flexibility for users who prefer not to use JSON but still need semi-structured data.

## MariaDB Road Ahead

The ill-fated IPO led to MariaDB's acquisition by K1, a private equity firm likely less interested in the intricacies of the database business and more focused on seizing an opportunity. Viewing the troubled MariaDB as a bargain, K1 swooped in with plans to restructure the company, stabilize it, and eventually will sell it off to another buyer.

On the other hand, the open-source MariaDB project still carries the torch passed down by its creator, Michael 'Monty' Widenius. It was born from a desire to keep the MySQL spirit alive after concerns arose about Oracle’s control. With a strong focus on openness and being driven by the community, MariaDB still has a fighting chance. Who knows? Maybe, like the PostgreSQL community, MariaDB’s supporters will rally, grow stronger, and create something even more impactful in the years to come.

---

It's also possible that MySQL and MariaDB co-exist inside an organization. And if you want to manage the database development lifecycle for both of them, please check out [Bytebase](/).

![change-query-secure-govern-database-all-in-one](/content/blog/_shared/db-scheme-lg.png)

## Other Comparisons

- [MySQL vs. Postgres](/blog/postgres-vs-mysql)
- [Postgres vs. MongoDB](/blog/postgres-vs-mongodb)
- [PlanetScale vs. Neon](/blog/planetscale-vs-neon)
- [Neon vs. Supabase](/blog/neon-vs-supabase)

<HintBlock type="info">

This post is maintained by Bytebase, an open-source database DevSecOps tool that can manage both Neon and Supabase (it's PG after all). We update the post every year.

</HintBlock>

| Update History | Comment |
| -------------- | ---------------------------------- |
| 2024/07/02 | Initial version. |
| 2025/04/28 | Updated for 2025. Improved pricing |
| 2025/05/19 | Add agentic workload |
| 2026/03/25 | Updated for 2026. Pricing, Neon acquisition, features |

**Neon is a serverless Postgres database; Supabase is a backend-as-a-service platform built on Postgres.** Choose Neon if you want a standalone, scale-to-zero Postgres with instant branching. Choose Supabase if you want a full-stack platform with auth, storage, realtime, and edge functions included alongside your database.

Besides the typical Postgres service providers like AWS RDS, Google Cloud SQL, DigitalOcean Managed Databases, [Neon](https://neon.com/) and [Supabase](https://supabase.com/) are the two modern Postgres service providers.

<HintBlock type="info">

Databricks [completed its acquisition](https://www.databricks.com/blog/databricks-neon) of Neon in 2025 for ~$1 billion, making Neon the Postgres foundation for Databricks' agentic AI platform. Neon continues to operate as an independent product with its own pricing and brand (now at neon.com).

</HintBlock>

On the surface, Neon and Supabase are different products. Neon is a database service. Supabase is a BaaS (Backend-as-a-Service) and Postgres is one of its included services.

They are comparable because they both offer a **developer-friendly**, **scalable** Postgres service. In the agentic era, each aims to become the de-facto database for agentic workloads.

![reddit](/content/blog/neon-vs-supabase/reddit.webp)

And their websites certainly don't help the choice easier.

![neon-website](/content/blog/neon-vs-supabase/neon-site.webp)

![supabase-website](/content/blog/neon-vs-supabase/supabase-site.webp)

At Bytebase, we are Postgres fans. Our founders build [Google Cloud SQL for PostgreSQL](https://cloud.google.com/sql) and Bytebase
also chooses Postgres to store its own metadata. Below we compare Neon and Supabase from
the following dimensions:

- [Architecture](#architecture)
- [Compatibility](#compatibility)
- [Branching](#branching)
- [Agentic Workload](#agentic-workload)
- [Integration](#integration)
- [Compliance](#compliance)
- [Open Source](#open-source)
- [Pricing](#pricing)
- [FAQ](#faq)

## Architecture

Neon is a shared-storage architecture. It separates the compute and storage. The compute part is
just normal Postgres server, the storage part is a custom-built multi-tenant storage system shared
by all Postgres compute nodes.

![neon-arch](/content/blog/neon-vs-supabase/neon-arch.webp)

Supabase is a battery-included Postgres platform. It uses vanilla Postgres as the core and augments the
database with various middlewares.

![supabase-arch](/content/blog/neon-vs-supabase/supabase-arch.webp)

## Compatibility

Neon is mostly compatible with vanilla Postgres whereas Supabase is a dedicated vanilla Postgres instance. They both bear the limitations of
a hosted database service (e.g. no superuser).

- [Neon compatibility](https://neon.com/docs/reference/compatibility)
- [Supabase compatibility](https://supabase.com/docs/guides/database/postgres/roles-superuser)

## Branching

Both Neon and Supabase are targeting developers, and they both offer a branching feature.

Neon purpose-built paging layer has copy-on-write (CoW), which enables database cloning instantaneous and cost-effective.

![neon-branch](/content/blog/neon-vs-supabase/neon-branch.webp)

Supabase branching integrates with Git repository. It provisions a new empty database, runs the migration script and initializes the seed data.

Overall, Neon's instant branch cloning is closer to the Git semantics.

## Agentic Workload

Both Neon and Supabase highlight AI agents as a primary use case.

![neon-agent](/content/blog/neon-vs-supabase/neon-ai-agent.webp)
![supabase-agent](/content/blog/neon-vs-supabase/supabase-ai-agent.webp)

The Databricks acquisition confirmed that Neon’s architecture — instant provisioning, scale-to-zero, and per-agent database branching — is purpose-built for agentic workloads.

![neon-4x-agent-db](/content/blog/neon-vs-supabase/neon-4x-agent-db.webp)

Neon now offers a unified `neon init` command that configures both its MCP Server and VS Code Extension in one step, giving AI coding assistants like Cursor direct database access. Neon Auth also branches automatically alongside database branches, so preview environments get isolated auth state.

Supabase is responding with its own agentic play. BKND joined Supabase to build a Lite offering specifically for agentic workloads. AI app builders such as [Lovable](https://lovable.dev/) and [bolt](https://bolt.new/) continue to adopt Supabase as their default backend.

Overall, Neon’s scale-to-zero and instant branching make it better suited for agents that spin up many short-lived databases. Supabase’s full-stack platform is a better fit for AI app builders who need auth, storage, and realtime alongside the database.

## Integration

Neon integrates with application platforms such as Vercel to provide a high-fidelity preview environment. The Vercel integration now automatically provisions Neon Auth on preview branches, so authentication works out of the box in preview deployments. Neon is also now part of the Databricks ecosystem, opening up integrations with Databricks' data and AI tools.

Supabase is an application platform by itself and boasts a wide variety of integrations.

![supabase-integration](/content/blog/neon-vs-supabase/supabase-integration.webp)

Recent additions include a one-click Stripe Sync Engine integration (query customers, subscriptions, and invoices with SQL) and the Hydra-powered Supabase Warehouse for analytics workloads. There are also quite a few SaaS boilerplates built on Supabase.

## Compliance

Both Neon and Supabase achieve SOC 2 Type 2. Both now offer HIPAA compliance — Neon added HIPAA eligibility on its Scale plan after the Databricks acquisition.

- [Neon security](https://neon.com/docs/security/security-overview)
- [Supabase security](https://supabase.com/security)

## Open Source

Neon [open sources](https://github.com/neondatabase/neon) its entire database codebase under Apache-2.0 license.

Supabase also [open sources](https://github.com/supabase/supabase) its entire platform under Apache-2.0 license.

Supabase is one of the most popular repositories on GitHub, while Neon's star growth is also impressive.

[![neon-vs-supabase](/content/blog/neon-vs-supabase/supabase-vs-neon.webp)](https://star-history.com/#supabase/supabase&neondatabase/neon&Date)

## Pricing

Both Neon and Supabase offer a free tier and usage-based pricing, but their models differ significantly.

### Neon — Usage-Based (Pay for What You Use)

Post-Databricks acquisition, Neon slashed prices: storage dropped from $1.75 to **$0.35 per GB-month** and compute costs fell ~25%.

| Plan | Monthly Cost | Included Compute | Compute Rate | Storage | Key Limits |
| ---- | ------------ | ---------------- | ------------ | ------- | ---------- |
| Free | $0 | 100 CU-hours/project | N/A | 0.5 GB/project | Up to 2 CU, scale-to-zero |
| Launch | Usage-based (~$15 typical) | - | $0.106/CU-hour | $0.35/GB-month | Up to 16 CU, 100 projects |
| Scale | Usage-based (~$701 typical) | - | $0.222/CU-hour | $0.35/GB-month | Up to 56 CU, SLA, SOC 2, HIPAA |
| Business | Custom | Custom | Custom | Custom | Dedicated infra, premium support |

Neon's scale-to-zero means you pay nothing when the database is idle — a significant advantage for dev/test environments and agentic workloads that spin up many short-lived databases.

### Supabase — Platform Fee + Usage

Supabase charges a platform subscription fee plus usage-based overages.

| Plan | Monthly Cost | Database | Auth MAU | Storage | Key Features |
| ---- | ------------ | -------- | -------- | ------- | ------------ |
| Free | $0 | 500 MB | 50K | 1 GB | 2 projects, paused after 7 days idle |
| Pro | $25 + usage | 8 GB included | 100K | 100 GB | $10 compute credit, spend cap |
| Team | $599 + usage | 8 GB included | 100K | 100 GB | SSO, SOC 2, 28-day logs |
| Enterprise | Custom | Custom | Custom | Custom | SLA, 24/7 support, BYO cloud |

Supabase's pricing includes auth, storage, edge functions, and realtime — all bundled into the platform fee. If you need these services, the value proposition is strong compared to assembling them separately.

## Neon or Supabase

If you want a **standalone Postgres database** with serverless scaling, instant branching, and scale-to-zero, Neon is the stronger choice. It is especially well-suited for agentic workloads and development workflows that benefit from lightweight, ephemeral databases. The Databricks acquisition gives it additional backing for long-term investment.

If you are building a **full-stack application** and want auth, file storage, realtime subscriptions, and edge functions included alongside your database, Supabase provides a more complete platform. Its dashboard, growing integration ecosystem, and active community make it a productive choice for shipping quickly.

## FAQ

### Is Neon still independent after the Databricks acquisition?

Yes. Neon continues to operate as an independent product with its own pricing, brand, and roadmap. It is now available at [neon.com](https://neon.com/) (previously neon.tech).

### Can I self-host Neon or Supabase?

Both are open source under Apache 2.0. [Neon](https://github.com/neondatabase/neon) and [Supabase](https://github.com/supabase/supabase) can be self-hosted, though the managed services include features (HA, backups, monitoring) that require additional setup when self-hosting.

### Which is cheaper for a small project?

Both offer generous free tiers. Neon's free plan includes 100 CU-hours and 0.5 GB storage per project. Supabase's free plan includes 500 MB database, 50K MAU, and 1 GB file storage. For hobby projects, both are effectively free. As you scale, Neon's usage-based model can be cheaper if your workload is bursty (scale-to-zero saves cost during idle periods), while Supabase's bundled platform can be cheaper if you would otherwise pay separately for auth, storage, and realtime.

### Which has better performance?

Both run vanilla Postgres, so raw query performance is comparable. Neon's shared-storage architecture adds a small latency overhead on cold starts but enables instant branching and scale-to-zero. Supabase runs dedicated Postgres instances with no cold-start penalty. For latency-sensitive production workloads, Supabase's always-on compute may have a slight edge; for workloads with variable traffic, Neon's autoscaling can be more efficient.

## Other Comparisons

- [PlanetScale vs. Neon](/blog/planetscale-vs-neon)
- [Postgres vs. MySQL](/blog/postgres-vs-mysql)
- [Postgres vs. MongoDB](/blog/postgres-vs-mongodb)
- [MySQL vs. MariaDB](/blog/mysql-vs-mariadb)

<HintBlock type="info">

This post is maintained by Bytebase, an open-source database DevSecOps tool that is a Liquibase/Flyway alternative. We update the post every year.

</HintBlock>

| Update History | Comment |
| -------------- | ------------------------------------------------------- |
| 2023/06/27 | Initial version. |
| 2025/05/23 | Update for 2025. |
| 2026/02/03 | Flyway 2026, Liquibase 5.0, pricing/licensing changes. |

When looking for a database CI/CD and schema migration change tool, Flyway and Liquibase are two common options. Understanding the differences between these two tools can help potential users choose the one that best meets their needs.

Both are popular Liquibase alternatives and Flyway alternatives to each other — this guide breaks down every aspect to help you decide which database migration tool is right for your team.

## What Flyway and Liquibase have in common

- Java-based, providing Java SDK and CLI.
- Provide open-source community version and closed-source commercial version.
- Native SQL supported, however Liquibase requires XML changelog to define the changes.
- Database CI/CD with version control system #GitOps (see [Database GitOps configuration](#database-gitops-configuration)).
- Auto SQL check (see [Auto SQL check](#sql-auto-check)).
- Schema Synchronization (see [Sync schema](#sync-schema)).
- Tiered pricing, offering both free and paid plan.
- Open source, both has long history.
 ![star-history](/content/blog/flyway-vs-liquibase/star-history-flyway-vs-liquibase.webp)

## What are the differences between Flyway and Liquibase?

While both Flyway and Liquibase are tools for database CI/CD, there are some key differences between the two. The following table summarizes the differences between Flyway and Liquibase.

| | Flyway | Liquibase |
| ------------------------------------------------------ | ------------------------------- | -------------------------------------- |
| [Product position](#product-position) | Schema Change & Version Control | Schema Change & Version Control |
| [Developer interface](#developer-interface) | CLI + Flyway Desktop | CLI |
| [Supported databases](#supported-databases) | 22 Only SQL + MongoDB (preview) | 50 SQL & NoSQL DB |
| [Programming language and installation](#installation) | Java + JVM | Java + JVM |
| [Change execution](#change-execution) | SQL script + CLI | Changelog (SQL, XML, JSON, YAML) + CLI |
| [Change orchestration](#change-orchestration) | Numbering of SQL files | Changelog + Flow |
| [Database GitOps](#database-gitops-configuration) | ✅ | ✅ |
| [SQL auto check](#sql-auto-check) | ✅ | ✅ |
| [Change history](#change-history) | ✅ | ✅ |
| [Sync schema](#sync-schema) | ✅ | ✅ |
| [Rollback](#rollback) | ✅ | ✅ |
| [Schema drift detection](#schema-drift-detection) | ✅ | ✅ |

### Product position

- **Flyway**: A database schema change and version control tool.

- **Liquibase**: A database schema change and version control tool. It helps you track, manage, and automate changes to your database.
 ![liquibase-position](/content/blog/flyway-vs-liquibase/liquibase-position.webp)

### Developer interface

- **Flyway**: A command-line tool with Java API, Maven plugin, and Gradle plugin. Flyway Desktop has been enhanced with full Schema Model view and Git history tracking, available for SQL Server, PostgreSQL, MySQL, and other supported databases.

- **Liquibase**: Primarily a command-line tool. The Liquibase Hub GUI was sunset in May 2023, with focus shifting to CLI and integration capabilities.

### Supported databases

- **Flyway**: 22+ SQL databases with support for:

 - MongoDB (Native Connectors mode only; JDBC support removed in 2025)
 - SinglestoreDB
 - Google Cloud Spanner
 - EnterpriseDB
 - TimescaleDB
 - Postgres 18, SQL Server 2025, Oracle 26ai (added 2025)
 - Full compatibility for all databases in Community version

- **Liquibase**: 50+ SQL and NoSQL databases with enhanced support for:
 - Databricks (added 2025)
 - Google BigQuery (including DATABASECHANGELOGHISTORY table support)
 - Continued MongoDB support
 - Snowflake with OAuth/PKI authentication

### Installation

- **Flyway**: Java-based tool requiring JVM installation. In 2025, configuration has moved to unified flyway.toml format, deprecating the older JSON format.

- **Liquibase**: Java-based tool requiring JVM installation. **Liquibase 5.0+ requires Java 17 minimum** (Java 8/11 deprecated). MacOS .dmg installer was deprecated in favor of Homebrew installation.

### Change execution

- **Flyway**: CLI or GitOps. Users write SQL files and then run command `flyway migrate`. In 2025, enhanced capabilities for script migrations and callbacks have been added to the Community version.

- **Liquibase**: CLI or GitOps. Users specify the changes by defining a `changelog` and then run a command. In 2025, flow enhancements provide more sophisticated orchestration capabilities.

![liquibase-changelog](/content/blog/flyway-vs-liquibase/liquibase-changelog.webp)
![liquibase-update](/content/blog/flyway-vs-liquibase/liquibase-update.webp)

### Change orchestration

- **Flyway**: Number the SQL files in the order you want them to be executed.

 ![liquibase-change-order](/content/blog/flyway-vs-liquibase/flyway-change-order.webp)

- **Liquibase**: Specify the order of changes in the changelog file. In 2025, Flow capabilities have been enhanced with conditionals and more advanced orchestration features.

 ![liquibase-change-order](/content/blog/flyway-vs-liquibase/liquibase-change-order.webp)

Liquibase continues to be more flexible as it can specify arbitrary orders, and the enhanced [flow file](https://docs.liquibase.com/liquibase-pro/flow/home.html) capabilities in 2025 make orchestrating complex steps even more powerful.

### Database GitOps configuration

- **Flyway**: Configure with VCS CI/CD workflow manually. In 2025, integration with Test Data Manager and Docker for dedicated development databases improves the GitOps workflow.

- **Liquibase**: Configure with VCS CI/CD workflow manually. In 2025, enhanced validation and error messaging improve the GitOps experience.

 ![liquibase-gitlab-gitops](/content/blog/flyway-vs-liquibase/liquibase-gitlab-gitops.webp)

### SQL auto check

SQL auto check helps developers write less buggy SQL and save DBAs manual review efforts.

- **Flyway**: Code Analysis with enhanced drift detection in 2025
- **Liquibase**: SQL Quality check with improved Policy Checks in 2025

#### Supported plan

- **Flyway**: Enterprise only (Teams discontinued in 2025)
- **Liquibase**: Paid tiers only (Starter and above)

#### Number of rules

- **Flyway**: 10+ general rules or integrate SQLFluff for more rules
- **Liquibase**: Enhanced policy checks with more comprehensive validation in 2025

#### How to configure

- **Flyway**: Predefined, users may set them active or not. In 2025, improved configuration through unified flyway.toml.

- **Liquibase**: Predefined, users may set levels while calling. Python-based Custom Policy Checks available in paid tiers.

 ![liquibase-quality-check](/content/blog/flyway-vs-liquibase/liquibase-quality-check.webp)

#### How to trigger

- **Flyway**: Run `flyway check -code ....` command to produce a report.

- **Liquibase**: Run `check` command or manually integrate this command in automation. Users may go to admin console to view logs.
 ![liquibase-validate-result](/content/blog/flyway-vs-liquibase/liquibase-validate-result.webp)

### Change history

- **Flyway**: Run `flyway info` to show `flyway_schema_history` table. In 2025, enhanced info filters provide more granular control.

- **Liquibase**: Simple Database Change Logs. In 2025, new parameters for the `history` command and improved Operation Reports.

 ![liquibase-logs](/content/blog/flyway-vs-liquibase/liquibase-logs.webp)

### Sync schema

- **Flyway**: Via Flyway Desktop, generate a migration script to bring the target database schema in sync. In 2025, the new backup-based baseline approach allows restoring a backup file instead of creating a database from scratch in the baseline script.

- **Liquibase**: Support `diff-changelog` to compare databases and create a deployable changelog to sync. In 2025, enhanced Drift Report capabilities improve schema synchronization.

### Rollback

- **Flyway**: Write rollback scripts manually. Enterprise only (Community does not support rollback).

- **Liquibase**: Support `rollback-one-changeset` or `rollback`. Enhanced Rollback Report in paid tiers.

### Schema drift detection

- **Flyway**: Run `flyway check -drift ...` to produce a report indicating difference between target database and the one created by the migrations applied by Flyway. Enhanced in 2025 with more comprehensive code analysis.

- **Liquibase**: Run `liquibase diff --format=json` to produce a report indicating difference between target database and source database. In 2025, improved Drift Report capabilities with better visualization.

## Flyway enhancements in 2025-2026

Key features released in [2025](https://www.red-gate.com/blog/redgate-flyway-2025-year-in-review):

- **State-based deployments** - Work the way you want with declarative schema definitions
- **Backups as baseline** - Use database backups instead of lengthy baseline scripts, solving issues with invalid objects or external server references
- **Improved drift detection** - Snapshots and drift resolution scripts to identify and resolve schema drift
- **Database support** - Added support for Postgres 18, SQL Server 2025, Oracle 26ai

[January 2026 updates](https://www.red-gate.com/blog/redgate-flyway-product-updates-january-2026):

- **AI-powered migration descriptions** - Flyway Desktop 8 generates descriptions based on migration script contents
- **New shadow database dialog** - Option to let Flyway create shadow databases automatically
- **Schema model view** - View all object definitions in Flyway Desktop
- **SQLFluff 4.0.0** - Upgraded from 3.5.0

**Important licensing change**: Since May 2025, Flyway only offers Community (free) and Enterprise (paid). The Teams tier was discontinued for new customers.

## Liquibase enhancements in 2025-2026

[Liquibase 5.0](https://docs.liquibase.com/community/release-notes/5-0) was released in September 2025 as a major release:

- **Java 17 minimum requirement** - Java 8 and 11 deprecated for improved performance and security
- **Liquibase Secure 5.0** - Certified enterprise distribution with integrated extensions, drivers, and policies
- **Liquibase Secure Developer** - New VS Code extension for enterprise users
- **AI Changelog Generator** - MCP Server powered tool to convert natural language to production-ready changelogs (Private Preview)
- **Databricks support** - Added database connection support

Flow and automation enhancements:

- **Conditional Flow File logic** - Based on action exit codes with new `continueOnError` property
- **Snowflake OAuth/PKI authentication** - Simplified authentication for Snowflake's token-based requirements
- **New policy check** - `FormattedSqlHeaderRequired` ensures proper SQL file formatting
- **Python checks enhancements** - New `get_config_value()` helper function

**Important licensing change**: Liquibase Community has adopted the [Functional Source License (FSL)](https://docs.liquibase.com/community/release-notes/5-0) to ensure long-term sustainability while keeping the software free for developers.

## Pricing

Flyway uses a per-user licensing model, while Liquibase uses a per-application/database-type licensing model.

| Aspect | Flyway | Liquibase |
| -------------------- | --------------------------------- | ------------------------------------------------ |
| **Free Tier** | Community | Community (FSL license) |
| **Paid Tiers** | Enterprise only | 4 tiers: Starter, Growth, Business, Enterprise |
| **Licensing Model** | Per-user | Per-application + database types |
| **Entry-Level** | Enterprise (contact for quote) | Starter: up to 5 apps, 1 DB type |
| **Mid-Tier** | — | Growth (10 apps, 3 DB types), Business (25 apps) |
| **Enterprise** | Unlimited (custom pricing) | Unlimited apps & DB types, 24/7 support |
| **Pricing** | Contact sales | Contact sales (all tiers) |

## Summary

Both Flyway and Liquibase are Java-based, offering with SDKs and CLI support, and follow a [migration-based](/blog/database-version-control-state-based-vs-migration-based/#migration-based-version-control-imperative) approach to schema changes.
They also leverage the same open-source monetization strategy. While Flyway is generally considered more developer-friendly, Liquibase offers a broader set of advanced features.

The key difference is that Liquibase introduces the Changelog and Flow concepts, enabling users to specify explicit migration ordering, preconditions, labels, and contexts. In contrast, Flyway determines migration order based solely on file naming conventions.

Both tools offer a Git-like experience for database migrations. However, if you're seeking a GitHub/GitLab-style experience with a user-friendly GUI and team collaboration features, consider our own Bytebase. Continue reading for a side-by-side comparison with each tool:

- [Bytebase vs. Liquibase](/blog/bytebase-vs-liquibase/)
- [Bytebase vs. Flyway](/blog/bytebase-vs-flyway/)

## FAQ

### Is Liquibase free?

Liquibase Community Edition is free under the Functional Source License (FSL). Paid tiers (Starter, Growth, Business, Enterprise) unlock additional features like policy checks and advanced orchestration.

### Is Flyway free?

Flyway Community Edition is free and open-source. Since May 2025, the only paid option is Flyway Enterprise (the Teams tier was discontinued).

### Which is better, Flyway or Liquibase?

It depends on your needs. Flyway is simpler and more developer-friendly with SQL-first approach. Liquibase offers more flexibility with its Changelog and Flow concepts, broader database support (50+ vs 22+), and more granular migration ordering. For teams needing a GUI and collaboration features, consider a tool like [Bytebase](/).

### What are alternatives to Liquibase and Flyway?

Popular alternatives include [Bytebase](/), Atlas, SchemaHero, and Skeema. Bytebase offers a web-based GUI with built-in SQL review, GitOps integration, and team collaboration features that both Flyway and Liquibase lack.

### Does Flyway support rollback?

Flyway supports rollback only in the Enterprise edition. You must write rollback scripts manually. Liquibase supports rollback in paid tiers with automatic rollback capabilities.


> CloudBeaver is a web-based SQL client. Bytebase is a database DevSecOps platform.

CloudBeaver and Bytebase both run server-side and render in a browser, which is where the similarity ends. CloudBeaver is the web-native descendant of DBeaver — a lightweight SQL client that a team can share through a browser instead of installing desktop software. Bytebase is a database DevSecOps platform built around change review, access control, data masking, and audit logging. If you want a shared web GUI for everyday querying and editing, CloudBeaver fits. If you need review workflows, granular permissions, masking, and audit trails, that's Bytebase.

## What Bytebase and CloudBeaver have in common

- Server-side, web-based GUI — query, browse, export, edit.
- Tiered pricing with free and paid plans.
- AWS Marketplace listings for easy procurement.
- Support for a range of SQL and NoSQL databases.
- Open source — CloudBeaver started slightly earlier; Bytebase's star growth has outpaced it since 2023.
  ![star-history](/content/blog/bytebase-vs-cloudbeaver/star-history.webp)

## Key Differences Between Bytebase and CloudBeaver

Bytebase is built around team workflow — every database change flows through review, approval, and audited rollout. CloudBeaver is built around shared data access — one browser-based client that a team can log into, with lightweight team permissions and a query history log.

|                                             | CloudBeaver                                                  | Bytebase                                              |
| ------------------------------------------- | ------------------------------------------------------------ | ----------------------------------------------------- |
| [Product position](#product-position)       | Web-based SQL client (DBeaver's cloud sibling)               | Database DevSecOps platform                           |
| [Developer interface](#developer-interface) | Server-side, web-based                                       | Server-side, web-based                                |
| [Installation](#installation)               | Java-based, requires JVM (or Docker)                         | Go binary, no external dependency                     |
| [Supported databases](#supported-databases) | CE: ~20; Enterprise: 80+                                     | 23 with deep integration                              |
| [Query](#query)                             | ✅                                                           | ✅                                                    |
| [Change](#change)                           | Direct                                                       | Review workflow + configurable direct / Admin mode    |
| [SQL review rules](#change)                 | ❌                                                           | ✅ 200+ rules (free)                                  |
| [Access control](#access-control)           | Basic team/user groups + per-connection restrictions          | Workspace/project roles (free); database/table-level (Enterprise) |
| [Data masking](#data-masking)               | ❌                                                           | ✅ Enterprise (dynamic, column-level)                 |
| [Compare schema](#compare-schema)           | ❌                                                           | ✅ 1:N (free)                                         |
| [Audit log](#audit-log)                     | ✅ Query Manager (paid)                                      | ✅ Pro                                                |
| [API](#api)                                 | GraphQL                                                       | gRPC + HTTP/REST + Terraform provider                |
| [GitOps](#gitops)                           | Not built in                                                  | ✅ GitHub, GitLab, Bitbucket, Azure DevOps           |

### Product position

- **CloudBeaver**: A lightweight web SQL client — DBeaver's browser-based cousin. It ships a shared web GUI for querying, editing, and browsing databases. Collaborative features like shared connections and team permissions have grown over time, but the product's center of gravity is still "one SQL client that a team can share in a browser."
  ![cb-position](/content/blog/bytebase-vs-cloudbeaver/cb-position.webp)

- **Bytebase**: A database DevSecOps platform built around team workflow — every change flows through review, approval, and audited rollout. SQL Editor is one surface; the rest is governance (access control, data masking, audit log, approval flows, GitOps).
  ![bb-3-op](/content/blog/bytebase-vs-cloudbeaver/bb-3-op.webp)

### Developer interface

- **CloudBeaver**: Web GUI focused on query, edit, and schema browsing.
  ![cb-gui](/content/blog/bytebase-vs-cloudbeaver/cb-ui.webp)

- **Bytebase**: Web GUI with an issue-based change workflow plus a SQL Editor for querying. Also exposes an [API](https://api.bytebase.com/), a [Terraform provider](https://docs.bytebase.com/integrations/terraform/overview), and [GitOps](https://docs.bytebase.com/gitops/overview) for teams that prefer automation.
  ![bb-issue](/content/blog/bytebase-vs-cloudbeaver/bb-issue-waiting.webp)
  ![bb-gui](/content/blog/bytebase-vs-cloudbeaver/bb-gui.webp)

### Installation

- **CloudBeaver**: Java-based, runs on a JVM or via Docker.

- **Bytebase**: Single Go binary, no external dependencies. Also ships as a [Docker image](https://docs.bytebase.com/get-started/deploy-with-docker) and [Helm chart for Kubernetes](https://docs.bytebase.com/get-started/deploy-with-kubernetes).

### Supported databases

- **CloudBeaver**: The **Community Edition** covers ~20 common databases (MySQL, PostgreSQL, Oracle, SQLite, SQL Server, MariaDB, and similar). **CloudBeaver Enterprise** adds 80+ engines including NoSQL (MongoDB, Cassandra, Redis) and cloud warehouses (Redshift, BigQuery, Snowflake).

- **Bytebase**: 25 engines with deep integration — 9 RDBMS (MySQL, PostgreSQL, Oracle, SQL Server, MariaDB, TiDB, OceanBase, CockroachDB, Spanner), 6 NoSQL (MongoDB, Redis, Cassandra, DocumentDB, DynamoDB, Cosmos DB), 9 data warehouses (Snowflake, BigQuery, Redshift, Hive, ClickHouse, Databricks, StarRocks, Doris, Trino), and Elasticsearch. Each engine gets engine-specific SQL review rules, schema sync, and masking support.

### Query

- **CloudBeaver**: Query by double-clicking a table or writing SQL with templates and autocomplete. **Enterprise** adds a visual query builder for point-and-click query construction.
  ![cb-query-sql](/content/blog/bytebase-vs-cloudbeaver/cb-query-sql.webp)

  Saved scripts can be shared alongside the connection they belong to.
  ![cb-script](/content/blog/bytebase-vs-cloudbeaver/cb-script.webp)

- **Bytebase**: The **SQL Editor** runs queries through the platform — double-click a table or write SQL with autocomplete.
  ![bb-data-query](/content/blog/bytebase-vs-cloudbeaver/bb-data-query.webp)

  Save or star scripts for quick access. On **Pro** and above, share scripts with your team or project.
  ![bb-sheets](/content/blog/bytebase-vs-cloudbeaver/bb-sheets.webp)

### Change

- **CloudBeaver**: Edit data directly in the result grid or run SQL manually. Changes hit the database immediately — no review, no approval flow. **Query Manager** logs executed statements for later inspection (paid tier).
  ![cb-change](/content/blog/bytebase-vs-cloudbeaver/cb-change.webp)

  ![cb-change-query-history](/content/blog/bytebase-vs-cloudbeaver/cb-change-query-history.webp)

- **Bytebase**: Changes flow through an **issue** — like a pull request for the database. Each issue runs automated [SQL review](https://docs.bytebase.com/sql-review/review-rules) (200+ rules), routes through a [custom approval flow](https://docs.bytebase.com/change-database/approval) if configured, and deploys with full history tracking. Admins can choose to allow direct change per environment or enforce review platform-wide.
  ![bb-issue](/content/blog/bytebase-vs-cloudbeaver/bb-issue-waiting.webp)

Every change is recorded in **Change History** with diff view and rollback scripts.
![bb-history](/content/blog/bytebase-vs-cloudbeaver/bb-history.webp)

Ad-hoc DDL (e.g. `ALTER TABLE` typed into SQL Editor) either creates a new issue or requires **Admin mode** — a deliberate escape hatch for break-glass scenarios, similar to SSH on a server.
![bb-force-issue](/content/blog/bytebase-vs-cloudbeaver/bb-force-issue.webp)
![bb-force-issue-preview](/content/blog/bytebase-vs-cloudbeaver/bb-force-issue-preview.webp)

For schema changes, most teams use the visual **Schema Editor** — edit columns, constraints, and indexes in the GUI and Bytebase generates the DDL automatically.

![bb-select-db](/content/blog/bytebase-vs-cloudbeaver/bb-select-db.webp)
![bb-schema-editor](/content/blog/bytebase-vs-cloudbeaver/bb-schema-editor.webp)

### Access Control

- **CloudBeaver**: Default teams are `admin` (admin panel access) and `user` (no admin access). You can create additional teams and restrict connection access per team or per user.
  ![cb-teams](/content/blog/bytebase-vs-cloudbeaver/cb-teams.webp)

  ![cb-db-access](/content/blog/bytebase-vs-cloudbeaver/cb-db-access.webp)

  The model is connection-scoped — if you can see a connection, you can see everything the connecting user can. No concept of permissions below the database level.

- **Bytebase**: Roles are scoped to **Workspace** and **Project** levels. `Workspace Admin` / `DBA` provision instances and manage members; `Project Owner` manages databases, issues, and roles within their project. Individuals can hold multiple project-level roles like `Project Developer` (change database) or `SQL Editor User` (query only).
  ![bb-access-control](/content/blog/bytebase-vs-cloudbeaver/bb-access-control.webp)

  On **Community**, users only see projects they're assigned to. On **Enterprise**, `Project Owner` can grant access down to specific databases or tables, and developers can request [Just-In-Time access](https://docs.bytebase.com/security/database-permission/overview) via a request issue that expires automatically.
  ![bb-access-control-adv](/content/blog/bytebase-vs-cloudbeaver/bb-access-control-adv.webp)

  Different environments can enforce different policies — e.g., read-only access to production, full access to development.
  ![bb-access-environment](/content/blog/bytebase-vs-cloudbeaver/bb-access-control-env.webp)

### Data Masking

- **CloudBeaver**: Not available. Query results return raw data.

- **Bytebase**: [Dynamic data masking](https://docs.bytebase.com/security/data-masking/overview) at the column level (Enterprise). `Admin` / `DBA` / `Project Owner` configure masking per column and authorize specific users to see the original data. Everyone else sees masked values in query results.

  Semantic types (email, phone, credit card, SSN) let you define reusable masking algorithms applied consistently across the schema.
  ![bb-masking-graph](/content/blog/bytebase-vs-cloudbeaver/bb-masking-graph.webp)

### Compare Schema

- **CloudBeaver**: Not available.

- **Bytebase**: **Schema Sync** is **1:N** (free) — pick one source database and compare against several targets at once. Results become a batch change issue that routes through SQL review, approval, and rollout.
  ![bb-schema-compare-several](/content/blog/bytebase-vs-cloudbeaver/bb-schema-compare-several.webp)

  ![bb-batch-issue](/content/blog/bytebase-vs-cloudbeaver/bb-batch-issue.webp)

### Audit log

- **CloudBeaver**: **Query Manager** (paid) logs executed SQL statements per connection.
  ![cb-audit-log](/content/blog/bytebase-vs-cloudbeaver/cb-audit-log.webp)

- **Bytebase**: [Audit Log](https://docs.bytebase.com/security/audit-log) (Pro and above) records every action in the platform — queries, DDL, approvals, policy changes. Filter by user, action type, or time range; export for SIEM ingestion.
  ![bb-audit-log](/content/blog/bytebase-vs-cloudbeaver/bb-audit-log-dropdown.webp)

### API

- **CloudBeaver**: [GraphQL API](https://github.com/dbeaver/cloudbeaver/wiki/Server-API-explorer). Documentation is sparse — mostly auto-generated schema reference.

- **Bytebase**: gRPC and HTTP/REST APIs with full [docs](https://docs.bytebase.com/api/overview/), [code samples](https://github.com/bytebase/example-api), and a [Terraform provider](https://docs.bytebase.com/integrations/terraform/overview).

### GitOps

- **CloudBeaver**: No built-in GitOps. You could roll your own via the GraphQL API, but there's no out-of-the-box workflow.

- **Bytebase**: [GitOps](https://docs.bytebase.com/gitops/overview) with GitHub, GitLab, Bitbucket, and Azure DevOps. SQL files committed to a repo create change issues in Bytebase automatically, with SQL review running as a merge check. See the [database-security example](https://github.com/bytebase/example-database-security) for a full GitOps setup.

### Pricing

- **CloudBeaver**: **Community Edition** is free and open source (AGPLv2). **CloudBeaver Team** and **Enterprise** are subscription-based with custom pricing — add NoSQL drivers, cloud warehouse support, Query Manager, SSO, and team management.

- **Bytebase**: [Community](https://www.bytebase.com/pricing/) is free, self-hosted, up to 20 users and 10 instances — includes SQL review, GitOps, multi-env rollouts. Pro is $20/user/month (cloud, up to 10 instances) and adds SSO, audit log, user groups. Enterprise is custom pricing, self-hosted or cloud, and unlocks dynamic data masking, custom approval flows, custom roles, SCIM, LDAP/OIDC SSO, and 2FA.

## When to Choose CloudBeaver

- You want a browser-based SQL client your team can share without installing desktop software.
- Your workflow is primarily query and edit — review, approval, and compliance aren't priorities.
- You need JDBC breadth for obscure or niche databases (Enterprise tier).
- You're already invested in the DBeaver ecosystem and want the web equivalent.

## When to Choose Bytebase

- You want a platform that covers change review, access control, data masking, and audit logging — not just a web SQL client.
- Developers write changes and DBAs (or platform engineers) review and deploy them.
- You need compliance artifacts — approval trails, audit logs, masking — for SOC 2, GDPR, or internal policy.
- You want database changes treated like code changes: reviewed, approved, traceable, and rolled out across environments.

## FAQ

### Can I use CloudBeaver and Bytebase together?

Yes. Teams sometimes use CloudBeaver as a general-purpose web SQL client for developers who need ad-hoc querying across many databases, and use Bytebase as the governance layer — any change that ships to production routes through Bytebase's review and approval pipeline.

### Isn't CloudBeaver just "DBeaver in a browser"?

Pretty much. CloudBeaver shares DBeaver's JDBC driver foundation and much of its UI language. The value over DBeaver is that it runs on a shared server and everyone logs in through a browser instead of installing a desktop client. The tradeoff is that some advanced desktop features (ER diagrams, data transfer wizards, complex query builders) are lighter or missing in CloudBeaver.

### Does Bytebase have everything CloudBeaver has?

For everyday team querying, yes — SQL Editor, schema browser, export, sharing. What Bytebase doesn't prioritize is JDBC breadth (25 engines vs CloudBeaver Enterprise's 80+). Bytebase goes deep on its supported engines with engine-specific review rules, schema sync, and masking support; CloudBeaver goes wide across more drivers.

### Is Bytebase Community actually usable for production?

Yes. Community includes the full GUI, 200+ SQL review rules, GitOps integration, multi-environment rollouts, and batch changes — same core workflow as the paid tiers. Limits: 20 users and 10 instances. You upgrade when you need SSO, audit log (Pro), or compliance features like data masking and custom approval flows (Enterprise).

---

Related comparisons:

- [Bytebase vs. DBeaver](/blog/bytebase-vs-dbeaver/)
- [Bytebase vs. DataGrip](/blog/bytebase-vs-datagrip/)
- [Top Open Source SQL Clients](/blog/top-open-source-sql-clients/)
- [Top DBeaver Alternatives](/blog/top-dbeaver-alternative/)



"Bytebase vs Metabase" is a common search, but the two tools sit in different product categories and solve different problems. Bytebase is a database DevSecOps platform for developers, DBAs, and platform teams. It handles schema changes, SQL review, access control, and data masking. Metabase is a business intelligence (BI) tool for data teams and business users. It turns SQL queries into dashboards and charts. This 2026 comparison covers where they actually overlap (web SQL editor, team permissions, self-hosting), where they differ (change workflow, BI visualization), and which one fits which role.

## Bytebase vs Metabase at a glance

|  | Metabase | Bytebase |
| --- | --- | --- |
| [Primary purpose](#primary-purpose) | Business intelligence (charts, dashboards) | Database change management + access control |
| [Primary users](#primary-purpose) | Data analysts, business users | Developers, DBAs, platform engineers |
| [Supported databases](#supported-databases) | 20+ (OLAP-leaning) | 20+ (OLTP-leaning, plus NoSQL) |
| [SQL editor](#query-experience) | ✅ (with visualization) | ✅ (SQL client feel, with schema browser) |
| [Dashboards / BI](#visualization-vs-schema-diagram) | ✅ Core product | ❌ Out of scope |
| [Schema change workflow](#change-workflow) | ❌ | ✅ Issue-based with approvals |
| [SQL Review (static + policy)](#sql-review) | ❌ | ✅ 200+ rules |
| [Data masking](#data-masking) | ❌ (row/column permissions only, Pro) | ✅ Dynamic masking with semantic types |
| [Audit log](#audit-log) | ✅ Pro/Enterprise | ✅ Pro/Enterprise |
| [Just-in-time access requests](#access-control) | ❌ | ✅ Enterprise |
| [Starting paid price](#pricing) | $100/mo flat (5 users) | $20/user/mo (Pro, cloud) |
| Open source | ✅ AGPL (fastest-growing BI) | ✅ MIT + Enterprise License |
| Self-host | ✅ | ✅ |

## Primary purpose

**Metabase** is a BI tool. Its core loop is: connect a database, ask a question (visual query builder or SQL), turn the result into a chart, pin the chart to a dashboard, share it with the team. Metabase 57 (Nov 2025) added Remote Sync for Git-tracked dashboard versioning, and Metabase 59 and 60 (Mar–Apr 2026) added AI SQL generation, Data Studio, and the Metabot AI assistant. Its audience is data analysts and business users who need to read the database, not change it.

**Bytebase** is a database DevSecOps platform. Its core loop is: register a database, developers submit change issues, SQL Review and custom approval gates run automatically, approved changes are applied and logged, and access to production data is gated by policy, masked, or granted just-in-time. Its audience is developers, DBAs, and platform teams who own the database lifecycle. For background on this category, see [what is database change management](/blog/what-is-database-change-management/).

![mb-position](/content/blog/bytebase-vs-metabase/mb-position.webp)

![bb-3-op](/content/blog/bytebase-vs-metabase/bb-3-op.webp)

In one line: Metabase reads and visualizes data; Bytebase governs and changes the database itself.

## Pricing

Pricing is one of the biggest practical differences. Metabase's paid tiers have a higher floor but bundle BI hosting. Bytebase's Pro tier is per-seat with a low entry point.

| Plan | Metabase | Bytebase |
| --- | --- | --- |
| Free / self-host Community | Open Source (self-host, unlimited users) | Free (self-host or cloud, up to 20 users, 10 instances) |
| Starter / entry paid | **Starter: $100/mo flat for 5 users**, then ~$6/user/mo monthly or $65/user/yr annual (cloud) | **Pro: $20/user/mo** (cloud only, unlimited users, SSO) |
| Mid tier | **Pro: $575/mo base**, then ~$12/user/mo monthly or $130/user/yr annual (cloud or self-host) | n/a |
| Enterprise | **Custom** (median ~$39k/yr, entry ~$20k/yr per Vendr data) | **Custom** (self-host or cloud, dynamic masking, custom approval, audit log, SCIM, SSO) |

Numbers above are from Metabase's public pricing page and Vendr's 2026 buyer data. Two things to watch:

- Metabase's $100/mo Starter cap of 5 users makes the per-user math misleading at small scale. A team of 10 on Starter is effectively $16/user/mo; on Pro, it's $57.50/user/mo before annual discounts.
- Bytebase Pro is cloud-only. Self-hosting drops you into Community (free but capped at 20 users / 10 instances) or Enterprise (custom). Most teams that self-host move straight to Enterprise.

## Supported databases

- **Metabase**: 20+ databases with a BI lean (BigQuery, Snowflake, Redshift, Athena, Druid, Presto, plus the usual OLTP engines: PostgreSQL, MySQL, SQL Server).
- **Bytebase**: 20+ databases with an OLTP lean (PostgreSQL, MySQL, Oracle, SQL Server, TiDB, Spanner, OceanBase), plus NoSQL (MongoDB, Redis) and analytics engines (ClickHouse, Snowflake, Redshift, BigQuery).

Both cover the common set. The tail is different because the products serve different workloads.

## Query experience

Both ship a web SQL editor, but the editor is doing different work in each product.

**Metabase** offers two entry points:

1. **Question builder**: a no-code interactive query for non-technical users. Drops to SQL when needed.
   ![mb-question](/content/blog/bytebase-vs-metabase/mb-question.webp)
2. **SQL query**: a raw SQL editor with schema browser. Results become charts and feed dashboards.
   ![mb-sql-query](/content/blog/bytebase-vs-metabase/mb-sql-query.webp)

**Bytebase SQL Editor** is closer to a traditional SQL client (think DBeaver or pgAdmin in the browser) with auto-completion, a schema browser, saved sheets, and a history pane. For a direct comparison against dedicated SQL clients, see [Bytebase vs DBeaver](/blog/bytebase-vs-dbeaver/) and [Bytebase vs CloudBeaver](/blog/bytebase-vs-cloudbeaver/), or the roundup at [top open source SQL clients](/blog/top-open-source-sql-clients/).

![bb-data-query](/content/blog/bytebase-vs-metabase/bb-data-query.webp)

The key difference: a Metabase query is meant to end in a chart. A Bytebase query is meant to end in verified data for a developer or an ad-hoc fix, and the editor enforces read-only or masking policies when the user opens it.

## Visualization vs schema diagram

- **Metabase**: visualization is the product. Line, bar, pie, map, pivot, funnel, and custom visualizations with dashboard embedding and AI-assisted chart suggestions in Metabase 60.
- **Bytebase**: no chart builder. The only visualization is the **Schema Diagram** for the database structure.
  ![bb-schema-diagram](/content/blog/bytebase-vs-metabase/bb-schema-diagram.webp)

If your team needs dashboards, Bytebase is not the tool. Pair it with Metabase.

## Access control

- **Metabase** organizes content into Collections with three permission levels (`Curate`, `View`, `No Access`) assignable to user groups. The Pro plan adds row- and column-level data permissions.
  ![mb-access-control](/content/blog/bytebase-vs-metabase/mb-access-control.webp)

- **Bytebase** has a two-level model: Workspace (Admin, DBA) manages database instances and global members; Project (Owner, Developer, SQL Editor User) manages day-to-day database work. Enterprise adds fine-grained controls: per-database or per-table access, per-environment policies (open in dev, restricted in prod), and request-based access where a developer files an issue to query or export a specific dataset.
  ![bb-access-control-adv](/content/blog/bytebase-vs-metabase/bb-access-control-adv.webp)

This second model matches [database access control best practices](/blog/database-access-control-best-practices/) for production: least privilege by default, access granted through approvals, every grant tied to an audit trail.

## Data masking

- **Metabase**: no dynamic masking. Row- and column-level permissions on Pro can hide fields from groups, but there's no transformation (e.g., showing `****-1234` instead of a full card number).
- **Bytebase**: dynamic data masking with semantic types. Admins tag a column (email, phone, credit card), Bytebase applies the appropriate masking algorithm, and authorized users can request unmasked access through an approval flow.
  ![bb-masking-graph](/content/blog/bytebase-vs-metabase/bb-masking-graph.webp)

Masking is an Enterprise feature. If you handle regulated data (PCI, HIPAA, GDPR), this is usually the deciding factor.

## Audit log

- **Metabase**: audit log is Pro+. Shows dashboard views, question runs, user sessions. Useful for BI usage analytics.
- **Bytebase**: audit log is Pro and Enterprise. Records every SQL Editor query, every schema change, every access grant, every approval decision. Built for compliance (SOC 2, ISO 27001) rather than product analytics. See [audit log for SOC 2 compliance](/blog/soc2-audit-logging/) for the mapping.

Both products are auditing different things, which follows from their primary purpose.

## SQL Review

**Only Bytebase has this.** SQL Review is a static-analysis layer with 200+ built-in rules plus custom rules (naming conventions, missing primary keys, dangerous statements, performance anti-patterns) that run automatically when a developer submits a change issue. It pairs with custom approval flows so that policy violations can block or gate a merge. Metabase has no equivalent because it does not change the database.

## Change workflow

- **Metabase**: not available. Metabase is a read-path tool; schema changes happen outside it.

- **Bytebase**: the change workflow is the product. A change goes through an issue that includes SQL Review, custom approval, scheduled rollout, rollback, and full history. If a developer types `ALTER TABLE` in SQL Editor, Bytebase prompts them to open an issue (or switch to Admin mode if they have the role).
  ![bb-issue](/content/blog/bytebase-vs-metabase/bb-issue-waiting.webp)

  Every change is recorded in the **Change History**. Bytebase also detects out-of-band changes and marks them as **Schema Drift**.
  ![bb-history](/content/blog/bytebase-vs-metabase/bb-history.webp)

## Open source and community

Both are open source, and they often appear together in database tooling stacks. Metabase has the longer history (launched 2015) and a larger BI user base. Bytebase (launched 2022) has grown faster on a per-month basis and sits in a different category.

![star-history](/content/blog/bytebase-vs-metabase/star-history.webp)

## Which should you choose?

This is rarely an either-or decision. A typical data stack uses both:

- **Pick Metabase** if your goal is dashboards, self-serve analytics, or embedded reporting. It is one of the cheapest ways to give business users a view into the database.
- **Pick Bytebase** if your goal is to govern database changes and production access: schema migrations with review and approval, masked queries for developers, audit trails for compliance.
- **Run both** if you have developers changing the schema and analysts reading from it. Bytebase protects the write path; Metabase powers the read path. They don't overlap.

## FAQ

**Is Bytebase a replacement for Metabase?**
No. Bytebase does not build dashboards or charts. If you need BI, you still need Metabase (or Superset, Looker, etc.). Bytebase replaces schema-change spreadsheets, ad-hoc DBA queues, and per-database access scripts: problems Metabase doesn't touch.

**Can Bytebase and Metabase run on the same database?**
Yes, and many teams do. Bytebase connects with an admin-level account to manage changes and access; Metabase connects with a read-only account for analytics. They don't conflict.

**Does Bytebase do data visualization?**
Only a Schema Diagram for database structure. There is no chart builder, no dashboard, no pivot. For data visualization, pair Bytebase with a BI tool.

**Does Metabase have schema change management or SQL review?**
No. Metabase reads the database; it does not alter it. Schema changes, SQL review, and approval workflows require a database change management tool like Bytebase, Liquibase, or Flyway.

**Which is cheaper for a 20-person team?**
Rough math with public pricing: Metabase Starter is $100/mo flat for 5 users + $65/user/yr for the other 15 on annual = ~$181/mo. Bytebase Pro is $20/user/mo × 20 = $400/mo. Bytebase Community (self-hosted) is free for up to 20 users. The answer depends on whether you need BI, self-hosting, and which features you actually use.

**Are both open source?**
Yes. Metabase is AGPL-licensed; Bytebase uses a mix of licenses with a commercial Enterprise edition. Self-hosting is supported by both.



> DBeaver is a desktop SQL client. Bytebase is a database DevSecOps platform.

DBeaver and Bytebase solve different problems. DBeaver is a universal SQL client — you install it locally, connect to a database, and query or edit data through a rich GUI. Bytebase is a server-side platform — you deploy it once, and teams use it for change review, access control, data masking, and audit logging across every database. If your team needs a powerful client to explore and edit data, DBeaver is the obvious choice. If your team needs to control who changes production and keep an audit trail of every operation, that's what Bytebase is built for.

## What Bytebase and DBeaver have in common

- Database management through a GUI — querying, exporting, data visualization, schema browsing.
- Tiered pricing with both free and paid plans.
- Support for a wide range of SQL and NoSQL databases.
- Open source — DBeaver has a longer history; Bytebase's star growth has outpaced it since 2023.
  ![star-history](/content/blog/bytebase-vs-dbeaver/star-history.webp)

## Key Differences Between Bytebase and DBeaver

The most significant difference is that Bytebase is a server-side platform built for teams — DBAs centralize access control, data masking, and audit logging. DBeaver is a client-side tool built primarily for individual use, with a Team Edition that adds shared connections but still runs as a desktop app per user.

|                                             | DBeaver                    | Bytebase                                              |
| ------------------------------------------- | -------------------------- | ----------------------------------------------------- |
| [Product position](#product-position)       | Powerful desktop SQL client | Database DevSecOps platform                          |
| [Developer interface](#developer-interface) | Client-side, desktop-based | Server-side, web-based                                |
| [Installation](#installation)               | Java-based, requires JVM   | Go binary, no external dependency                     |
| [Supported databases](#supported-databases) | 200+                       | 23                                                    |
| [Query](#query)                             | ✅                         | ✅                                                    |
| [Change](#change)                           | Direct                     | Review workflow (Git-style issue) + Admin mode        |
| [SQL review rules](#change)                 | ❌                         | ✅ 200+ rules (free)                                  |
| [Access control](#access-control)           | ❌ (Team Edition: shared conns only) | ✅ Free: workspace/project roles; Enterprise: database/table-level |
| [Data masking](#data-masking)               | ❌                         | ✅ Enterprise (dynamic, column-level)                 |
| [Compare schema](#compare)                  | 1:1 (Pro)                  | 1:N (free)                                            |
| [Audit log](#audit-log)                     | ❌                         | ✅ Pro                                                |

### Product position

- **DBeaver**: A universal SQL client. It started as an individual desktop tool and expanded to a Team Edition and a web-based CloudBeaver. Its center of gravity is still **individual database management** — rich query editor, data viewer, schema browser, ER diagrams.
  ![dbv-position](/content/blog/bytebase-vs-dbeaver/dbv-position.webp)

- **Bytebase**: A database DevSecOps platform for developers, DBAs, and platform teams. Built around **team workflows** — change review, custom approval flows, access control, data masking, audit logging — with a SQL Editor layered on top.
  ![bb-3-op](/content/blog/bytebase-vs-dbeaver/bb-3-op.webp)

### Developer interface

- **DBeaver**: Desktop GUI on Windows / macOS / Linux. Every user installs it locally and connects directly to databases. DBeaver Team Edition adds a shared connection store, but the client is still a per-user desktop app.
  ![dbv-gui](/content/blog/bytebase-vs-dbeaver/dbv-gui.webp)

- **Bytebase**: Web GUI with an issue-based change workflow plus a SQL Editor for querying. Also exposes an [API](https://api.bytebase.com/), a [Terraform provider](https://docs.bytebase.com/integrations/terraform/overview), and [GitOps](https://docs.bytebase.com/gitops/overview) for teams that prefer automation.
  ![bb-issue](/content/blog/bytebase-vs-dbeaver/bb-issue-waiting.webp)
  ![bb-gui](/content/blog/bytebase-vs-dbeaver/bb-gui.webp)

### Installation

- **DBeaver**: Download from the official website — bundled with Eclipse and a JVM. DBeaver Team Edition requires a server on Linux or macOS.

- **Bytebase**: Single Go binary, no external dependencies. Also ships as a [Docker image](https://docs.bytebase.com/get-started/deploy-with-docker) and [Helm chart for Kubernetes](https://docs.bytebase.com/get-started/deploy-with-kubernetes).

### Supported databases

- **DBeaver**: 200+ databases in the **Community** edition via JDBC, covering MySQL, PostgreSQL, Oracle, SQL Server, SQLite, and many more. The **Pro** editions (Ultimate, Team) add NoSQL drivers (MongoDB, Cassandra, Redis, DynamoDB) and cloud warehouses (Redshift, BigQuery, Snowflake, Databricks).

- **Bytebase**: 25 engines with deep integration (not just query) — 9 RDBMS (MySQL, PostgreSQL, Oracle, SQL Server, MariaDB, TiDB, OceanBase, CockroachDB, Spanner), 6 NoSQL (MongoDB, Redis, Cassandra, DocumentDB, DynamoDB, Cosmos DB), 9 data warehouses (Snowflake, BigQuery, Redshift, Hive, ClickHouse, Databricks, StarRocks, Doris, Trino), and Elasticsearch. Each engine gets engine-specific SQL review rules, schema sync, and masking support.

### Query

- **DBeaver**: Query by double-clicking a table or by writing SQL in the editor with templates, syntax highlighting, and autocomplete. The GUI is dense and configurable — power users can tune toolbars, keybindings, and result grid behavior in detail.
  ![dbv-data-query](/content/blog/bytebase-vs-dbeaver/dbv-data-query.webp)

  Saved scripts stay local in Community; **Team Edition** shares scripts and connections across the team.
  ![dbv-sheets](/content/blog/bytebase-vs-dbeaver/dbv-sheets.webp)

- **Bytebase**: The **SQL Editor** runs queries through the platform — double-click a table or write SQL with autocomplete. The GUI is lighter than DBeaver but covers everyday querying, exporting, and chart-based visualization.
  ![bb-data-query](/content/blog/bytebase-vs-dbeaver/bb-data-query.webp)

  Save or star scripts for quick access. On **Pro** and above, share scripts with your team or project.
  ![bb-sheets](/content/blog/bytebase-vs-dbeaver/bb-sheets.webp)

### Change

- **DBeaver**: Edit data directly in the result grid or run SQL manually. Edits stay local to the user's session — no review, no approval, no centralized audit. A per-user transaction log records what you ran.
  ![dbv-edit-cell-dbclick](/content/blog/bytebase-vs-dbeaver/dbv-edit-cell-dbclick.webp)

  ![dbv-change-history](/content/blog/bytebase-vs-dbeaver/dbv-change-history.webp)

- **Bytebase**: Changes flow through an **issue** — like a pull request for the database. Each issue runs automated [SQL review](https://docs.bytebase.com/sql-review/review-rules) (200+ rules), routes through a [custom approval flow](https://docs.bytebase.com/change-database/approval) if configured, and deploys with full history tracking.
  ![bb-issue](/content/blog/bytebase-vs-dbeaver/bb-issue-waiting.webp)

  Every change is recorded in **Change History** with diff view and rollback scripts.
  ![bb-history](/content/blog/bytebase-vs-dbeaver/bb-history.webp)

  Ad-hoc DDL (e.g. `ALTER TABLE` typed into SQL Editor) either creates a new issue or requires **Admin mode** — a deliberate escape hatch for break-glass scenarios, similar to SSH on a server.
  ![bb-force-issue](/content/blog/bytebase-vs-dbeaver/bb-force-issue.webp)
  ![bb-force-issue-preview](/content/blog/bytebase-vs-dbeaver/bb-force-issue-preview.webp)

For schema changes, most teams use the visual **Schema Editor** — edit columns, constraints, and indexes in the GUI and Bytebase generates the DDL automatically.

![bb-select-db](/content/blog/bytebase-vs-dbeaver/bb-select-db.webp)
![bb-schema-editor](/content/blog/bytebase-vs-dbeaver/bb-schema-editor.webp)

### Access Control

- **DBeaver**: Desktop-based — each user's credentials live in their local client. DBeaver Team Edition centralizes connection storage and adds shared credential management, but actual database permissions are still whatever the DBMS grants each connecting user. No concept of granular permission control inside DBeaver itself.

- **Bytebase**: Roles are scoped to **Workspace** and **Project** levels. `Workspace Admin` / `DBA` provision instances and manage members; `Project Owner` manages databases, issues, and roles within their project. Individuals can hold multiple project-level roles like `Project Developer` (change database) or `SQL Editor User` (query only).
  ![bb-access-control](/content/blog/bytebase-vs-dbeaver/bb-access-control.webp)

  On **Community**, users only see projects they're assigned to. On **Enterprise**, `Project Owner` can grant access down to specific databases or tables, and developers can request [Just-In-Time access](https://docs.bytebase.com/security/database-permission/overview) via a request issue that expires automatically.
  ![bb-access-control-adv](/content/blog/bytebase-vs-dbeaver/bb-access-control-adv.webp)

  Different environments can enforce different policies — e.g., read-only access to production, full access to development.
  ![bb-access-environment](/content/blog/bytebase-vs-dbeaver/bb-access-control-env.webp)

### Data Masking

- **DBeaver**: No data masking. Because DBeaver is client-side, the raw data is already on the user's machine by the time it's rendered.

- **Bytebase**: [Dynamic data masking](https://docs.bytebase.com/security/data-masking/overview) at the column level (Enterprise). `Admin` / `DBA` / `Project Owner` configure masking per column and authorize specific users to see the original data. Everyone else sees masked values in query results.

  Semantic types (email, phone, credit card, SSN) let you define reusable masking algorithms applied consistently across the schema.
  ![bb-masking-graph](/content/blog/bytebase-vs-dbeaver/bb-masking-graph.webp)

### Compare

- **DBeaver**: Schema compare is **1:1** — compare two databases at a time. Results export as DDL scripts or Liquibase changelogs. Available in DBeaver Pro.
  ![dbv-schema-compare](/content/blog/bytebase-vs-dbeaver/dbv-schema-compare.webp)

- **Bytebase**: **Schema Sync** is **1:N** (free) — pick one source database and compare against several targets at once. Results become a batch change issue that routes through SQL review, approval, and rollout.
  ![bb-schema-compare-several](/content/blog/bytebase-vs-dbeaver/bb-schema-compare-several.webp)

  ![bb-batch-issue](/content/blog/bytebase-vs-dbeaver/bb-batch-issue.webp)

### Audit log

- **DBeaver**: No audit log — client-side by design, so there's no central event stream to capture.

- **Bytebase**: [Audit Log](https://docs.bytebase.com/security/audit-log) (Pro and above) records every action in the platform — queries, DDL, approvals, policy changes. Filter by user, action type, or time range; export for SIEM ingestion.
  ![bb-audit-log](/content/blog/bytebase-vs-dbeaver/bb-audit-log-dropdown.webp)

### Pricing

- **DBeaver**: Community is free and open source. DBeaver Pro is $10/month for Ultimate (personal) or $15/user/month for Team Edition (collaborative, server-based). Enterprise is custom pricing.

- **Bytebase**: [Community](https://www.bytebase.com/pricing/) is free, self-hosted, up to 20 users and 10 instances — includes SQL review, GitOps, multi-env rollouts. Pro is $20/user/month (cloud, up to 10 instances) and adds SSO, audit log, user groups. Enterprise is custom pricing, self-hosted or cloud, and unlocks dynamic data masking, custom approval flows, custom roles, SCIM, LDAP/OIDC SSO, and 2FA.

## When to Choose DBeaver

- You need a powerful desktop SQL client to explore and edit data across many database types.
- Your team works individually — each developer manages their own connections and runs their own queries.
- You rely on JDBC breadth (200+ engines) and don't need server-side governance.
- Query convenience and rich UI features (ER diagrams, data transfer, visual query builder) matter more than review workflows.

## When to Choose Bytebase

- You want a single platform that covers change review, access control, data masking, and audit logging — not just a SQL client.
- Developers write changes and DBAs (or platform engineers) review and deploy them.
- You need compliance artifacts — approval trails, audit logs, masking — for SOC 2, GDPR, or internal policy.
- You want database changes treated like code changes: reviewed, approved, traceable, and rolled out across environments.

## FAQ

### Can I use DBeaver and Bytebase together?

Yes — many teams do. Developers use DBeaver locally for rich data exploration and ad-hoc querying, and submit schema or production data changes through Bytebase for review and controlled rollout. DBeaver handles "I need to explore"; Bytebase handles "this change is going to production."

### Does Bytebase replace my local SQL client?

For everyday querying across reviewed databases, yes — the SQL Editor works well. For deep data exploration, complex result-grid operations, or connecting to obscure JDBC databases, a desktop client like DBeaver still has an edge. Bytebase's SQL Editor is built around team workflow, not raw client capability.

### Is DBeaver Team Edition comparable to Bytebase?

Partially. Team Edition shares connections and scripts across a team and adds a server-based backend — but it doesn't add review workflows, approval flows, data masking, or audit logging. The core mental model is still "desktop clients connecting to a shared connection store."

### Is Bytebase Community actually usable for production?

Yes. Community includes the full GUI, 200+ SQL review rules, GitOps integration, multi-environment rollouts, and batch changes — same core workflow as the paid tiers. Limits: 20 users and 10 instances. You upgrade when you need SSO, audit log (Pro), or compliance features like data masking and custom approval flows (Enterprise).

---

Related comparisons:

- [Bytebase vs. CloudBeaver](/blog/bytebase-vs-cloudbeaver/)
- [Bytebase vs. DataGrip](/blog/bytebase-vs-datagrip/)
- [Top DBeaver Alternatives](/blog/top-dbeaver-alternative/)
- [pgAdmin vs. DBeaver](/blog/pgadmin-vs-dbeaver/)
- [Top Open Source SQL Clients](/blog/top-open-source-sql-clients/)


<HintBlock type="info">

This post is maintained by Bytebase, an open-source database DevSecOps tool that can manage both Aurora and RDS. We update the post every year.

</HintBlock>

| Update History | Comment |
| -------------- | ------------------ |
| 2024/01/22 | Initial version. |
| 2025/04/27 | Added Aurora DSQL. |

![gartner](/content/blog/aurora-vs-rds/gartner-2024.webp)

Once again, AWS is ranked among the top in the latest Gartner 2024 Magic Quadrant for Cloud Database Management Systems.
The AWS OLTP relational database portfolio consists of 3 products:

- `Amazon RDS` (Relational Database Service) was announced by AWS on October 22, 2009 and probably
 is the largest database fleet in the world.

- `Amazon Aurora` was announced on November 12, 2014, positioned as RDS premium providing unparalleled high performance and availability at global scale with full MySQL and PostgreSQL compatibility.

- `Amazon Aurora DSQL` was announced in December 2024 at AWS re:Invent, offering a distributed SQL database with 99.999% multi-Region availability, strong consistency, PostgreSQL compatibility, and claims of 4x faster reads and writes compared to other distributed SQL databases.

When an engineering team onboards AWS, one of the very first tasks is to choose the right database solution for their needs (unless they decide to go NoSQL, then they need to pick between MongoDB, DynamoDB, and other options). Below we provide our engineering perspective to compare the following dimensions:

- [Supported Databases](#supported-databases)
- [Architecture](#architecture)
- [Compatibility](#compatibility)
- [Performance](#performance)
- [Elasticity](#elasticity)
- [High Availability](#high-availability)
- [Pricing](#pricing)
- [Recommended Setup](#recommended-setup)
- [Further Readings](#further-readings)

## Supported Databases

![databases](/content/blog/aurora-vs-rds/databases.webp)

RDS supports all mainstream relational databases including MySQL, PostgreSQL, MariaDB, SQL Server, Oracle, and Db2, with Extended Support for older versions. As of 2025, RDS maintains major versions of these engines.

Aurora supports MySQL and PostgreSQL. The service has maintained its focus on these two popular open-source database engines, optimizing their performance in the cloud environment.

![databases](/content/blog/aurora-vs-rds/upstream-version.webp)

Aurora DSQL, the newest addition to AWS's database portfolio, currently supports PostgreSQL compatibility.

RDS offers all upstream versions, but some are nearing end-of-support, such as PostgreSQL 12 (end date: Feb 28, 2025) and MySQL versions 8.0.32-8.0.36 (end date: Mar 31, 2025). Extended Support is available for up to 3 years at additional cost, starting March 1, 2024.

Aurora versions include MySQL 3.08.2 (compatible with MySQL 8.0.39) and PostgreSQL 16.4, with new features like fast failover. Aurora DSQL focuses on PostgreSQL compatibility and distributed workloads with strong consistency across regions.

## Architecture

RDS mostly runs the vanilla database engines on the cloud. While Aurora is a **cloud-native** database service.
Aurora is cloud-native because it leverages the cloud environment to separate compute and storage, uses [Amazon S3](https://aws.amazon.com/s3/) to persist data. This novel approach increase performance, high availability, and scalability.

![aurora-arch](/content/blog/aurora-vs-rds/aurora-arch.webp)

Aurora DSQL builds on this with a distributed, multi-region design, enabling active-active architecture for global operations. It ensures consistency through **decoupled transaction processing** and **microsecond-accurate time synchronization**, ideal for globally distributed, highly available applications.

![aurora-dsql-arch](/content/blog/aurora-vs-rds/aurora-dsql-arch.webp)

## Compatibility

Both Aurora and RDS bear the same limitation as a Cloud database:

- You can only have [semi-super user](https://docs.aws.amazon.com/AmazonRDS/latest/UserGuide/UsingWithRDS.MasterAccounts.html).
- You are not allowed to access the database server file systems. For MySQL, this means
 you can only use `LOAD DATA LOCAL` to import data from your local file system, while you can't use `LOAD DATA` to import from the server
 file system.

Though Aurora is a proprietary technology, it mostly differs from the vanilla MySQL / PostgreSQL on the storage engine layer.
On the other hand, the server layer, which determines most user-facing behaviors is almost identical. Thus the main compatibility gaps for Aurora is storage engine related. e.g. Aurora for MySQL only supports InnoDB, while RDS supports old engines such as MyISAM (however, you won't use MyISAM engine anyway if you start a new project).

Saying that, Aurora codebase diverges more from the vanilla MySQL/Postgres than RDS, thus you should expect more lags for the AWS team to bring in the latest upstream update. e.g. Aurora PostgreSQL added the popular pgvector extension 2 months later than RDS.

![pgvector-rds](/content/blog/aurora-vs-rds/pgvector-rds.webp)
![pgvector-aurora](/content/blog/aurora-vs-rds/pgvector-aurora.webp)

For Aurora DSQL, as a newer service, the extension ecosystem is still developing. While it maintains core PostgreSQL compatibility, not all extensions available in standard PostgreSQL or Aurora PostgreSQL are currently supported in Aurora DSQL. AWS has indicated plans to expand extension support based on customer demand.

## Performance

According to [the official website](https://aws.amazon.com/rds/aurora/features/), Aurora offers up to 5x the throughput of MySQL and 3x the throughput of PostgreSQL.

This [benchmark](https://dev.to/aws-heroes/amazon-aurora-is-now-60-times-faster-than-rds-for-mysql-really-3c0e) suggesting Aurora can be 60 times faster than RDS.

Aurora provides better write performance because it reduces the write amplification by only sending the redo log to the remote storage service, which eliminates other writes during transaction commit path such as the infamous double-write buffer.

Aurora provides better read scalability because of the log-based architecture, it can support up to 15 read-replicas. RDS can only support 5, RDS doesn't support more because the classic streaming replication carries more performance penalty on the primary. Aurora also incurs much lower replication lags, especially under write-heavy load.

Aurora DSQL takes performance to a new level for distributed workloads. According to AWS, Aurora DSQL delivers 4x faster reads and writes compared to other popular distributed SQL databases. This is achieved through its innovative approach to transaction processing that parallelizes writes across regions and its use of the Amazon Time Sync Service for precise operation ordering.

RDS uses EBS, the disk performance differs based on the [storage types](https://docs.aws.amazon.com//AmazonRDS/latest/UserGuide/CHAP_Storage.html). Since 2023, AWS has continued to improve the underlying EBS performance, but the architectural differences still give Aurora and Aurora DSQL significant advantages for most workload types.

In general, Aurora outperforms RDS considerably, and Aurora DSQL offers even greater performance for distributed workloads. But you still need to benchmark for your own specific use cases to determine the best fit.

## Elasticity

![gp2-burst](/content/blog/aurora-vs-rds/gp2-burst.webp)

RDS doesn't have much elasticity, if you choose the gp2 SSD storage, it can be [bursted](https://docs.aws.amazon.com//AmazonRDS/latest/UserGuide/CHAP_Storage.html) and that's pretty much it.

Standard Aurora can provision read-replicas quickly, other than that, it doesn't provide elasticity out of the box. However, Aurora's architecture does allow for faster storage scaling compared to RDS.

Aurora Serverless and especially its [Serverless v2](https://docs.aws.amazon.com/AmazonRDS/latest/AuroraUserGuide/aurora-serverless-v2.html) offers great elasticity to scale up/down computing resources. Great for workloads such as e-commerce. In 2025, AWS announced [Serverless Reservations](https://aws.amazon.com/about-aws/whats-new/2025/04/serverless-reservations-discounted-pricing-option-amazon-redshift-serverless/), a new discounted pricing option for serverless database services, making Aurora Serverless even more cost-effective for predictable workloads with variable capacity needs.

Aurora DSQL takes elasticity to a new level with its distributed architecture. It automatically scales to meet any workload demand without database sharding or instance upgrades, and can scale reads and writes independently, eliminating scaling bottlenecks while maintaining performance. This makes Aurora DSQL particularly well-suited for applications with unpredictable global traffic patterns or those requiring rapid scaling across regions.

## High Availability

![ha](/content/blog/aurora-vs-rds/ha.webp)

RDS offers multi-AZ HA setup with a SLA of `up to 99.95%`, while Aurora HA offers `up to 99.99%`. Aurora can failover faster because of its log-based architecture. And for write-intensive load, RDS failover are more problematic because of the high replication lag.

Aurora also has [global databases](https://docs.aws.amazon.com/AmazonRDS/latest/AuroraUserGuide/aurora-global-database.html) to enable low latency global reads and disaster recovery from outages across an AWS Region.

Aurora DSQL takes high availability to a new level with its 99.999% multi-Region availability (five nines). Its active-active architecture and automated failure recovery ensure that applications can always read and write to any Aurora DSQL endpoint, even during regional outages. All transactions written in one Region are reflected in other Regions with strong consistency, eliminating the traditional trade-offs between availability and consistency.

In 2024, AWS also enhanced Amazon DynamoDB global tables with multi-Region strong consistency, using the same underlying technology leveraged by Aurora DSQL. This indicates AWS's strategic focus on providing strongly consistent, highly available database services across its portfolio.

## Pricing

![pricing-rds-aurora](/content/blog/aurora-vs-rds/pricing-rds-aurora.webp)

If you follow the AWS wizard to pick the production template (8c64g r6g.2xlarge + multi-AZ HA), you will find the price
for Aurora is much cheaper than RDS. Too good to be true.

![pricing-aurora-notes](/content/blog/aurora-vs-rds/pricing-aurora-notes.webp)

The caveat is Aurora also charges on I/O that is not included in the pricing estimate. This surprised quite a few
customers when they receive a huge AWS bill. This unpredictable cost was the biggest concern for teams adopting
Aurora initially. In 2023, Aurora fixed this by introducing Aurora I/O-Optimized to include the I/O cost in the storage, and this pricing model has continued to be refined through 2025.

![pricing-aurora-io-optimized](/content/blog/aurora-vs-rds/pricing-aurora-io-optimized.webp)

In 2025, AWS introduced Serverless Reservations, a new discounted pricing option for serverless database services including Aurora Serverless. This provides cost savings for customers who can commit to a minimum level of usage while still maintaining the flexibility of serverless scaling.

For RDS, starting March 2024, Extended Support fees apply (5%–20% over three years) for outdated database versions, making version planning critical.

Aurora DSQL, while more expensive, can be cost-effective for multi-region, strongly consistent applications compared to building custom solutions.

Besides, only RDS offers free-tier and low db instance tiers starting from 2c1g t3.micro. Standard Aurora
starts from 2c16g r5.large and burstable classes start from 2c4g t3.medium. Aurora Serverless can specify
0.5 ACU as the minimum. 1 ACU provides 2 GiB of memory and corresponding compute and networking.

You should use [AWS Pricing Calculator](https://calculator.aws/) and it's also a good idea to monitor your
database cost by using a specialized tool such as [Vantage](https://www.vantage.sh/).

## Recommended Setup

Capability-wise, AWS Aurora beats RDS in almost every aspect, and Aurora DSQL now extends these capabilities even further for distributed applications. AWS continues to invest more resources in Aurora and Aurora DSQL than in RDS since these services are key differentiators from other cloud vendors. Though Aurora is mostly MySQL / PostgreSQL compatible, it creates a different vendor lock-in by providing unmatched ROI. Aurora DSQL adds another layer of capability for applications requiring global distribution with strong consistency.

For bootstrapped or small business, Aurora is still pricy and RDS is a cost-effective solution:

| Startup | Choice |
| ------------- | -------------- |
| Test Instance | RDS without HA |
| Prod Instance | RDS with HA |

For growth stage business, Aurora is more suitable, and Aurora Serverless offers the extra flexibility
to optimize the cost.

| Growth | Choice |
| ------------------------------------ | ---------------------------- |
| Always-on Test Instance | Standard Aurora without HA |
| On-demand Test Instance | Aurora Serverless without HA |
| Prod Instance | Standard Aurora with HA |
| Prod Instance with fluctuate traffic | Aurora Serverless with HA |

For enterprise businesses with global applications requiring the highest levels of availability and consistency, Aurora DSQL provides capabilities that were previously difficult to achieve:

| Enterprise | Choice |
| ---------------------------------------------------- | ----------- |
| Global applications | Aurora DSQL |
| Multi-region applications with high write throughput | Aurora DSQL |

Normally, you should start with RDS and migrate to Aurora when the business takes off, and consider Aurora DSQL when your application requires global distribution with strong consistency. AWS provides [detailed guide](https://aws.amazon.com/getting-started/hands-on/migrate-rdsmysql-to-auroramysql/) on how to migrate from RDS to Aurora with near zero downtime, and similar migration paths are available from Aurora to Aurora DSQL.

---

After you decide the database product, there remains task to figure out the database
development workflow:

- How developers propagate the change from dev to production?
- How to get the SQL change peer-reviewed by the DB person in an efficient manner? Or even better with automated SQL checks?
- How to enforce data access policy for PII compliance?

If you are thinking about these problems, please check out Bytebase. It's an [all-in-one tool](/blog/all-database-tools-bytebase-replaces) to centralize all human-to-db operations for heterogeneous databases.

![change-query-secure-govern-database-all-in-one](/content/blog/_shared/db-scheme-lg.png)

## Further Readings

- [Postgres vs. MySQL: a Complete Comparison](/blog/postgres-vs-mysql)
- [Amazon Aurora ascendant: How we designed a cloud-native relational database](https://www.allthingsdistributed.com/2019/03/amazon-aurora-design-cloud-native-relational-database.html)
- [Amazon database master user account privileges](https://docs.aws.amazon.com/AmazonRDS/latest/UserGuide/UsingWithRDS.MasterAccounts.html)
- [Amazon Aurora features](https://aws.amazon.com/rds/aurora/features/)
- [Amazon Aurora pricing](https://aws.amazon.com/rds/aurora/pricing/)
- [Amazon Aurora DSQL: The fastest distributed SQL database](https://aws.amazon.com/rds/aurora/dsql/)
- [Using Aurora Serverless v2](https://docs.aws.amazon.com/AmazonRDS/latest/AuroraUserGuide/aurora-serverless-v2.html)
- [High availability for Amazon Aurora](https://docs.aws.amazon.com/AmazonRDS/latest/AuroraUserGuide/Concepts.AuroraHighAvailability.html)
- [Amazon RDS features](https://aws.amazon.com/rds/features/)
- [Amazon RDS pricing](https://aws.amazon.com/rds/pricing/)
- [Amazon RDS Extended Support](https://docs.aws.amazon.com/AmazonRDS/latest/UserGuide/extended-support.html)
- [Amazon RDS DB instance storage](https://docs.aws.amazon.com//AmazonRDS/latest/UserGuide/CHAP_Storage.html)
- [AWS Serverless Reservations pricing](https://aws.amazon.com/about-aws/whats-new/database/?whats-new-content.sort-by=item.additionalFields.postDateTime&whats-new-content.sort-order=desc&awsf.whats-new-products=*all)

<HintBlock type="info">

This post is maintained by Bytebase, an open-source database DevSecOps tool that can manage both Postgres and MongoDB. We update the post every year.

</HintBlock>

| Update History | Comment |
| -------------- | ----------------- |
| 2024/01/14 | Initial version. |
| 2025/02/27 | Added AI section. |

> For the impatience, jump to the [last section](#postgres-or-mongodb) to see the comparison table.

## Why Comparing Postgres and MongoDB

Postgres is the most admired, desired database in the last 2 Stack Overflow surveys ([2024](https://survey.stackoverflow.co/2024/technology/#1-databases), [2023](https://survey.stackoverflow.co/2023/#most-popular-technologies-database).

![stackoverflow](/content/blog/postgres-vs-mongodb/stackoverflow.webp)

ICYDK, MongoDB used to hold that title for 4 consecutive years from 2017 to 2020. And according to DB-Engines, Postgres and MongoDB are among the top 5 databases. They are the two climbling the ladder and eating the shares of the big three, Oracle, MySQL, and Microsoft SQL Server.

![db-engines](/content/blog/postgres-vs-mongodb/db-engines.webp)

MongoDB is widely perceived as a NoSQL database which is opposite to the SQL database clan including Postgres.
But in recent years, they are converging:

- MongoDB becomes more like a traditional RDBMS, adding multi-document ACID transaction, secondary-index, advanced
 query capabilities.
- Postgres keeps improving its JSON capabilities such as indexing, query optimization, and more operators, which makes people wonder whether MongoDB becomes obsolete.

![hn](/content/blog/postgres-vs-mongodb/hn.webp)

In 2018, folks at The Guardian wrote a [lengthy post](https://www.theguardian.com/info/2018/nov/30/bye-bye-mongo-hello-postgres) about migrating MongoDB to Postgres. A lot of things have changed since
then, but one thing still holds true, it's always painful to migrate databases.

![hn](/content/blog/postgres-vs-mongodb/byebye-mongo.webp)

At Bytebase, we work with both databases extensively since the Bytebase product needs to integrate
with both. Our founders also build [Google Cloud SQL](https://cloud.google.com/sql), one of the largest hosted database services.

Based on our operating experience, below we give an extensive comparison between Postgres and MongoDB
from the following dimensions:

- [License](#license)
- [Data Model](#data-model)
- [JSON Support](#json-support)
- [Performance](#performance)
- [Reliability](#reliability)
- [Scalability](#scalability)
- [Usability](#usability)
- [Operability](#operability)
- [Ecosystem](#ecosystem)
- [AI](#ai)

_Unless otherwise specified, the comparison below is between the latest major release, Postgres 15 vs. MongoDB 6. We also use Postgres instead of PostgreSQL throughout the article, though we know the latter is the official name, which is considered as [the biggest mistake in Postgres History](https://www.craigkerstiens.com/2018/10/30/postgres-biggest-mistake/)_.

## License

- Postgres is released under the PostgreSQL license which is a liberal Open Source license similar to
 the BSD or MIT licenses.

- MongoDB community edition is released under [Server Side Public License (SSPL)](https://en.wikipedia.org/wiki/Server_Side_Public_License), a license created by MongoDB Inc. itself to prevent others from offering the competing
 MongoDB service. MongoDB Inc. also provides commercial licenses upon request.

Most companies use databases to support their internal infrastructure, both Postgres and MongoDB permit this usage.

## Data Model

Postgres is a relational database management system. It stores data in tables with predefined columns and data types. Relationships between tables are established using foreign keys.

MongoDB is a document-oriented database, which means that data is stored as documents in a collection.
Each document is a JSON-like structure that can contain nested fields and arrays. MongoDB is designed to handle unstructured and semi-structured data.

| Postgres (Tabular Model) | MongoDB (Document Model) |
| ------------------------ | ------------------------ |
| Database | Database |
| Schema | N/A |
| Table | Collection |
| Row | Document |
| Column | Field |

Postgres also supports JSON column, so one can also use Postgres in the MongoDB way, defining table as:

```sql
CREATE TABLE my_collection (
 id SERIAL PRIMARY KEY,
 data JSONB
);
```

## JSON Support

Both MongoDB and Postgres are very capable of handling JSON. MongoDB stores JSON using its own invented [BSON](https://www.mongodb.com/docs/manual/reference/bson-types/), while Postgres uses a different JSONB format. For those interested, there is a [lengthy discussion](https://www.postgresql.org/message-id/CA%2BTgmoagjFfJst%3D9kSu4rZatCE8SRuOQCH_h-_YW%3D4_c687GTA%40mail.gmail.com) around whether to choose BSON or JSONB in Postgres.

MongoDB has 2 advantages:

1. Built-in schema validator.
1. Its integration with the Node.js/frontend ecosystem. MongoDB is a favored choice among full-stack developers who commonly utilize Node.

## Performance

Performance is primarily determined by the access pattern. If an operation involves different entities, MongoDB is usually
faster because data is de-normalized and doesn't require costly joins between tables. On the other hand, Postgres
is more capable of handling complex queries thanks to SQL and its sophisticated query optimizer.

## Reliability

MongoDB was known to be less reliable because it didn't support ACID transaction semantics in the early days.
This has changed since they acquired WiredTiger and use its WiredTiger storage engine. Today, from the transaction perspective, MongoDB is as solid as Postgres.

MongoDB provides built-in automatic failover via its replica set. Postgres needs 3rd party solutions like [pg_auto_failover](https://github.com/hapostgres/pg_auto_failover).

## Scalability

MongoDB scales out, while Postgres scales up.

MongoDB is a distributed database supporting automatic sharding. For Postgres, people usually scale up the single node postgres first and defer the sharding solution as late as possible. Of course, sharding Postgres is doable:

- [Sharding & IDs at Instagram](https://instagram-engineering.com/sharding-ids-at-instagram-1cf5a71e5a5c)
- [Herding elephants: Lessons learned from sharding Postgres at Notion](https://www.notion.so/blog/sharding-postgres-at-notion)

## Usability

MongoDB doesn't enforce schema upfront and has an easy learning curve. Back in 2010, 10gen (the original name for the company that developed MongoDB) released the first version of the official MongoDB Node.js driver, which allowed developers to easily interact with MongoDB databases from Node.js applications. The driver provided a simple and intuitive API that supported a wide range of MongoDB features, including querying, indexing, and aggregation. Today, MongoDB
still is the de-facto choice for full-stack developers because of its ease of use.

Postgres, as a relational database, enforces schemas. And even among the relational database group, Postgres is
more rigorous than other peers like MySQL.

![stackoverflow](/content/blog/postgres-vs-mongodb/stackoverflow-learning.webp)

As the Stack Overflow survey shows, though Postgres is the most popular database among all respondents, MongoDB
is more welcomed by the new learners.

On the other hand, MQL (MongoDB Query Language) is less powerful than SQL in handling complex queries.

## Operability

Running a multi-node MongoDB is easier than running a multi-node Postgres, since sharding, failover
are already handled by MongoDB itself. On the other hand, if you run both databases on a single
node, then those distributed features in MongoDB become an overhead.

A variety of 3rd party vendors provide Postgres hosting service. MongoDB's license has prevented
3rd party from providing the MongoDB hosting service, and their own MongoDB Altas is more polished than
the equivalent Postgres hosting options. After all, it's hard for 3rd party to beat 1st party cloud service.

## Ecosystem

Postgres has an extensible architecture and is still maintained by the community. The Postgres ecosystem is
thriving in recent years, it has [a plethora of extensions](/blog/top-postgres-extension/) making
it more capable of handling different workloads than other databases. And thanks to its liberal license and
solid architecture, for every application platform offering a hosted database service, they all choose Postgres. From the [Heroku](https://www.heroku.com/) in the early days to the new [Supabase](https://supabase.com/), [render](https://render.com/), [Fly.io](https://fly.io/).

MongoDB, as the most successful commercial open source business by market cap, takes a more commercialized
approach. Whenever there emerges a good solution in the ecosystem, MongoDB Inc. would consider acquiring it
to make sure it becomes an integral part of the overall MongoDB product line. Notable acquisitions include:

- [WiredTiger](https://www.mongodb.com/press/wired-tige) for the storage engine.
- [mLab](https://www.mongodb.com/press/mongodb-strengthens-global-cloud-database-with-acquisition-of-mlab) for the hosting service.
- [Realm](https://www.mongodb.com/press/mongodb-strengthens-mobile-offerings-with-acquisition-of-realm) for the mobile offering.
- [Compass](https://www.mongodb.com/products/compass) for the MongoDB GUI client.

Though Postgres and MongoDB take polarized approach to cultivate the ecosystem, both are widely successful.

## AI

Postgres leverages [pgvector](https://github.com/pgvector/pgvector) extension for vector operations. MongoDB provide built-in [vector search](https://www.mongodb.com/products/platform/atlas-vector-search). MongoDB also [just acquired Voyage AI](https://www.mongodb.com/blog/post/redefining-database-ai-why-mongodb-acquired-voyage-ai) to tackle the hallucination problem of LLM.

![mongodb-voyage](/content/blog/postgres-vs-mongodb/mongodb-voyage.webp)

## Postgres or MongoDB

| | Postgres | MongoDB |
| ------------ | --------------------------------------------------------------- | ------------------------------------------------------- |
| License | Postgres License (MIT alike) | SSPL |
| Data Model | Tabular, relational | Document |
| JSON Support | Capable and integrated with SQL | Built-in schema validator |
| Performance | Optimized for complex query | Optimized for de-normalized data |
| Reliability | Full ACID transaction support | Transaction + built-in auto failover |
| Scalability | Scale-up | Scale-out |
| Usability | Rigorous and powerful query capability | Relaxed enforcement and easy to start |
| Operability | Optimized for single node and a wide range of hosting providers | Optimized for multi-node and a polished hosting service |
| Ecosystem | Community driven, decentralized, vibrant | Business driven, centralized, integral |
| AI | pgvector extension | Built-in vector search |

There are obvious domains where one database is more suitable than the other:

- Choose Postgres if your application has complex business logic and requires complex queries.
- Choose MongoDB if your application has a simple data model and handles web scale data.

Yet, the majority of application use cases fall between them and both databases are very capable:

- MongoDB as a document database, naturally provides 1st class JSON support. While Postgres also has the best
 JSON support among all open source relational databases.
- For full-stack developers, MongoDB used to be the go-to choice because of its excellent Node.js integration. While
 Postgres has been attracting them as every major 3rd party application platform offers managed Postgres service.
- For backend developers, MongoDB becomes appealing ever since it added transaction support. Who doesn't want
 to avoid [stressful schema change procedure](/blog/how-to-handle-database-schema-change) and enjoy faster iteration cycle?
- MongoDB Atlas has built one of the most polished hosted database services. While many 3rd party vendors, from large to small, all provide great Postgres hosting solutions.

Overall, Postgres is a more versatile database. It adopts relational model, provides comprehensive SQL capability, carries an extensible architecture, and is driven by an enthusiastic community.

MongoDB is a holistic database solution. It popularizes document model, provides built-in scaling and high availability, offers an integral developer experience, and is driven by a sharp-minded for-profit business entity.

If choosing between [Postgres and MySQL](/blog/postgres-vs-mysql) is hard, then choosing between Postgres and MongoDB is no easier. And as both databases are heading upward, the choice will only become harder 🤷‍♂️.

---

It's also common that Postgres and MongoDB co-exist inside an organization. And if you want to manage the database development
lifecycle for both of them, please check out [Bytebase](/).

![change-query-secure-govern-database-all-in-one](/content/blog/_shared/db-scheme-lg.png)

## Further Readings

- [Bye bye Mongo, Hello Postgres](https://www.theguardian.com/info/2018/nov/30/bye-bye-mongo-hello-postgres)
- [Ask HN: Is MongoDB obsolote when Postgres and SQLite provide JSON types?](https://news.ycombinator.com/item?id=30464075)
- [Proposal: Add JSON support for Postgres](https://www.postgresql.org/message-id/e7e5fefd1003281348v6feb1730u7d43ccf011be6976%40mail.gmail.com)
- [Discussion for using BSON or JSONB for Postgres ](https://www.postgresql.org/message-id/CA%2BTgmoagjFfJst%3D9kSu4rZatCE8SRuOQCH_h-_YW%3D4_c687GTA%40mail.gmail.com)
- [Sharding & IDs at Instagram](https://instagram-engineering.com/sharding-ids-at-instagram-1cf5a71e5a5c)
- [Herding elephants: Lessons learned from sharding Postgres at Notion](https://www.notion.so/blog/sharding-postgres-at-notion)
- [How to Handle Database Migration / Schema Change](/blog/how-to-handle-database-schema-change)

## Other Comparisons

- [Postgres vs. MySQL](/blog/postgres-vs-mysql)
- [MySQL vs. MariaDB](/blog/mysql-vs-mariadb)
- [PlanetScale vs. Neon](/blog/planetscale-vs-neon)
- [Neon vs. Supabase](/blog/neon-vs-supabase)

The
Bytebase Blog

Explore the standard for database development

Solutions & Platform

Comparisons

Legal

Resources

Company