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<HintBlock type="info">

This post is maintained by Bytebase, an open-source database DevSecOps tool that can manage PostgreSQL. We
will constantly update this post to include the latest Postgres 18.x releases.

</HintBlock>

| Update History | Comment          |
| -------------- | ---------------- |
| 2025/05/13     | Initial version. |

PostgreSQL 18 ships on September 25, 2025. The release moves on async I/O, UUIDv7, and a set of long-requested operational fixes — adding `NOT NULL` without a full table scan, virtual generated columns, `RETURNING` with `old`/`new` aliases, and a faster `pg_upgrade` path. This post walks each feature and what it changes for the people running these databases.

## Release Timeline

- **PostgreSQL 18 Beta 1**: May 8, 2025
- **PostgreSQL 18 Beta 2**: July 17, 2025
- **PostgreSQL 18 Beta 3**: August 14, 2025
- **PostgreSQL 18 RC 1**: September 4, 2025
- **PostgreSQL 18 Official Release**: September 25, 2025

## Asynchronous I/O

PostgreSQL 18 introduces an asynchronous I/O subsystem, selected by the new `io_method` server variable. Linux installations can use `io_uring`; every platform gets a worker-based fallback. The first wave covers reads — sequential scans, bitmap heap scans, and VACUUM. A new system view, `pg_aios`, exposes the file handles in flight.

**DBA Note:** Reads only at first. Write-heavy OLTP will not see a step change. The largest gains should land on cloud databases with network-attached storage, where I/O latency dominates wall-clock time. Re-run baseline benchmarks before assuming existing capacity numbers still hold, and watch for new failure modes specific to the chosen I/O method.

## UUIDv7

The new `uuidv7()` function returns timestamp-ordered UUIDs. `uuidv4()` is now an alias for `gen_random_uuid()`.

**DBA Note:** Time-ordered UUIDs reduce B-tree page splits and keep hot indexes warmer. For application primary keys generated outside the database, this closes a long-standing gap against sequence-based integers without giving up global uniqueness.

## Improved EXPLAIN

`EXPLAIN ANALYZE` now includes `BUFFERS` by default. `EXPLAIN ANALYZE VERBOSE` adds WAL usage, CPU statistics, and per-row averages. `EXPLAIN (WAL)`, `VACUUM`/`ANALYZE (VERBOSE)`, and the autovacuum log all gain a full WAL buffer count.

**DBA Note:** `BUFFERS` by default lowers the bar for spotting cache misses during development. WAL and per-row averages in `VERBOSE` matter most when diagnosing replication lag and autovacuum throughput — both areas where the previous output left gaps.

## NOT NULL constraints as NOT VALID

`NOT NULL` can now be added with `NOT VALID`, then validated separately:

```sql
ALTER TABLE table_name ADD CONSTRAINT constraint_name NOT NULL (column) NOT VALID;
ALTER TABLE table_name VALIDATE CONSTRAINT constraint_name;
```

The initial `ADD CONSTRAINT` skips the table scan. Validation later runs under `ShareUpdateExclusiveLock`, so `SELECT`, `INSERT`, `UPDATE`, and `DELETE` proceed normally. The constraint still rejects new NULLs from the moment it is created.

<HintBlock type="info">

Before Postgres 18, the equivalent pattern was the less obvious `CHECK (column IS NOT NULL) NOT VALID`.

</HintBlock>

**DBA Note:** Adding a `NOT NULL` on a multi-terabyte table no longer requires an `ACCESS EXCLUSIVE` lock for the duration of the scan. New rows are protected immediately; historical cleanup happens on your schedule. One fewer reason to book a maintenance window.

## OLD/NEW values in RETURNING

`RETURNING` accepts changeable `old` and `new` aliases. `INSERT ... ON CONFLICT` can return prior values. `UPDATE` can return both old and new. `DELETE` can surface values produced by an `ON DELETE` row assignment.

**DBA Note:** Application code that needed before-and-after state previously read the row first or attached a trigger. The aliases fold that into the original statement, which removes a category of race conditions and a familiar set of trigger workarounds.

## Virtual generated columns

Generated columns are now virtual by default — computed on read rather than stored. `STORED` remains available when materialization is required. Stored generated columns can now be logically replicated.

**DBA Note:** Disk and write overhead drop for derived columns that do not need persistence — concatenations, formatted output, simple arithmetic. Reads pay the compute cost on each access, so expressions that are expensive or sit on hot read paths should stay `STORED`. Logical replication of stored columns lines up with what downstream consumers typically expect.

## Major version upgrades

The headline change for upgrade operators is preserved planner statistics. The newly upgraded cluster keeps its plans without waiting for `ANALYZE` to repopulate. `pg_upgrade --jobs` parallelizes pre-upgrade checks; `--swap` exchanges directories instead of copying, which is the difference between minutes and hours on large installations.

The 18 release also lands several incompatibilities. Time zone abbreviation handling, MD5 password deprecation, `VACUUM`/`ANALYZE` inheritance behavior, and `COPY FROM` end-of-file handling all changed. Each is small in isolation; together they are where existing automation tends to break.

**DBA Note:** Statistics persistence removes one of the most common post-upgrade incident patterns — query plans regressing under load while `ANALYZE` catches up. `--swap` is the lever to pull when copy time dominated past upgrade windows. Test the incompatibilities in staging; upgrades remain the place where the unread footnote causes pages.

## Further Readings

- [Postgres 18 full release notes](https://www.postgresql.org/docs/18/release-18.html)
- [Developer's perspective about Postgres 18](/blog/what-is-new-in-postgres-18-for-developer)



<HintBlock type="info">

This post is maintained by Bytebase, an open-source database DevSecOps tool which supports amazon rds and some other alternatives.

</HintBlock>

| Update History | Comment            |
| -------------- | ------------------ |
| 2025/05/21     | Initial version.   |

Amazon's Relational Database Service (RDS) is a popular managed database service that simplifies database administration tasks. However, organizations often explore alternatives due to cost concerns, specialized requirements, or architectural preferences. This article provides a concise overview of RDS alternatives, covering both **self-hosted** solutions and other **cloud providers**' offerings.

## Understanding Amazon RDS: Benefits and Limitations

Amazon RDS, launched in 2009, provides managed database services for popular engines like MySQL and PostgreSQL. It handles routine tasks such as provisioning, patching, backups, and monitoring, allowing teams to focus on application development.

It supports the following database engines:
- **Open Source:** MySQL, PostgreSQL, MariaDB, and Amazon Aurora (MySQL-Compatible and PostgreSQL-Compatible Editions).
- **Commercial:** SQL Server, Oracle, and IBM Db2.

**Key Benefits:**
- Automatic software patching and backups
- Read replicas for improved performance
- Multi-availability zone deployments
- Integration with other AWS services

**Limitations:**
- Vertical scaling constraints despite high costs
- Complex pricing model leading to unexpected expenses
- Limited customization for specialized workloads
- Restricted administrative access to underlying systems

## AWS Alternatives

### Amazon Aurora

Amazon Aurora is a cloud-native relational database service developed by AWS. It is compatible with MySQL and PostgreSQL databases and offers enhanced performance, reliability, and scalability compared to traditional RDS instances.

**Key Features:**

- Distributed, fault-tolerant architecture that replicates data across multiple availability zones
- Up to 5x better throughput than MySQL on RDS and up to 3x better performance than PostgreSQL on RDS
- Automatic storage scaling and database replication
- Available in standard provisioned and serverless configurations

Aurora was designed and built from the ground up to take advantage of the cloud, with numerous improvements to the database engine implemented by AWS themselves. It automatically handles tasks like database replication, failover, and recovery, making it a powerful alternative to standard RDS.

## Other Hyperscale Alternatives

### GCP Cloud SQL

Google Cloud SQL is Google's fully managed relational database service that makes it easy to set up, maintain, manage, and administer relational databases on Google Cloud Platform.

**Key Features:**

- Supports PostgreSQL, MySQL, and SQL Server database engines
- Handles routine database tasks such as backups and patch management
- Integrates seamlessly with other Google Cloud services
- Offers automatic storage increases and high availability configuration
- Straightforward pricing model that some users find more transparent than AWS

Cloud SQL follows a traditional managed database model with managed VMs and persistent disk storage. It's particularly well-suited for organizations already invested in the Google Cloud ecosystem or those looking for a straightforward managed database service with predictable pricing.

### GCP AlloyDB for PostgreSQL

AlloyDB is Google's high-performance, PostgreSQL-compatible database service designed specifically for demanding enterprise workloads.

**Key Features:**

- Cloud-native architecture with disaggregated compute and storage
- Up to 4x faster for transactions and 100x faster for analytics compared to standard PostgreSQL
- Built-in columnar engine for hybrid transactional/analytical processing (HTAP)
- Fully elastic storage/compute with auto-scaling capabilities
- 99.99% SLA with sub-minute failover and zero-downtime maintenance

AlloyDB uses a modern, cloud-native architecture with separate compute and storage layers, delivering high performance and scalability specifically for PostgreSQL. It includes a distributed storage system built for PostgreSQL, independent compute scaling with smart caching, and a built-in columnar engine for analytics.

AlloyDB is the better choice for enterprise PostgreSQL workloads, HTAP (Hybrid Transactional/Analytical Processing), and mission-critical systems, while Cloud SQL is more suitable for general-purpose applications and multi-engine needs.

### Azure SQL Database

Microsoft Azure offers several managed database services that compete with RDS, including Azure Database for PostgreSQL, Azure Database for MySQL, and Azure SQL Database (for Microsoft SQL Server).

**Key Features:**

- Automated backups, high availability, and security features
- Integration with other Azure services
- Intelligent performance recommendations and advanced threat protection
- Geo-redundant backups and flexible scaling options

Azure's database offerings provide similar benefits to RDS and integrate well with other Azure services, making them a natural choice for organizations already using Microsoft's cloud platform. For organizations heavily invested in Microsoft technologies or those using hybrid cloud architectures with on-premises Microsoft infrastructure, Azure's database services offer seamless integration and familiar management interfaces.

## Other DBaaS provider

### DigitalOcean

DigitalOcean Managed Databases provides a simplified database service for PostgreSQL, MySQL, Redis, and MongoDB. It's designed with developers in mind, offering a straightforward setup process and user-friendly management interface.

**Key Features:**

- Automatic backups and standby nodes for high availability
- End-to-end encryption and horizontal scaling through read-only nodes
- Transparent pricing model and developer-friendly features
- Simplified management compared to larger cloud providers

DigitalOcean's offering is particularly well-suited for small to medium-sized businesses, startups, and individual developers looking for a cost-effective and easy-to-use managed database service without the complexity of larger cloud providers.

DigitalOcean's managed PostgreSQL service starts at $15 per month for a basic instance with 1GB of RAM and 10GB of storage, making it an affordable option for smaller workloads.

### Aiven

Aiven is a fully managed open-source database service provider that offers PostgreSQL, MySQL, Redis, Kafka, and other data infrastructure services across all major cloud platforms.

**Key Features:**

- Multi-cloud deployment options (AWS, GCP, Azure, DigitalOcean, etc.)
- High availability with automatic backups and failover
- Advanced monitoring and metrics
- Enterprise-grade security with encryption and VPC peering
- Seamless scaling and version upgrades

Aiven's PostgreSQL service includes all the extensions you might need out of the box and is now AI-optimized. Their platform allows you to deploy and manage databases across different cloud providers from a single interface, providing flexibility and avoiding vendor lock-in.

Aiven is particularly suitable for organizations that want to leverage open-source technologies with professional management and support, or those looking for a consistent database experience across multiple cloud environments.

### Timescale Cloud

Timescale Cloud is a fully managed, cloud-native database service built on PostgreSQL, tailored for time-series and analytics workloads. It extends PostgreSQL with features optimized for handling time-stamped data, making it ideal for applications like IoT, financial analytics, and monitoring systems.

**Key Features:**

- Time-Series Optimizations: Utilizes hypertables and continuous aggregates for efficient storage and querying of time-series data.
- Data Compression: Achieves up to 95% storage savings through native compression techniques.
- Performance: Offers up to 350x faster queries and 44% faster data ingestion compared to Amazon RDS PostgreSQL for time-series workloads.
- Managed Service: Provides automated backups, high availability, and seamless scaling without manual intervention.
- Developer-Friendly: Maintains full SQL support, allowing developers to leverage existing PostgreSQL tools and expertise.

For organizations dealing with large volumes of time-series data, Timescale Cloud offers a performant and cost-effective solution that combines the reliability of PostgreSQL with specialized time-series capabilities.

## Cost Comparison (us-east-1)

To provide a clear comparison of database costs across different providers, below is pricing information for various RDS alternatives in the us-east-1 region (or equivalent). This comparison focuses on PostgreSQL offerings with similar specifications to enable direct comparison.

### Comparison Table for Basic PostgreSQL Instance (2 vCPU, 8GB RAM, 100GB Storage)

| Provider | Service | Monthly Cost (est.) | Pricing Model | Notable Features |
|----------|---------|---------------------|---------------|------------------|
| AWS | RDS PostgreSQL | $210-230 | Pay-as-you-go or reserved instances | Automatic backups, Multi-AZ option |
| AWS | Aurora PostgreSQL | $290-320 | Pay-as-you-go or reserved instances | Enhanced performance, auto-scaling storage |
| GCP | Cloud SQL | $190-210 | Pay-as-you-go | Straightforward pricing, seamless GCP integration |
| GCP | AlloyDB | $260-290 | Pay-as-you-go | 4x faster transactions, columnar engine for analytics |
| Azure | Database for PostgreSQL | $200-230 | Pay-as-you-go or reserved capacity | Intelligent performance recommendations |
| DigitalOcean | Managed PostgreSQL | $120-140 | Fixed monthly pricing | Simple pricing, developer-friendly |
| Aiven | PostgreSQL | $200-220 | Tiered plans with hourly billing | Multi-cloud deployment, all-inclusive pricing |
| Timescale | Timescale Cloud | $180-210 | Usage-based (compute + storage) | Time-series optimizations, columnar compression |

### Cost-Saving Considerations

1. **Reserved Instances/Commitments**: AWS, GCP, and Azure offer significant discounts (up to 60%) for 1-3 year commitments.

1. **Right-sizing**: Choosing the appropriate instance size for your workload can significantly reduce costs.

1. **Storage Optimization**: 
   - Timescale Cloud's columnar compression can reduce storage costs by up to 95% for time-series data
   - Aurora's storage auto-scaling only charges for what you use
   - DigitalOcean's fixed pricing includes storage allocations

1. **Serverless Options**:
   - Aurora Serverless v2 (minimum 0.5 ACUs at ~$0.06/hour in us-east-1)
   - AlloyDB Omni through Aiven (starting at $90/month)

1. **Free Tiers**:
   - AWS RDS: 750 hours of db.t4g.micro instance per month for 12 months
   - Azure: 12 months of free services for new customers
   - GCP: $300 free credit for new customers
   - Timescale Cloud: Free tier available for development

### Performance vs. Cost Considerations

When evaluating cost, it's important to consider performance benefits that may justify higher prices:

- **Aurora PostgreSQL**: 3x better performance than standard RDS PostgreSQL
- **AlloyDB**: 4x faster for transactions, 100x faster for analytics than standard PostgreSQL
- **Timescale Cloud**: Up to 350x faster queries for time-series data compared to RDS
- **DigitalOcean**: Lower performance but significantly lower cost for basic workloads

## Conclusion

When choosing an alternative to Amazon RDS, consider your specific workload requirements, budget constraints, and existing cloud investments. Each alternative offers distinct advantages:

- **Amazon Aurora** provides enhanced performance within the AWS ecosystem
- **GCP Cloud SQL** offers a straightforward managed service with multi-engine support
- **GCP AlloyDB** delivers high-performance PostgreSQL for enterprise workloads
- **Azure SQL Database** integrates seamlessly with Microsoft technologies
- **DigitalOcean** provides simplified, cost-effective database management
- **Aiven** offers multi-cloud flexibility with open-source technologies
- **Timescale Cloud** excels for time-series data with significant performance advantages

For specialized workloads like time-series data, purpose-built solutions like Timescale Cloud can offer substantial performance and cost benefits over general-purpose database services. From a cost perspective, DigitalOcean offers the most affordable option for predictable workloads, while AWS Aurora and GCP AlloyDB provide premium performance at higher price points.


As organizations increasingly adopt Infrastructure as Code (IaC) practices, securing these definitions becomes critical to prevent vulnerabilities from reaching production environments. IaC security tools help identify misconfigurations, compliance issues, and security risks early in the development lifecycle.

This article examines the leading open-source IaC security solutions to help you select the right tool for your requirements.

## Checkov

[Checkov](https://github.com/bridgecrewio/checkov) is a static code analysis tool for detecting security misconfigurations in infrastructure as code and performing software composition analysis.

![Checkov](/content/blog/top-open-source-iac-security-tools/checkov.webp)

Maintained by Prisma Cloud, Checkov analyzes multiple frameworks including Terraform, CloudFormation, Kubernetes, Dockerfile, and ARM templates. Its distinctive graph-based scanning detects security issues across interconnected resources, with over 1,000 built-in policies covering compliance frameworks and cloud provider guidelines.

The tool integrates with CI/CD pipelines and provides remediation suggestions for detected issues. Developers can suppress false positives using inline code comments (`checkov:skip`). Checkov's multi-framework support and comprehensive policy library make it effective for implementing security checks early in development.

## TFLint

[TFLint](https://github.com/terraform-linters/tflint) is a pluggable Terraform linter that detects possible errors, deprecated syntax, and enforces best practices in your Terraform code.

TFLint catches issues that Terraform's native validation misses by validating against actual cloud provider constraints (AWS, Azure, GCP). It identifies potential errors like invalid instance types or misconfigured security groups before deployment.

Its plugin architecture supports custom rules and provider-specific validations. TFLint integrates with IDEs, command-line workflows, and CI pipelines for quick feedback. While primarily focused on correctness rather than security scanning, it serves as a valuable complement to other security tools, especially in Terraform-centric environments.

## Trivy

[Trivy](https://github.com/aquasecurity/trivy) is a multi-purpose security scanner that detects vulnerabilities and misconfigurations in both containers and infrastructure code.

![Trivy](/content/blog/top-open-source-iac-security-tools/trivy.webp)

Trivy scans across multiple targets: container images, filesystems, git repositories, and various IaC formats. For infrastructure code, it identifies misconfigurations in Terraform, Kubernetes, CloudFormation, and other formats using policies based on industry standards.

The tool combines container and IaC scanning in a single solution, making it valuable for DevSecOps pipelines. Trivy integrates with CI/CD systems and produces outputs in multiple formats (JSON, SARIF, HTML). Its regularly updated vulnerability database helps protect against new threats. This unified approach makes Trivy particularly useful for teams looking to consolidate security tooling across container and infrastructure environments.

## CloudSploit

[CloudSploit](https://github.com/aquasecurity/cloudsploit) is an open-source Cloud Security Posture Management (CSPM) tool that identifies security risks across major cloud providers.

![CloudSploit](/content/blog/top-open-source-iac-security-tools/cloudsploit.webp)

CloudSploit scans live cloud infrastructure environments to detect misconfigurations and security issues. Unlike static IaC scanners, it examines the actual deployed state of resources across AWS, Azure, GCP, and Oracle Cloud. This runtime analysis helps catch drift between defined infrastructure and production environments.

The tool features a plugin architecture with over 300 security checks and configurable risk levels. CloudSploit provides remediation guidance in its reports and supports multiple output formats including console, CSV, JSON, and JUnit XML. Its ability to monitor running infrastructure makes it particularly useful for continuous security assessment of cloud environments after deployment.

## KICS

[KICS](https://github.com/Checkmarx/kics) (Keeping Infrastructure as Code Secure) is an open-source solution that scans infrastructure code to find security vulnerabilities, compliance issues, and misconfigurations.

![KICS](/content/blog/top-open-source-iac-security-tools/kics.webp)

KICS uses a query-based approach to security scanning with over 1,500 predefined queries for multiple IaC frameworks. It supports Terraform, Kubernetes, Docker, CloudFormation, Ansible, Bicep, and others through a unified scanning engine. The tool covers security, compliance, and infrastructure reliability checks.

Its key feature is the extensible query engine that allows teams to define custom rules using a simplified query language based on Open Policy Agent. KICS provides detailed vulnerability explanations with remediation guidance and severity ratings. It integrates with CI/CD systems and offers multiple output formats for reporting. This makes KICS especially useful for organizations working with diverse infrastructure definitions that need customizable security rules.

## Conclusion

| Tool        | Best For                     | Coverage                 | Integration Capabilities | Key Features                               |
| ----------- | ---------------------------- | ------------------------ | ------------------------ | ------------------------------------------ |
| Checkov     | Multi-framework scanning     | Multiple IaC frameworks  | CI/CD pipelines          | Graph-based scanning, 1,000+ policies      |
| TFLint      | Terraform validation         | Terraform                | IDE integration          | Provider-specific rules, plugin system     |
| Trivy       | Unified security scanning    | Container and IaC        | CI/CD systems            | Regularly updated vulnerability database   |
| CloudSploit | Runtime security assessment  | Multi-cloud environments | Notification systems     | 300+ checks, configuration drift detection |
| KICS        | Customizable security checks | Multiple IaC frameworks  | CI/CD integration        | 1,500+ queries, OPA-based rule engine      |

Each tool serves different security needs:

- **Checkov**: Provides comprehensive policy coverage across multiple frameworks with graph-based analysis
- **TFLint**: Specializes in Terraform-specific validation against actual cloud provider constraints
- **Trivy**: Offers unified scanning for both infrastructure code and containers in a single tool
- **CloudSploit**: Focuses on runtime scanning of deployed cloud resources across major providers
- **KICS**: Features an extensive, customizable query engine for diverse infrastructure definitions

Integrating IaC security early in development is essential for maintaining secure infrastructure at scale. When selecting an IaC security tool, you may consider these factors above.



<HintBlock type="info">

This post is maintained by Bytebase, an open-source database DevSecOps tool that can manage PostgreSQL hosted in any providers. We update the post every year.

</HintBlock>

| Update History | Comment          |
| -------------- | ---------------- |
| 2026/04/01     | Add PlanetScale Postgres. |
| 2025/05/06     | Initial version. |

PostgreSQL remains a top choice for relational databases in 2025 due to its reliability, extensibility, and active open-source community. With many hosting options available, choosing the right provider is key to balancing performance, scalability, and cost.

This article compares PostgreSQL hosting solutions from major clouds (AWS, GCP, Azure) and specialized providers (DigitalOcean, Aiven, Neon, PlanetScale, Supabase, Timescale, Heroku), focusing on pricing models and key cost factors to help you make an informed decision.

## Understanding Pricing Models

Before comparing providers, it’s important to understand the main cloud database pricing models:

**Instance-Based:** Pay hourly for fixed vCPU/RAM; storage and I/O billed separately. Common with major clouds.

**Usage-Based/Serverless:** Pay only for what you use — compute, storage, I/O, and data transfer. Ideal for variable workloads.

**Tiered Plans:** Fixed-price bundles from specialized providers offer predictable costs but less flexibility.

Most providers mix these models, with key cost factors including compute, storage, data transfer, backups, and high availability.

## Comparison Table

| Provider             | Entry-Level (Monthly)                   | Mid-Range (Monthly)                         | Enterprise-Level (Monthly)                   | Free Tier Available |
| -------------------- | --------------------------------------- | ------------------------------------------- | -------------------------------------------- | ------------------- |
| **AWS RDS**          | $12.41 (db.t4g.micro: 2 vCPU, 1 GiB)    | $99.28 (db.m6g.large: 2 vCPU, 8 GiB)        | $794.24 (db.m6g.4xlarge: 16 vCPU, 64 GiB)    | Yes (12 months)     |
| **AWS Aurora**       | $59.86 (db.t4g.medium: 2 vCPU, 4 GiB)   | $211.70 (db.r6g.large: 2 vCPU, 16 GiB)      | $1,693.60 (db.r6g.4xlarge: 16 vCPU, 128 GiB) | Yes (12 months)     |
| **Google Cloud SQL** | $11.32 (db-f1-micro: 0.2 vCPU, 0.6 GiB) | $122.49 (db-n1-standard-2: 2 vCPU, 7.5 GiB) | $980.03 (db-n1-standard-16: 16 vCPU, 60 GiB) | Yes ($300/90 days)  |
| **Google AlloyDB**   | $143.68 (2 vCPU, 16 GiB)                | $301.44 (4 vCPU, 32 GiB)                    | $1,209.60 (16 vCPU, 128 GiB)                 | No                  |
| **Azure PostgreSQL** | $14.60 (B1ms: 1 vCPU, 2 GiB)            | $99.28 (GP_Gen5_4: 4 vCPU, 16 GiB)          | $794.24 (GP_Gen5_32: 32 vCPU, 128 GiB)       | Yes (12 months)     |
| **DigitalOcean**     | $15.00 (1 vCPU, 1 GiB)                  | $60.00 (2 vCPU, 4 GiB)                      | $244.00 (6 vCPU, 16 GiB)                     | No                  |
| **Aiven**            | Free (2 vCPU, 1 GiB)                    | $110 (4 vCPU, 8 GiB)                        | $435 (8 vCPU, 16 GiB)                        | Yes                 |
| **Neon**             | Free (1 vCPU, 1 GiB)                    | $59 (2 vCPU, 4 GiB)                         | $299 (8 vCPU, 16 GiB)                        | Yes                 |
| **Supabase**         | Free (1 vCPU, 0.5 GiB)                  | $25 (2 vCPU, 1 GiB)                         | $99 (4 vCPU, 8 GiB)                          | Yes                 |
| **Timescale**        | Free (1 vCPU, 1 GiB)                    | $89 (2 vCPU, 4 GiB)                         | $499 (8 vCPU, 16 GiB)                        | Yes                 |
| **PlanetScale Postgres** | $5 (PS-5: 1/16 vCPU, 512 MiB)       | $117 (PS-160: 2 vCPU, 16 GiB)               | $467 (PS-640: 8 vCPU, 64 GiB)                | No                  |
| **Heroku Postgres**  | Free (0.5 vCPU, 512 MiB)                | $50 (1 vCPU, 1 GiB)                         | $500+ (8 vCPU, 16 GiB)                       | Yes                 |

## Major Cloud Providers: The Big Three

The top public clouds — **AWS**, **Google Cloud**, and **Azure** — offer powerful managed PostgreSQL services. They provide flexibility and performance, but with complex pricing models.

### AWS

AWS offers two primary managed PostgreSQL options: Amazon RDS for PostgreSQL and Amazon Aurora (PostgreSQL-compatible edition).

#### RDS for PostgreSQL

- 💰 **Pricing**: Instance-based (e.g., `db.t3.micro`, `db.m6g.large`)
- 📦 **Storage**: gp3 (scalable) or io1/io2 (high-performance)
- 🛡️ **High Availability**: Multi-AZ; doubles instance cost
- 💾 **Backups**: Free up to DB size; extra charged per GB-month
- 🧮 **Discounts**: Reserved Instances, Savings Plans
- 💵 **Entry Point**: Very low (e.g., `db.t3.micro` + minimal storage)

#### Aurora PostgreSQL-Compatible

- 💰 **Pricing**: Cluster-based, compute + storage + I/O billed separately
- ⚙️ **Compute**: Per instance-hour or per ACU-hour (Serverless v2)
- 📦 **Storage**: Auto-scaling, charged per GB-month
- 🔁 **I/O**: Pay-per-request (Standard) or free (I/O-Optimized)
- 🛡️ **High Availability**: Built-in, cross-AZ replication
- 💾 **Backups**: Continuous to S3
- 💵 **Entry Point**: Higher than RDS; designed for heavier workloads

### Google Cloud

GCP provides two main managed PostgreSQL offerings: the standard Cloud SQL for PostgreSQL and the higher-performance AlloyDB for PostgreSQL.

#### Cloud SQL for PostgreSQL

- 💰 **Pricing**: Instance-based (`db-f1-micro`, `db-n1-standard-2`, etc.)
- ⚙️ **Compute**: Billed per vCPU + RAM hour
- 📦 **Storage**: SSD or HDD, per GB-month
- 🛡️ **High Availability**: Regional standby; doubles cost
- 💾 **Backups**: Charged per GB-month
- 🧮 **Discounts**: Committed Use Discounts (CUDs)
- 💵 **Entry Point**: Very low (`db-f1-micro`)

#### AlloyDB for PostgreSQL

- 💰 **Pricing**: Cluster-based with separate compute + storage
- ⚙️ **Compute**: Per vCPU + RAM hour
- 📦 **Storage**: Auto-scaling, includes I/O
- 🛡️ **High Availability**: Built-in, 99.99% SLA
- 💾 **Backups**: Continuous with PITR
- 💵 **Entry Point**: Higher (min: 2 vCPU, 16 GiB RAM)

### Microsoft Azure

Azure's primary offering for managed PostgreSQL is Azure Database for PostgreSQL, available in different deployment models. The recommended and most versatile option is the Flexible Server model.

#### PostgreSQL Flexible Server

- 💰 **Pricing**: Compute + Storage
- ⚙️ **Tiers**:
  - **Burstable**: Low-cost (e.g., B1ms: 1 vCPU, 2 GiB RAM)
  - **General Purpose**: Balanced (D-series)
  - **Memory Optimized**: For in-memory workloads (E-series)
- 📦 **Storage**: Per GB-month with baseline and optional IOPS
- 🛡️ **High Availability**: Optional zone-redundant (doubles cost)
- 💾 **Backups**: Configurable, per GB-month
- 🧮 **Discounts**: Reserved Capacity (up to 70% off compute)
- 💵 **Entry Point**: Very low (B-series)

## Specialized Managed PostgreSQL Providers

Beyond the big three cloud providers, numerous companies offer specialized managed PostgreSQL hosting. These often focus on developer experience, specific features (like serverless or time-series), or simplified pricing, sometimes building on top of the major cloud infrastructure.

### DigitalOcean

DigitalOcean offers Managed Databases for PostgreSQL, focusing on simplicity and predictable pricing.

- 💰 **Model**: Tiered monthly plans
- 💵 **Entry Point**: $15/month (1 vCPU, 1 GiB RAM, 10 GB SSD)
- 📦 **Storage**: Included in the plan
- 🌐 **Bandwidth**: 1 TB outbound included; overage billed
- 🛡️ **High Availability**: Optional standby node (doubles cost)
- 💾 **Backups**: Daily backups + 7-day PITR
- 🆓 **Free Tier**: ❌ Not available

### Aiven

Aiven provides managed PostgreSQL (and other open-source data services) hosted on major cloud providers (AWS, GCP, Azure, DigitalOcean, UpCloud).

- 💰 **Model**: Tiered plans by cloud & region
- 💵 **Entry Point**: Free (2 vCPU, 1 GiB RAM, 1–5 GB SSD); paid plans from ~$19/month
- 📦 **Storage & Network**: Included in plan; generous allowances
- 🛡️ **High Availability**: Included in Business and Premium tiers
- 💾 **Backups**: Automatic backups included
- 🆓 **Free Tier**: ✅ Yes

### Neon

Neon offers a serverless PostgreSQL platform with a unique architecture separating storage and compute.

- 💰 **Model**: Serverless, usage-based
- 💵 **Entry Point**: Free (shared vCPU, 10 GB storage)
- ⚙️ **Compute**: Billed only when active; scales to zero when idle
- 📦 **Storage**: Billed per GB-month and GB written
- 🛡️ **High Availability**: Built into platform architecture
- 💾 **Backups**: Includes time-travel and PITR
- 🆓 **Free Tier**: ✅ Yes

### PlanetScale Postgres

PlanetScale offers a managed PostgreSQL service with resource-based pricing, pro-rated to the millisecond.

- 💰 **Model**: Resource-based, pro-rated to the millisecond
- 💵 **Entry Point**: $5/month (1/16 vCPU, 10 GB storage)
- ⚙️ **Compute**: Billed only when active; scales to zero when idle
- 📦 **Storage**: 10 GB included, then billed per GB-month
- 🛡️ **High Availability**: Built-in, cross-AZ replication
- 💾 **Backups**: 2x per day included + PITR
- 🆓 **Free Tier**: ❌ No, but startup credits available case-by-case

### Supabase

Supabase positions itself as an open-source Firebase alternative, providing a suite of backend tools, including a dedicated PostgreSQL database for each project.

- 💰 **Model**: Tiered plans + usage-based overages
- 💵 **Entry Point**: Free (shared DB, 500 MB); Pro starts at $25/month
- ⚙️ **Compute & Storage**: Varies by tier; add-ons available
- 🛡️ **High Availability**: Included in Team and Enterprise tiers
- 💾 **Backups**: PITR; retention depends on tier
- 🆓 **Free Tier**: ✅ Yes

### Timescale Cloud

Timescale Cloud is a managed PostgreSQL platform optimized for time-series data, built upon the TimescaleDB extension, but fully compatible with standard PostgreSQL.

- 💰 **Model**: Pure usage-based (compute + storage)
- 💵 **Entry Point**: ~$0.02/hr (smallest compute unit)
- 📦 **Storage**: Includes hypertables + regular tables
- 🛡️ **High Availability**: Optional (adds to cost)
- 💾 **Backups**: Continuous with PITR
- 🆓 **Free Tier**: ✅ $300 in trial credits

### Heroku Postgres

Heroku Postgres is one of the original and well-known managed PostgreSQL services, tightly integrated with the Heroku PaaS.

- 💰 **Model**: Tiered hourly/monthly billing
- 💵 **Entry Point**: $5/month (Essential-0)
- 📦 **Storage**: Included based on plan
- 🛡️ **High Availability**: Premium and above tiers only
- 💾 **Backups**: PG Backups (free); PITR on paid tiers
- 🆓 **Free Tier**: ✅ Yes (Essential-0, limited)

## Conclusion

In 2025, PostgreSQL hosting ranges from flexible cloud platforms (AWS, GCP, Azure) to simple, developer-focused options (Neon, PlanetScale, Supabase, Aiven, etc.).

Cloud providers offer power and scale, but with complex pricing. Specialized hosts are easier to use, often with free tiers and clear pricing. Match your choice to workload needs, budget, and team expertise. Start small, test with free plans, and scale as needed.



As organizations scale their database operations, the need for proper governance becomes critical. Database governance tools help teams manage database changes safely, enforce access control policies, track schema migrations, and maintain compliance.

This article examines the leading open-source database governance solutions to help you select the right tool for your requirements.

## OpenMetadata

[OpenMetadata](https://github.com/open-metadata/OpenMetadata) is an open-source platform for data discovery, observability, and governance built around a central metadata repository.

![OpenMetadata](/content/blog/top-open-source-database-governance-tools/openmetadata.webp)

OpenMetadata offers comprehensive metadata management with powerful search capabilities and rich data context. Its standout features include column-level lineage tracking, automated data quality monitoring, and a no-code profiling system that builds trust in organizational data assets.

The platform provides collaborative workflows for metadata enrichment, detailed data insights, and sophisticated classification for governance requirements. Through its extensible connector framework, OpenMetadata integrates with over 80 data sources across databases, dashboards, messaging, and pipelines. It has become a leading solution for organizations seeking modern, API-first metadata management.

## DataHub

[DataHub](https://github.com/datahub-project/datahub) is an open-source metadata platform for the modern data stack, enabling data discovery, observability, and governance.

![DataHub](/content/blog/top-open-source-database-governance-tools/datahub.webp)

DataHub employs a centralized metadata model to create consistent context across diverse data ecosystems. The platform features powerful search, end-to-end lineage tracking, and a comprehensive set of integrations spanning databases, BI tools, and data processing frameworks.

Its strengths include a robust GraphQL API, fine-grained access controls, automated metadata ingestion, and customizable metadata models. DataHub's microservices architecture supports scalable deployment options from single-docker to Kubernetes. With an active LFAI foundation project status, DataHub suits organizations requiring flexible, enterprise-grade metadata management well.

## Apache Atlas

[Apache Atlas](https://github.com/apache/atlas) is a scalable governance and metadata framework for enterprise data assets that provides open metadata management and governance capabilities.

![Apache Atlas](/content/blog/top-open-source-database-governance-tools/atlas.webp)

Apache Atlas provides a strong foundation for data governance with its extensible type system designed to model business metadata. It excels in highly regulated environments where detailed data lineage and classification are critical. The platform offers comprehensive services for data discovery, classification, and auditing with a focus on Hadoop ecosystem compatibility.

Key strengths include its robust security integration with Apache Ranger, flexible metadata classification system, and comprehensive audit capabilities for compliance reporting. Atlas provides APIs and hooks for integration with various data sources and destinations. Atlas remains a solid choice for organizations with complex governance requirements, particularly those with significant investments in the Hadoop ecosystem.

## Amundsen

[Amundsen](https://github.com/amundsen-io/amundsen) is an open-source data discovery and metadata platform that improves data accessibility within organizations.

![Amundsen](/content/blog/top-open-source-database-governance-tools/amundsen.webp)

Amundsen employs a PageRank-inspired algorithm to surface relevant data assets based on usage patterns. The platform offers an intuitive search interface and rich metadata presentation, making data discovery straightforward even in complex environments.

Amundsen features native integrations with popular data sources, quality monitoring tools, and documentation systems like Confluence. Its microservice architecture (with frontend, metadata, and search services) ensures scalability and flexibility. While more focused on discovery than comprehensive governance, Amundsen provides an accessible entry point for organizations beginning their metadata management journey.

## Bytebase

[Bytebase](https://github.com/bytebase/bytebase) is an open-source database DevSecOps platform that focuses on operational governance — controlling how database changes are made, who can make them, and maintaining a full audit trail.

While the tools above focus on metadata discovery and cataloging, Bytebase governs the operational side: change review, deployment approval, access control, and compliance auditing. It supports 25+ databases including PostgreSQL, MySQL, SQL Server, Oracle, and MongoDB.

Key governance capabilities:

- **Change review and approval** — every DDL/DML change goes through a structured workflow with [200+ SQL review rules](https://docs.bytebase.com/sql-review/review-rules/) enforced before execution. Enterprise tier adds [custom multi-tier approval workflows](https://docs.bytebase.com/change-database/approval/).
- **Role-based access control** — workspace and project-level roles control who can view, query, or modify each database. Pro tier adds SSO integration.
- **[Audit logging](https://docs.bytebase.com/security/audit-log/)** — every query, schema change, login, and permission change is logged with the real user's identity, timestamp, and full SQL text. Available on Pro and Enterprise.
- **[Dynamic data masking](https://docs.bytebase.com/security/data-masking/overview/)** — mask sensitive columns in query results based on user roles and semantic types, without changing the underlying data. Enterprise tier.
- **[Just-in-time data access](/blog/just-in-time-database-access/)** — grant temporary, time-limited access to sensitive databases for emergency debugging, with automatic expiration.
- **Multi-environment deployment** — enforce dev → staging → production pipelines so changes are tested before reaching production.

Bytebase is MIT-licensed with commercial features (Enterprise) for advanced governance needs. It complements metadata platforms like OpenMetadata or DataHub — they catalog what data exists; Bytebase governs how it's changed and accessed.

## Conclusion

- **OpenMetadata** excels with its modern architecture and comprehensive connector ecosystem, ideal for organizations seeking an API-first approach with rich collaboration features;
- **DataHub** provides enterprise-grade scalability with strong lineage capabilities, making it suitable for complex data environments requiring detailed context;
- **Apache Atlas** remains the go-to solution for Hadoop-centric organizations and highly regulated industries needing robust classification and security integration;
- **Amundsen** offers the most accessible entry point for teams prioritizing data discovery and user adoption over comprehensive governance;
- **Bytebase** covers the operational governance side — change review, approval workflows, access control, audit logging, and data masking — complementing the metadata-focused tools above.

As database estates grow increasingly complex, these open-source tools continue to evolve to meet emerging challenges. Many organizations start with targeted implementations addressing specific pain points before expanding to full governance frameworks.

The future of database governance lies in automation, AI-assisted metadata management, and seamless integration across the entire data lifecycle. Whichever tool you choose, establishing strong governance practices early will pay dividends as your data ecosystem grows.

## FAQ

**What is database governance?**

Database governance is the set of policies, processes, and tools that control how databases are managed across an organization. It covers two areas: metadata governance (cataloging what data exists, who owns it, and how it flows) and operational governance (controlling who can change or access the data, enforcing review before deployment, and maintaining audit trails).

**What is the difference between metadata governance and operational governance?**

Metadata governance tools (OpenMetadata, DataHub, Apache Atlas) focus on data discovery, cataloging, lineage tracking, and classification. Operational governance tools (Bytebase) focus on change management, access control, deployment approval, and audit logging. Most organizations need both — one to understand their data, the other to control changes to it.

**Which database governance tool should I choose?**

It depends on your primary challenge. If you need data discovery, lineage tracking, and metadata cataloging, start with OpenMetadata or DataHub. If you need to control database changes, enforce SQL review, manage access permissions, and maintain an audit trail for compliance, start with Bytebase. For comprehensive governance, combine a metadata platform with an operational governance tool.



Modern organizations need efficient tools to manage their complex workflows. Workflow orchestration tools help coordinate tasks, handle errors, and monitor execution processes.

This article compares the leading open-source workflow orchestration solutions to help you find the best fit for your requirements.

## Apache Airflow

[Apache Airflow](https://airflow.apache.org/) is a platform for developing, scheduling, and monitoring batch-oriented workflows. It enables users to define workflows as Directed Acyclic Graphs (DAGs) of tasks in Python.

![Apache Airflow](/content/blog/top-open-source-workflow-orchestration-tools/airflow.webp)

Airflow excels with scheduled batch processes and appeals to Python-oriented teams. It offers strong dependency management, extensive integrations, and comprehensive monitoring through its web UI. Its large, active community provides extensive documentation, plugins, and support.

The platform is optimized for scheduled, finite workflows rather than streaming or event-driven workloads. It can be resource-intensive compared to newer alternatives and requires significant Python knowledge to use effectively.

## Temporal

[Temporal](https://github.com/temporalio/temporal) is a workflow orchestration platform built for reliability in microservices and business processes. It evolved from Uber's Cadence project and employs event sourcing to track workflow state.

![Temporal](/content/blog/top-open-source-workflow-orchestration-tools/temporal.webp)

Temporal specializes in durable, long-running processes with built-in fault tolerance. Its key strengths include stateful workflow execution, type-safe SDKs for multiple languages (Go, Java, PHP, TypeScript), and exactly-once execution guarantees. The platform excels in scenarios requiring complete execution history and audit trails.

The system requires running a dedicated server and has a steeper learning curve for teams unfamiliar with event sourcing patterns. While newer than Airflow, Temporal has gained significant adoption for mission-critical applications where reliability is paramount.

## Dagster

[Dagster](https://dagster.io/) is a modern data orchestrator built for engineers creating data and AI platforms. It puts data assets at the center of workflows rather than focusing solely on tasks.

![Dagster](/content/blog/top-open-source-workflow-orchestration-tools/dagster.webp)

Dagster excels with data-aware pipelines where quality and dependencies are primary concerns. Its asset-centric approach makes data products more manageable and observable. The platform offers end-to-end lineage tracking, rich metadata, and native dbt integration. Dagster brings software engineering practices to data workflows with features like branch deployments for testing, comprehensive observability, and cost insights. It integrates seamlessly with popular data tools while providing a single interface for developing, testing, and monitoring pipelines.

## Prefect

[Prefect](https://www.prefect.io/) is a Python-native workflow orchestration tool for building, scheduling and monitoring data pipelines. It transforms standard code into fault-tolerant dataflows with minimal changes.

![Prefect](/content/blog/top-open-source-workflow-orchestration-tools/prefect.webp)

Prefect specializes in dynamic, event-driven workflows with an intuitive Python API. Its decoupled design allows workflows to run anywhere (locally, in CI/CD, on VMs, or in Kubernetes) with consistent behavior. The platform features task caching, automatic retries, state handling, and real-time observability.

Key strengths include asynchronous execution, concurrent task runs, and support for complex flow patterns. Prefect Cloud provides a managed service option, while Prefect Server enables self-hosting. With over 15,000 GitHub stars and a growing community, Prefect has established itself as a modern alternative to traditional orchestrators.

## Flyte

[Flyte](https://github.com/flyteorg/flyte) is a Kubernetes-native workflow orchestration platform that unifies data processing, ML, and analytics pipelines focusing on scalability, reproducibility, and type safety.

![Flyte](/content/blog/top-open-source-workflow-orchestration-tools/flyte.webp)

Flyte excels with compute-intensive workloads requiring strong reproducibility guarantees. Key strengths include strong type checking, immutable executions, and automated data lineage. The platform features GPU acceleration, container-based dependency isolation, dynamic branching, and efficient parallel task execution.

While it requires Kubernetes knowledge, its robust type system and reproducibility features make it particularly valuable for machine learning and data science teams building production-grade pipelines.

## Argo Workflows

[Argo Workflows](https://github.com/argoproj/argo-workflows) is a Kubernetes-native workflow engine for orchestrating parallel jobs using DAGs. It's part of the Argo suite that includes Argo CD, Argo Events, and Argo Rollouts.

![Argo Workflows](/content/blog/top-open-source-workflow-orchestration-tools/argo.webp)

Argo Workflows excels at compute-intensive tasks requiring high parallelism. It leverages Kubernetes for resource management and container orchestration, making it ideal for ML training, data processing, and CI/CD pipelines. Key features include reusable workflow templates, artifact passing, conditional execution, and a user-friendly UI for monitoring.

The platform uses Kubernetes CRDs for workflow definitions and supports both YAML and Go/Python SDKs. With 13,000+ GitHub stars and adoption at companies like BlackRock, Intuit, and Red Hat, Argo Workflows has established itself as the leading Kubernetes-native orchestration solution.

## Conclusion

When choosing a workflow orchestration tool, consider these factors:

| Tool           | Best For                        | Learning Curve      | Maturity | Kubernetes Integration |
| -------------- | ------------------------------- | ------------------- | -------- | ---------------------- |
| Apache Airflow | General-purpose data workflows  | Moderate            | High     | Good                   |
| Temporal       | Long-running business processes | Moderate-High       | High     | Good                   |
| Dagster        | Data-aware pipelines            | Moderate            | Moderate | Good                   |
| Prefect        | Modern data workflows           | Low-Moderate        | Moderate | Good                   |
| Flyte          | ML workflows & reproducibility  | Moderate-High       | Moderate | Excellent              |
| Argo Workflows | Container-native parallelism    | High (requires K8s) | High     | Excellent              |

The landscape of open-source workflow orchestration tools offers solutions tailored to different needs:

- **Apache Airflow**: Mature ecosystem for batch-oriented workflows
- **Temporal**: Fault-tolerant execution for critical business processes
- **Dagster**: Asset-centric approach with strong data engineering practices
- **Prefect**: Flexible Python-native workflows with minimal overhead
- **Flyte**: Reproducible, strongly-typed ML and data pipelines
- **Argo Workflows**: High-performance parallel execution on Kubernetes

The best choice depends on your specific use cases, existing infrastructure, and team expertise. Many organizations even use multiple orchestration tools for different purposes, applying each tool to the workflows where it provides the most value.



<HintBlock type="info">

This post is maintained by Bytebase, an open-source database DevSecOps tool that can manage MySQL hosted in any providers. We update the post every year.

</HintBlock>

| Update History | Comment          |
| -------------- | ---------------- |
| 2026/04/01     | Update PlanetScale. |
| 2025/04/28     | Initial version. |

This concise guide compares pricing across seven major MySQL hosting providers: AWS RDS, AWS Aurora, Google Cloud SQL, Azure Database for MySQL, DigitalOcean, PlanetScale, and Aiven. Assuming you're familiar with these services, we'll focus on pricing structures, cost comparisons, and recommendations for different use cases.

## Pricing Comparison Tables

### Basic Monthly Pricing Comparison

| Provider         | Entry-Level                             | Mid-Range                                   | Enterprise-Level                             | Free Tier       |
| ---------------- | --------------------------------------- | ------------------------------------------- | -------------------------------------------- | --------------- |
| AWS RDS MySQL    | $12.41 (db.t4g.micro: 2 vCPU, 1 GiB)    | $99.28 (db.m6g.large: 2 vCPU, 8 GiB)        | $794.24 (db.m6g.4xlarge: 16 vCPU, 64 GiB)    | Yes (12 months) |
| AWS Aurora MySQL | $59.86 (db.t4g.medium: 2 vCPU, 4 GiB)   | $211.70 (db.r6g.large: 2 vCPU, 16 GiB)      | $1,693.60 (db.r6g.4xlarge: 16 vCPU, 128 GiB) | Yes (12 months) |
| Google Cloud SQL | $11.32 (db-f1-micro: 0.2 vCPU, 0.6 GiB) | $122.49 (db-n1-standard-2: 2 vCPU, 7.5 GiB) | $980.03 (db-n1-standard-16: 16 vCPU, 60 GiB) | Yes (90 days)   |
| Azure MySQL      | $14.60 (B1ms: 1 vCPU, 2 GiB)            | $99.28 (GP_Gen5_4: 4 vCPU, 16 GiB)          | $794.24 (GP_Gen5_32: 32 vCPU, 128 GiB)       | Yes (12 months) |
| DigitalOcean     | $15.15 (1 vCPU, 1 GiB)                  | $60.90 (2 vCPU, 4 GiB)                      | $244.35 (6 vCPU, 16 GiB)                     | No              |
| Aiven            | Free (2 vCPU, 1 GiB)                    | $110 (Startup tier: 2-4 vCPU, 4-8 GiB)      | $435 (Premium tier: 4-8 vCPU, 16-32 GiB)     | Yes             |
| PlanetScale      | $39 (1/8 vCPU, 1 GiB) - includes 1 primary and 2 replicas | $179 (1 vCPU, 8 GiB) - includes 1 primary and 2 replicas | $999 (8 vCPU, 32 GiB) - includes 1 primary and 2 replicas | No              |

_Entry-level options provide the most economical starting point, with Google Cloud SQL and AWS RDS offering the lowest prices. DigitalOcean, PlanetScale, and Aiven provide more predictable performance at this tier compared to the burstable instances from hyperscalers. Notably, Aiven offers a completely free tier suitable for development._

_PlanetScale has a locally-attached NVMe offering which has a much better price to performance ratio, but we have chosen not to include it here as the specs aren't directly comparable to their network-attached storage product._

![entry_level_pricing_chart](/content/blog/mysql-hosting-options-pricing-comparison/entry_level_pricing_chart.webp)

_The chart above illustrates the significant price difference between AWS Aurora and other providers at the entry-level tier. Aiven's free tier stands out as the most cost-effective option for development and testing environments._

### Mid-Range Pricing Comparison

![mid_range_pricing_chart](/content/blog/mysql-hosting-options-pricing-comparison/mid_range_pricing_chart.webp)

_At the mid-range tier, AWS Aurora is significantly more expensive than other options, while DigitalOcean offers the most economical solution. AWS RDS and Azure provide identical pricing at this tier, making them interchangeable from a pure cost perspective. PlanetScale is among the more expensive tiers at first glance, but their clusters include an HA setup by default with 1 primary and 2 replicas distributed across 3 AZs._

### Enterprise-Level Pricing Comparison

![enterprise_pricing_chart](/content/blog/mysql-hosting-options-pricing-comparison/enterprise_pricing_chart.webp)

_The enterprise tier shows the most dramatic pricing differences, with AWS Aurora costing more than twice as much as DigitalOcean. The significant price premium for Aurora reflects its advanced features and performance capabilities for mission-critical workloads._

### Storage Pricing

| Provider         | Storage Type               | Price per GB-month  | Performance                 |
| ---------------- | -------------------------- | ------------------- | --------------------------- |
| AWS RDS MySQL    | General Purpose SSD (gp3)  | $0.115              | 3,000 IOPS baseline         |
| AWS RDS MySQL    | Provisioned IOPS SSD (io1) | $0.125 + $0.10/IOPS | Up to 256,000 IOPS          |
| AWS Aurora MySQL | Aurora Storage             | $0.10               | Auto-scaling, distributed   |
| Google Cloud SQL | SSD                        | $0.17               | Scales with instance        |
| Google Cloud SQL | HDD                        | $0.09               | Lower performance           |
| Azure MySQL      | General Purpose            | $0.115              | Included IOPS based on size |
| Azure MySQL      | Business Critical          | $0.25               | Higher performance          |
| DigitalOcean     | SSD                        | Included in plan    | Varies by plan              |
| Aiven            | SSD                        | Included in plan    | Varies by plan              |
| PlanetScale      | General Purpose             | $0.50               | Scales with instance        |
| PlanetScale      | NVMe                        | Included            | Extreme performance; No IOPS limits |

_Storage pricing varies significantly between unbundled (AWS, Google, Azure) and bundled (DigitalOcean, Aiven) approaches. For high-performance workloads, AWS's provisioned IOPS offers the most control but at premium pricing. Google Cloud SQL has the highest SSD pricing, while Aurora offers good value with its distributed storage system._

![storage_pricing_chart](/content/blog/mysql-hosting-options-pricing-comparison/storage_pricing_chart.webp)

_The chart above shows that Azure Business Critical storage is significantly more expensive than other options at $0.25/GB-month, while Google Cloud SQL HDD offers the lowest unbundled price at $0.09/GB-month. AWS Aurora provides a good balance of performance and cost at $0.10/GB-month. Note that DigitalOcean and Aiven include storage in their base pricing, making them potentially more economical for storage-intensive workloads._

### Network Transfer Pricing

| Provider         | Inbound | Outbound (Same Region) | Outbound (Different Region) | Outbound to Internet |
| ---------------- | ------- | ---------------------- | --------------------------- | -------------------- |
| AWS RDS/Aurora   | Free    | Free                   | $0.01-$0.02/GB              | $0.09-$0.15/GB       |
| Google Cloud SQL | Free    | Free                   | $0.01-$0.08/GB              | $0.08-$0.15/GB       |
| Azure MySQL      | Free    | Free                   | $0.01-$0.05/GB              | $0.05-$0.12/GB       |
| DigitalOcean     | Free    | Free                   | N/A                         | $0.01/GB (after 1TB) |
| Aiven            | Free    | Free                   | Included                    | Included             |
| PlanetScale      | Free    | Free                   | Included                    | Included             |

_Network transfer costs become significant for applications with heavy data movement, especially across regions or to the internet. DigitalOcean offers the most economical outbound internet transfer, while Aiven includes all network transfer in their base pricing, eliminating this variable cost component._

![network_transfer_pricing_chart](/content/blog/mysql-hosting-options-pricing-comparison/network_transfer_pricing_chart.webp)

_The chart above highlights the dramatic difference in outbound internet transfer costs between providers. AWS RDS/Aurora and Google Cloud SQL charge up to $0.15/GB, while DigitalOcean charges only $0.01/GB after the first 1TB. Aiven stands out by including all network transfer in their base pricing, making it the most predictable option for applications with heavy data transfer requirements._

### High Availability and Backup Pricing

| Provider         | High Availability Setup            | Backup Storage                       | Point-in-Time Recovery |
| ---------------- | ---------------------------------- | ------------------------------------ | ---------------------- |
| AWS RDS MySQL    | 2x instance cost                   | Free up to 100% of DB storage        | Included               |
| AWS Aurora MySQL | Built-in (minimal additional cost) | Free up to 100% of DB storage        | Included               |
| Google Cloud SQL | 2x instance cost                   | 7 backups free, then $0.08/GB-month  | Included               |
| Azure MySQL      | 1.5-2x instance cost               | Included for 7-35 days based on tier | Included               |
| DigitalOcean     | Additional cost for standby nodes  | Included in plan                     | Included               |
| Aiven            | Included in Business/Premium tiers | Included in plan                     | Included               |
| PlanetScale      | Included (1 primary + 2 replicas)  | 2x daily backups included            | Not available           |

_High availability configurations significantly impact total cost, effectively doubling the price for most providers except Aurora (which has built-in HA) and Aiven (which includes it in higher tiers). PlanetScale MySQL/Vitess clusters come with HA and 3 nodes by default. Backup costs are generally included with limitations, with Google Cloud SQL being the only provider to charge after a certain threshold._

![ha_backup_pricing_chart](/content/blog/mysql-hosting-options-pricing-comparison/ha_backup_pricing_chart.webp)

_The chart above illustrates two critical cost factors: high availability multipliers and backup storage costs. AWS Aurora and Aiven offer the most economical high availability options, with Aurora at just 1.1x the base price and Aiven including it in their base pricing. For backup storage, Google Cloud SQL is the only provider charging extra ($0.08/GB-month) after 7 free backups, while all other providers include backups in their base pricing with various retention periods._

## Cost Optimization Strategies

### Long-Term Commitments

| Provider     | Commitment Options          | Maximum Discount | Flexibility                    |
| ------------ | --------------------------- | ---------------- | ------------------------------ |
| AWS          | 1-3 year Reserved Instances | 62%              | Limited to specific instance   |
| Google Cloud | 1-3 year Committed Use      | 57%              | Flexible across instance types |
| Azure        | 1-3 year Reserved Capacity  | 55%              | Limited to specific instance   |
| DigitalOcean | None                        | N/A              | Pay-as-you-go only             |
| Aiven        | Annual commitments          | Custom pricing   | Limited flexibility            |
| PlanetScale  | Annual commitments          | Discounts for consumption and AWS/GCP Marketplace | Custom                         |

_For predictable, stable workloads, commitment-based discounts from major cloud providers offer substantial savings. AWS provides the highest potential discount but with less flexibility than Google's commitment model._

### Serverless and Variable Workloads

| Provider     | Serverless Option                | Minimum Cost       | Scaling Granularity |
| ------------ | -------------------------------- | ------------------ | ------------------- |
| AWS Aurora   | Aurora Serverless                | Pay per ACU-second | 0.5 ACU increments  |
| Azure        | Flexible Server (preview)        | Hourly billing     | Preset tiers        |
| Google Cloud | None (only auto storage scaling) | Full instance cost | N/A                 |
| DigitalOcean | None                             | Full instance cost | N/A                 |
| Aiven        | None                             | Full instance cost | N/A                 |
| PlanetScale  | None                             | Full instance cost | N/A                 |

_For variable or unpredictable workloads, AWS Aurora Serverless offers the most mature consumption-based model, potentially reducing costs during periods of low activity. Azure is developing similar capabilities but with less granularity._

## Regional Availability and Data Residency

| Provider         | Number of Regions                 | Data Residency Controls | Regional Pricing Variations |
| ---------------- | --------------------------------- | ----------------------- | --------------------------- |
| AWS RDS/Aurora   | 27+ regions                       | Strong                  | 10-30% variation            |
| Google Cloud SQL | 24+ regions                       | Strong                  | 5-20% variation             |
| Azure MySQL      | 30+ regions                       | Strong                  | 5-25% variation             |
| DigitalOcean     | 8 regions                         | Limited                 | Uniform pricing             |
| Aiven            | 90+ regions (via cloud providers) | Strong                  | Uniform pricing             |
| PlanetScale      | 18 regions (via cloud providers)  | Strong                  | 5-20% variation             |

_Regional availability directly impacts data sovereignty compliance. The hyperscalers offer the most comprehensive regional coverage, while Aiven leverages underlying cloud providers to offer the widest deployment options with consistent pricing across regions._

## Conclusion

The MySQL hosting market in 2025 offers diverse pricing models catering to different priorities:

1. **AWS and Google Cloud** provide granular control with complex pricing structures.
2. **Azure** balances feature richness with moderate pricing complexity.
3. **DigitalOcean** emphasizes simplicity and predictability at the expense of some flexibility.
4. **Aiven** offers multi-cloud flexibility with straightforward pricing but at a premium for higher tiers.
5. **PlanetScale** starts at a higher price, but includes high availability by default and offers better price to performance options with locally-attached NVMe.

## References

**Official Pricing Pages**

1. [AWS RDS MySQL Pricing](https://aws.amazon.com/rds/mysql/pricing/)
1. [AWS Aurora Pricing](https://aws.amazon.com/rds/aurora/pricing/)
1. [Google Cloud SQL Pricing](https://cloud.google.com/sql/pricing)
1. [Azure Database for MySQL Pricing](https://azure.microsoft.com/en-us/pricing/details/mysql/)
1. [DigitalOcean Managed Databases Pricing](https://www.digitalocean.com/pricing/managed-databases)
1. [Aiven MySQL Pricing](https://aiven.io/pricing)
1. [PlanetScale Pricing](https://planetscale.com/pricing)

**Cost Optimization Guides**

1. [AWS RDS Cost Optimization](https://aws.amazon.com/blogs/database/best-practices-for-amazon-rds-cost-optimization/)
1. [Google Cloud SQL Cost Optimization](https://cloud.google.com/blog/products/databases/optimize-cloud-sql-costs)
1. [Azure MySQL Cost Optimization](https://learn.microsoft.com/en-us/azure/mysql/flexible-server/concepts-cost-optimization)
1. [Multi-Cloud Database Strategy Guide](https://aiven.io/blog/multi-cloud-strategy-for-databases)



<HintBlock type="info">

This post is maintained by [Bytebase](/), an open-source database DevSecOps solution. We update the post every year to reflect the latest developments in the Kubernetes ecosystem.

</HintBlock>

| Update History | Comment          |
| -------------- | ---------------- |
| 2025/04/28     | Initial version. |

As Kubernetes matures, managing multiple clusters at scale presents new challenges. Organizations now deploy Kubernetes across on-premises and multiple cloud environments, facing new configuration, deployment, and governance complexities.

This article explores top open-source tools that help teams efficiently manage multiple Kubernetes clusters while maintaining consistency, security, and operational excellence.

## What are the criteria?

Key evaluation factors for Kubernetes cluster management tools include:

- **Multi-cluster support**: Managing multiple clusters from a single control plane
- **Community support**: Active development and documentation
- **Governance features**: Policy enforcement and security controls

## Argo CD

![argocd](/content/blog/top-open-source-kubernetes-cluster-management-tools/argocd.webp)

[Argo CD](https://github.com/argoproj/argo-cd) is a declarative, GitOps continuous delivery tool for Kubernetes that automates synchronization between application definitions in Git and their deployed state.

Originally developed by Intuit and now a CNCF graduated project with over 19K GitHub stars, Argo CD has become a widely adopted standard for GitOps on Kubernetes, offering strong visualization capabilities and effective integration with progressive delivery through Argo Rollouts.

[Akuity](https://akuity.io/), founded by the original creators of Argo CD, offers commercial support and an enterprise platform built on top of Argo CD.

**Key Features:**

- GitOps-First Approach:

  Industry-leading implementation of Git as the single source of truth

- Declarative Application Model:

  Define applications once and deploy consistently across environments

- Automated Sync & Drift Detection:

  Continuously reconcile live state with desired state in Git

- Progressive Delivery:

  Seamless integration with Argo Rollouts for canary and blue/green deployments

- Mature Ecosystem:

  CNCF Graduated project with extensive adoption and community support

**Use Cases:**

Argo CD excels in environments where:

- Advanced progressive delivery (canary, blue/green) is required through Argo Rollouts integration
- Strict compliance validation and comprehensive auditing of all deployment activities is essential
- Organizations need declarative GitOps with automated drift detection and remediation
- Visual application deployment state is needed with a detailed dependency visualization UI

## Karmada

![karmada](/content/blog/top-open-source-kubernetes-cluster-management-tools/karmada.webp)

[Karmada](https://karmada.io/) is a multi-cluster orchestration solution that maintains the standard Kubernetes API experience. It provides federation capabilities without requiring application changes, ideal for organizations with existing Kubernetes deployments.

Originally developed by Huawei, Karmada is now a part of the CNCF ecosystem, with commercial support options available through various cloud service providers.

**Key Features:**

- Native Kubernetes API:

  Uses standard Kubernetes APIs without requiring custom CRDs, minimizing learning curve

- Multi-cluster Orchestration:

  True federation capabilities with automatic failover between clusters

- Global/Regional Resource Model:

  Unique approach to defining which resources are global vs. cluster-specific

- Resource Propagation:

  Fine-grained control over how workloads are distributed to member clusters

- Override Policies:

  Customize deployments for specific clusters while maintaining a single source of truth

**Use Cases:**

Karmada is particularly valuable for:

- Zero-change multi-cloud federation of existing Kubernetes workloads
- Cross-cluster automated failover for high availability
- Native Kubernetes API usage without custom abstractions
- Region-specific workload propagation with granular control

## Sveltos

![sveltos](/content/blog/top-open-source-kubernetes-cluster-management-tools/sveltos.webp)

[Sveltos](https://projectsveltos.github.io/sveltos/) addresses the challenges of policy management across Kubernetes clusters through a lightweight agent architecture.

Developed with a focus on simplicity, Sveltos unifies multiple policy engines under a single control plane and introduces an innovative classification system that automatically targets clusters based on their attributes.

**Key Features:**

- Classification System:

  Automatically target clusters based on labels, capabilities, or other attributes

- Cluster Profiles:

  Define policies, add-ons, and configurations that should be applied to matching clusters

- Multiple Policy Formats:

  Support for Kyverno, OPA/Gatekeeper, and other policy engines in one system

- Pre-validation:

  Test policies before deploying to production clusters

- Drift Detection and Remediation:

  Automatically detect and fix policy violations across the cluster fleet

**Use Cases:**

Sveltos shines in:

- Multi-policy environment management (Kyverno, OPA/Gatekeeper) with unified controls
- Policy testing and validation before production deployment
- Automatic cluster targeting based on capabilities or attributes
- Environments where simplicity is valued over complex features

## Plural

![plural](/content/blog/top-open-source-kubernetes-cluster-management-tools/plural.webp)

[Plural](https://github.com/pluralsh/plural) combines infrastructure provisioning with application deployment capabilities, offering an integrated marketplace of pre-configured applications for Kubernetes. It emphasizes the application layer while providing infrastructure automation and cost monitoring features.

[Plural.sh](https://www.plural.sh/), the company behind the project, offers commercial support and enterprise features beyond the open-source offering.

**Key Features:**

- Application Marketplace:

  Deploy common applications with best practices baked in

- Infrastructure as Code:

  Define infrastructure using Terraform with composable recipes

- Multi-Cloud Bootstrapping:

  Create and configure clusters across major cloud providers

- Cost Monitoring:

  Track and optimize Kubernetes spending

- Community-driven Applications:

  Growing library of applications maintained by the community

**Use Cases:**

Plural is particularly effective for:

- Organizations wanting an application marketplace with best practices baked in
- Teams requiring both infrastructure provisioning and application deployment in one tool
- Environments needing built-in Kubernetes cost monitoring
- Projects leveraging community-maintained application packages

## Summary

**Comparison tables**

| Feature              | Argo CD                      | Karmada                     | Sveltos                | Plural                     |
| -------------------- | ---------------------------- | --------------------------- | ---------------------- | -------------------------- |
| **Primary Focus**    | GitOps & Continuous Delivery | Multi-cluster Orchestration | Policy Management      | Application Deployment     |
| **Architecture**     | Control plane + agents       | Control plane + API servers | Control plane + agents | Control plane + installers |
| **Standard K8s API** | ❌ (uses CRDs)               | ✅                          | ❌ (uses CRDs)         | ❌ (uses CRDs)             |
| **GitOps Native**    | ✅                           | Optional                    | ✅                     | ✅                         |
| **UI Complexity**    | Moderate                     | High                        | Low                    | Moderate                   |
| **Learning Curve**   | Moderate                     | Steep                       | Gentle                 | Moderate                   |
| **Maturity**         | Very High (CNCF Graduated)   | High (CNCF Incubating)      | Medium                 | Medium-High                |
| **Community Size**   | Very Large                   | Large                       | Growing                | Medium                     |

The Kubernetes cluster management landscape continues to evolve rapidly in 2025, with different tools addressing various aspects of the multi-cluster challenge:

- **Argo CD** remains the gold standard for GitOps-based delivery to Kubernetes clusters, with a mature feature set and extensive community support.
- **Karmada** excels in true multi-cluster orchestration with its Kubernetes-native approach and sophisticated scheduling capabilities.
- **Sveltos** offers a lightweight yet powerful approach to policy management across cluster fleets.
- **Plural** tackles both infrastructure provisioning and application deployment, creating a more integrated experience.

## Further Reading

- [Top Open Source Kubernetes Dashboard](/blog/top-open-source-kubernetes-dashboard)
- [Top Open Source GitOps Tools](/blog/top-open-source-gitops-tools)
- [Top Open Source Database Management Software](/blog/top-open-source-database-management-software)



![reddit](/content/blog/golang-orm-query-builder/reddit.webp)

Selecting the appropriate ORM—or opting to avoid one entirely—remains a contentious topic. In Go, the discussion is particularly intense given the existence of a robust `database/sql` standard library and multiple competitive alternatives. Let's examine the available options:

- **[GORM](https://github.com/go-gorm/gorm)** is a full-featured ORM (Object-Relational Mapper) that provides high-level abstractions over database operations.

- **[Squirrel](https://github.com/Masterminds/squirrel)** is a SQL query builder that helps construct SQL queries programmatically without being a full ORM.

- **[sqlc](https://github.com/sqlc-dev/sqlc)** takes a unique approach by generating type-safe Go code from SQL queries you write.

## Ease of Use

### GORM

GORM excels at simplifying database interactions through its intuitive API:

```go
// Define a model
type Product struct {
  gorm.Model
  Name          string
  Price         float64
  StockQuantity int
}

// Create a product
db.Create(&Product{Name: "Smartphone", Price: 799.99, StockQuantity: 100})

// Read a product
var product Product
db.First(&product, "name = ?", "Smartphone")

// Update a product
db.Model(&product).Updates(Product{Price: 749.99, StockQuantity: 95})

// Delete a product
db.Delete(&product)
```

**Limitations:**

- Magic happens behind the scenes, making it harder to debug complex issues
- Performance overhead due to reflection and metadata processing
- Limited control over generated SQL, which can lead to inefficient queries

```go
// Example of a potentially inefficient query in GORM
// This loads all products into memory before filtering
var expensiveProducts []Product
db.Find(&expensiveProducts).Where("price > ?", 1000)

// Correct approach
db.Where("price > ?", 1000).Find(&expensiveProducts)
```

### Squirrel

Squirrel provides a [fluent-style](https://en.wikipedia.org/wiki/Fluent_interface) API for building SQL queries:

```go
// Create a SQL builder for PostgreSQL
psql := sq.StatementBuilder.PlaceholderFormat(sq.Dollar)

// Build and execute a query
query := psql.
    Select("id", "name", "price").
    From("products").
    Where(sq.Lt{"stock_quantity": 10})

rows, err := query.RunWith(db).Query()
```

**Limitations:**

- No object mapping capabilities, requiring manual scanning of results
- No schema management or validation
- Verbose for complex queries compared to writing raw SQL
- Immutable design can lead to excessive object creation

```go
// Example of verbose code required for scanning results
rows, err := query.RunWith(db).Query()
if err != nil {
    return err
}
defer rows.Close()

var products []Product
for rows.Next() {
    var p Product
    if err := rows.Scan(&p.ID, &p.Name, &p.Price); err != nil {
        return err
    }
    products = append(products, p)
}
```

### sqlc

sqlc takes a SQL-first approach:

```sql
-- name: GetProduct :one
SELECT * FROM products
WHERE id = $1 LIMIT 1;

-- name: CreateProduct :one
INSERT INTO products (name, price, stock_quantity)
VALUES ($1, $2, $3)
RETURNING *;
```

After defining queries, sqlc generates type-safe Go code:

```go
// Using generated code
product, err := queries.CreateProduct(ctx, db.CreateProductParams{
    Name:          "Smartphone",
    Price:         799.99,
    StockQuantity: 100,
})
```

**Limitations:**

- Requires writing raw SQL, which may be challenging for developers unfamiliar with SQL
- Limited dynamic query capabilities; difficult to build truly dynamic queries
- Initial setup more complex than other options

```go
// Example of limitation with dynamic queries
// This is not directly supported in sqlc - you'd need multiple query definitions
// or fall back to raw SQL for truly dynamic conditions

// For dynamic WHERE clauses, you might need multiple query definitions:
// -- name: GetProductsByPriceRange :many
// SELECT * FROM products WHERE price >= $1 AND price <= $2;
//
// -- name: GetProductsByStock :many
// SELECT * FROM products WHERE stock_quantity <= $1;
//
// -- name: GetProductsByCategory :many
// SELECT * FROM products WHERE category = $1;
```

## Schema Migration

For schema migration, all three tools have different approaches and limitations. Many teams pair these tools with a dedicated migration tool like [Goose](https://github.com/pressly/goose):

```sql
-- Goose migration example
-- +goose Up
ALTER TABLE products ADD COLUMN description TEXT;

-- +goose Down
ALTER TABLE products DROP COLUMN description;
```

### GORM

GORM follows a code-first approach where you define models in Go and let GORM handle the database schema:

```go
// Define models
type Product struct {
    gorm.Model
    Name          string
    Price         float64
    StockQuantity int
}

// Auto migrate the schema
db.AutoMigrate(&Product{})
```

**Limitations:**

- AutoMigrate is not suitable for production environments
- Schema changes can be unpredictable and potentially destructive
- Limited control over index creation and constraint definition
- Poor handling of complex migrations (e.g., data transformations)
- No versioning or rollback capabilities

```go
// Example of a limitation: AutoMigrate won't rename columns or handle complex changes
// If you rename a field in your struct:
type Product struct {
    gorm.Model
    Title string // Was previously "Name"
    Price float64
    StockQuantity int
}

// AutoMigrate will add a new column rather than renaming the existing one
db.AutoMigrate(&Product{}) // Creates a new "title" column, "name" remains

// Example of a dangerous limitation: dropping columns
// If you remove a field from your struct:
type Product struct {
    gorm.Model
    // Name field removed
    Price         float64
    StockQuantity int
}

// With DropColumn option, this will drop the column without warning
db.AutoMigrate(&Product{}).DropColumn // Dangerous in production
```

**Development Workflow with GORM:**

1. Define or update Go struct models
2. Run AutoMigrate during development
3. For production, create manual migrations or use a dedicated migration tool
4. Update application code to work with the new schema

### Squirrel

Squirrel has no schema management capabilities and must be paired with a migration tool.

### sqlc

sqlc verifies queries against schema changes but doesn't handle migrations itself:

```bash
# Verify queries against schema changes
sqlc verify
```

**Limitations:**

- No built-in migration capabilities
- Verification requires schema definition files
- Limited to checking SQL validity, not data integrity
- Requires external tools for schema evolution

**Development Workflow with sqlc:**

1. Create schema migrations using a dedicated tool like Goose
2. Run migrations to update the database schema
3. Update SQL queries in .sql files
4. Regenerate Go code with sqlc
5. Fix any compilation errors due to schema changes

```go
// Example of limitation: Need to regenerate code after query changes
// If you modify a query in your .sql file:
// -- name: GetProduct :one
// SELECT id, name, price, stock_quantity, created_at FROM products
// WHERE id = $1 LIMIT 1;

// And then add a new field to your database:
// ALTER TABLE products ADD COLUMN description TEXT;

// You must regenerate the code with sqlc generate
// Until then, the generated code won't include the new field
```

## Complex Query Generation

### GORM

GORM supports complex queries through its API:

```go
// Find products with joins and conditions
var results []Product
db.Joins("JOIN order_items ON order_items.product_id = products.id").
   Where("order_items.order_id = ?", orderId).
   Group("products.id").
   Having("COUNT(order_items.id) > ?", 1).
   Find(&results)
```

**Limitations:**

- Complex queries become unwieldy and hard to read
- Limited control over query optimization
- Performance overhead for complex operations
- Often requires falling back to raw SQL for advanced queries

```go
// Example of GORM limitations with complex queries
// This becomes hard to read and maintain
var results []struct {
    ProductID   uint
    ProductName string
    OrderCount  int
    TotalValue  float64
}

db.Table("products").
   Select("products.id as product_id, products.name as product_name, COUNT(orders.id) as order_count, SUM(order_items.quantity * order_items.unit_price) as total_value").
   Joins("LEFT JOIN order_items ON products.id = order_items.product_id").
   Joins("LEFT JOIN orders ON order_items.order_id = orders.id").
   Where("orders.created_at BETWEEN ? AND ?", startDate, endDate).
   Group("products.id, products.name").
   Having("COUNT(orders.id) > ?", 5).
   Order("total_value DESC").
   Limit(10).
   Find(&results)

// Often clearer to use raw SQL for complex queries
db.Raw(`
    SELECT
        p.id as product_id,
        p.name as product_name,
        COUNT(o.id) as order_count,
        SUM(oi.quantity * oi.unit_price) as total_value
    FROM products p
    LEFT JOIN order_items oi ON p.id = oi.product_id
    LEFT JOIN orders o ON oi.order_id = o.id
    WHERE o.created_at BETWEEN ? AND ?
    GROUP BY p.id, p.name
    HAVING COUNT(o.id) > ?
    ORDER BY total_value DESC
    LIMIT 10
`, startDate, endDate, 5).Find(&results)
```

### Squirrel

Squirrel excels at building complex queries programmatically:

```go
query := psql.
    Select("p.id", "p.name", "COUNT(oi.id) as order_count").
    From("products p").
    Join("order_items oi ON p.id = oi.product_id").
    Where(sq.Eq{"oi.order_id": orderId}).
    GroupBy("p.id", "p.name").
    Having(sq.Gt{"COUNT(oi.id)": 1})
```

**Limitations:**

- Can become verbose for very complex queries
- No compile-time SQL validation
- Limited database-specific optimizations
- Manual result scanning required

```go
// Example of verbosity with complex queries
query := psql.
    Select(
        "p.id AS product_id",
        "p.name AS product_name",
        "COUNT(DISTINCT o.id) AS order_count",
        "SUM(oi.quantity * oi.unit_price) AS total_value",
        "AVG(oi.unit_price) AS avg_price",
    ).
    From("products p").
    LeftJoin("order_items oi ON p.id = oi.product_id").
    LeftJoin("orders o ON oi.order_id = o.id").
    Where(sq.And{
        sq.GtOrEq{"o.created_at": startDate},
        sq.LtOrEq{"o.created_at": endDate},
        sq.Eq{"o.status": "completed"},
    }).
    GroupBy("p.id", "p.name").
    Having(sq.Gt{"COUNT(DISTINCT o.id)": 5}).
    OrderBy("total_value DESC").
    Limit(10)

// Scanning results requires manual mapping
rows, err := query.RunWith(db).Query()
if err != nil {
    return nil, err
}
defer rows.Close()

var results []ProductSalesReport
for rows.Next() {
    var r ProductSalesReport
    if err := rows.Scan(&r.ProductID, &r.ProductName, &r.OrderCount, &r.TotalValue, &r.AvgPrice); err != nil {
        return nil, err
    }
    results = append(results, r)
}
```

### sqlc

sqlc handles complex queries naturally since you write the SQL directly:

```sql
-- name: GetProductsInOrder :many
SELECT p.id, p.name, p.price, COUNT(oi.id) as order_count
FROM products p
JOIN order_items oi ON p.id = oi.product_id
WHERE oi.order_id = $1
GROUP BY p.id, p.name, p.price
HAVING COUNT(oi.id) > 1;
```

**Limitations:**

- Limited support for dynamic queries
- Need to define multiple similar queries for different conditions
- Generated code can become large with many query variations

```sql
// Example of limitation with dynamic queries
// For a report with optional filters, you might need multiple query definitions:

// -- name: GetProductSalesReport :many
// SELECT
//     p.id AS product_id,
//     p.name AS product_name,
//     COUNT(DISTINCT o.id) AS order_count,
//     SUM(oi.quantity * oi.unit_price) AS total_value
// FROM products p
// LEFT JOIN order_items oi ON p.id = oi.product_id
// LEFT JOIN orders o ON oi.order_id = o.id
// WHERE o.created_at BETWEEN $1 AND $2
// GROUP BY p.id, p.name
// HAVING COUNT(DISTINCT o.id) > $3
// ORDER BY total_value DESC
// LIMIT $4;

// For dynamic filtering, you'd need separate queries or fall back to raw SQL:
// -- name: GetProductSalesReportByCategory :many
// SELECT ... WHERE p.category = $1 AND o.created_at BETWEEN $2 AND $3 ...

// -- name: GetProductSalesReportByStatus :many
// SELECT ... WHERE o.status = $1 AND o.created_at BETWEEN $2 AND $3 ...
```

## Making Your Choice

| Tool         | Ease of Use | Schema Migration | Complex Query Generation | Performance | Overall                                    |
| ------------ | ----------- | ---------------- | ------------------------ | ----------- | ------------------------------------------ |
| **GORM**     | ★★★☆☆       | ★★★☆☆            | ★★★☆☆                    | ★★★☆☆       | Good for rapid development and simple CRUD |
| **Squirrel** | ★★★☆☆       | ★☆☆☆☆            | ★★★★☆                    | ★★★★☆       | Excellent for dynamic queries              |
| **sqlc**     | ★★★★☆       | ★★☆☆☆            | ★★★☆☆                    | ★★★★★       | Best for performance and type safety       |

**GORM** excels at ease of use but has limitations with schema migration and complex queries. It's ideal for rapid development and simple CRUD operations.

**Squirrel** offers a good balance of flexibility and control for query building but requires more code and has no schema management capabilities.

**sqlc** provides the best performance and type safety for complex queries but requires SQL knowledge and has limited support for dynamic queries.

## Practical Recommendation

For production applications in 2025, consider these combinations:

### 1. GORM + Goose

Use GORM for its developer-friendly API and Goose for robust migrations.

```go
// Application code uses GORM
db.First(&product, "id = ?", productID)

// Schema changes use Goose migrations
// migrations/20250427_add_description.sql
// -- +goose Up
// ALTER TABLE products ADD COLUMN description TEXT;
// -- +goose Down
// ALTER TABLE products DROP COLUMN description;
```

**When this combination works well:**

- Team is more comfortable with ORM concepts
- Application has mostly simple CRUD operations

### 2. sqlc + Squirrel + Goose

Use sqlc for type-safe static queries, Goose for migrations, and Squirrel for dynamic queries.

```go
// Use sqlc for predefined queries
product, err := queries.GetProduct(ctx, productID)

// Use Squirrel for dynamic queries
query := psql.
    Select("*").
    From("products").
    Where(sq.Eq{"category": category})

if minPrice > 0 {
    query = query.Where(sq.GtOrEq{"price": minPrice})
}

if maxPrice > 0 {
    query = query.Where(sq.LtOrEq{"price": maxPrice})
}

// Use Goose for migrations
// migrations/20250427_add_indexes.sql
```

**When this combination works well:**

- Application has both static and dynamic queries
- Team has SQL expertise
- Type safety is important

### 3. Raw SQL + sqlc + Goose

Use raw SQL for one-off operations, sqlc for frequently used queries, and Goose for migrations.

```go
// Use raw SQL for administrative or infrequent operations
_, err := db.Exec(`
    UPDATE products
    SET featured = true
    WHERE category = $1 AND price > $2
`, category, minPrice)

// Use sqlc for frequent operations
products, err := queries.ListProductsByCategory(ctx, category)

// Use Goose for schema migrations
// migrations/20250427_add_featured_column.sql
// -- +goose Up
// ALTER TABLE products ADD COLUMN featured BOOLEAN NOT NULL DEFAULT false;
// -- +goose Down
// ALTER TABLE products DROP COLUMN featured;
```

**When this combination works well:**

- Team has strong SQL expertise
- Application has many one-off operations
- Type safety is still desired for core functionality



<HintBlock type="info">

This post is maintained by Bytebase, an open-source database DevSecOps tool that can manage MySQL. We
will constantly update this post to include the latest MySQL 9.x releases.

</HintBlock>

| Update History | Comment          |
| -------------- | ---------------- |
| 2025/04/26     | Initial version. |

## Release Timeline

- **MySQL 9.0.0**: July 1, 2024 (Innovation Release) - _Removed from distribution due to critical bug_
- **MySQL 9.0.1**: Released as replacement for 9.0.0
- **MySQL 9.1.0**: October 15, 2024 (Innovation Release)
- **MySQL 9.2.0**: January 21, 2025 (Innovation Release)
- **MySQL 9.3.0**: April 15, 2025 (Innovation Release)

This post provides a comprehensive overview of what's new in the entire MySQL 9.x series (9.0, 9.1, 9.2, and 9.3), organized by edition type and including notable bug fixes and dropped features.

## MySQL Community New Features

### VECTOR Type Support (9.0)

MySQL 9.0 introduced a new `VECTOR` data type, which is a timely addition for applications working with vector data, such as machine learning and AI applications.

**DBA Note:** _First-generation implementation with significant limitations - can't be used as keys and has limited function compatibility. Don't expect performance to match dedicated vector databases yet._

#### Key Capabilities

- A data structure consisting of a list of entries (4-byte floating-point values)
- Expression as either a binary string value or a list-formatted string
- Declaration with a maximum length or number of entries (in parentheses)
- Default maximum length of 2048, with an absolute maximum of 16383

#### Vector Functions

MySQL 9.0 includes several functions for working with vector data:

- `VECTOR_DIM()`: Returns the length of a vector
- `STRING_TO_VECTOR()` (alias: `TO_VECTOR()`): Converts a list-formatted representation to binary string representation
- `VECTOR_TO_STRING()` (alias: `FROM_VECTOR()`): Converts binary string representation to list-formatted representation
- Vector comparison operators for equality testing

### Enhanced Foreign Key Constraints (9.0)

MySQL 9.0 enhances foreign key handling with long-waited improvements to standards compliance and usability.

**DBA Note:** _This fixes long-standing behavior that should have worked correctly years ago. Test carefully when upgrading as applications that relied on the previous broken behavior may now encounter constraint violations._

#### Improvements

- MySQL now enforces inline foreign key specifications, which were previously accepted by the parser but ignored
- MySQL 9.0 also accepts implicit references to parent table primary key columns
- Multiple foreign key constraint definitions in a single CREATE TABLE statement are now properly handled

### Saving JSON Output from EXPLAIN ANALYZE (9.0)

MySQL 9.0 provides support for saving the output from EXPLAIN ANALYZE into a user variable.

**DBA Note:** _Useful for automated query analysis, but you'll still need to build your own tools to make practical use of the JSON output. Would have been nice to include helper functions for common analysis tasks._

### Trigger Handling Optimization (9.1)

- Previously, for tables with triggers, the triggers were fully parsed and loaded into memory every time the table was accessed, causing high resource usage
- MySQL 9.1 divides trigger parsing and loading into two separate phases:
  - First phase: Only reads trigger metadata (stored once and shared between instances)
  - Second phase: Actual parsing and execution (only performed for operations that modify table data)
- New system variable `table_open_cache_triggers` controls the maximum number of open tables with fully loaded triggers
- New status variables track cache usage for tables with triggers

### Optimizer Improvements (9.2-9.3)

- **9.2**: Fixed performance issue with hash joins that spent unreasonably high time in `pack_rows::RequestRowId()`
- **9.2**: `EXPLAIN FORMAT=TREE` now shows the clustered primary key scan for RowID-Ordered Retrieval (ROR) intersection plans
- **9.3**: Extended the `subquery_to_derived` optimization to support more comparison operations
- **9.3**: Now supports all comparison operations (`>ANY`, `>=ANY`, `<ANY`, `<=ANY`, `>ALL`, `>=ALL`, `<ALL`, `<=ALL`, `=ANY`, `<>ALL`)
- **9.3**: Transformation now supported in both `SELECT` and `WHERE` clauses

### Performance Schema Enhancements (9.1-9.3)

- **9.1**: Improved OpenTelemetry support for exporting telemetry logs to OpenTelemetry backends
- **9.1**: Added ability to enable/disable Telemetry meters from command line or configuration file
- **9.3**: Added ability to configure network namespace for telemetry endpoints on Linux platforms
- **9.3**: Added new system variables for telemetry network namespace configuration

## MySQL Enterprise New Features

### JavaScript Stored Programs (9.0+)

MySQL 9.0 Enterprise Edition introduced support for stored programs written in JavaScript through the Multilingual Engine Component (MLE). This feature has been continuously enhanced in subsequent releases.

**DBA Note:** _While this opens up database programming to JavaScript developers, expect potential performance overhead compared to native SQL stored procedures. Monitor memory usage carefully in production._

#### Key Capabilities in 9.0

- Support for both stored procedures and stored functions written in JavaScript
- Conformance to the ECMAScript 2023 Specification
- Strict mode by default (cannot be disabled)
- All standard ECMAScript library objects (`Object`, `Function`, `Math`, `Date`, `String`, etc.)
- Support for `console.log()` and `console.error()` for debugging

#### JavaScript Enhancements in 9.1-9.3

- **9.2**: Added support for MySQL `ENUM` and `SET` types as arguments of JavaScript stored routines
- **9.2**: Added support for accessing user-defined functions, procedures, and variables from JavaScript routines
- **9.2**: Implemented JavaScript MySQL transaction API for actions like `START TRANSACTION`, `COMMIT`, `ROLLBACK`, and `SET AUTOCOMMIT`
- **9.2**: Added support for reusable JavaScript libraries with new SQL statements (`CREATE LIBRARY`, `DROP LIBRARY`, `SHOW CREATE LIBRARY`)
- **9.3**: Added full support for the `DECIMAL` type (including its alias `NUMERIC`) in JavaScript stored programs
- **9.3**: Added support for the JavaScript `Intl` global object for localization of numbers, dates, and other values
- **9.3**: Enhanced `ALTER PROCEDURE` and `ALTER FUNCTION` to accept a `USING` clause to add, replace, or remove libraries
- **9.3**: Enhanced `mle_session_reset()` function with optional string argument to clear `stderr`, `stdout`, or both
- **9.3**: Upgraded the MLE component to use GraalVM Truffle version 24.2.0

### Replication Enhancements (9.1-9.3)

- **9.1**: Added Replication Applier Metrics component, providing statistical information about replication formerly logged in the error log
- **9.3**: Added Group Replication Primary Election component, enabling specification of the most-up-to-date selection method for choosing a new primary during failover
- **9.3**: Fixed issues in the Flow Control Statistics component

### MySQL Option Tracker (9.1-9.3)

- **9.1**: Initial implementation
- **9.2**: Added ability to provide usage information about binary log, group replication, and server replica usage
- **9.3**: Added support for tracking the traditional MySQL Optimizer and MySQL Hypergraph Optimizer
- **9.3**: Each feature that supports Option Tracker now provides a global status variable

## Noticeable Bugs Fixed

### Critical InnoDB Server Restart Issue (9.0.1)

**Bug #36808732 (Fixed in MySQL 9.0.1)**

A critical issue in MySQL 9.0.0 caused the server to fail to restart after creating a large number of tables (8001 or more). This bug was so severe that it led to the removal of MySQL 9.0.0 from distribution, with users advised to upgrade directly to MySQL 9.0.1 instead.

**DBA Note:** _A reminder of why you should never deploy .0 releases to production. This kind of fundamental issue making it to GA release raises questions about testing procedures._

### Improved InnoDB Tablespace File Scan Performance (9.0.1)

**Bug #110402, Bug #35200385 (Fixed in MySQL 9.0.1)**

MySQL 9.0.1 includes improvements to tablespace file scan performance at startup, which can reduce server startup times, especially for installations with many tablespaces.

**DBA Note:** _Welcome improvement for environments with many tablespaces, but startup times will still be noticeably slower than competing databases with large numbers of tables._

### Group Replication CREATE TABLE ... SELECT Fix (9.0.1)

**Bug #36784284 (Fixed in MySQL 9.0.1)**

Running a `CREATE TABLE ... SELECT` statement on a source coming from an asynchronous channel to Group Replication previously led to errors on the replica. This issue has been resolved in MySQL 9.0.1.

**DBA Note:** _Group Replication continues to have edge cases and reliability issues. Test thoroughly with your specific workload before deploying to production._

### Performance Schema Service Thread Exposure (9.3)

Fixed issue where the `PERFORMANCE_SCHEMA` service thread v7 was not exposed, preventing its use by components.

## Features Removed in MySQL 9.0

MySQL 9.0 has removed several features that were present in previous versions. Applications using these features should be updated to use alternatives.

### Authentication Plugin Removals

**mysql_native_password Plugin**

The `mysql_native_password` authentication plugin, which was deprecated in MySQL 8.0, has been completely removed in MySQL 9.0. The server now rejects `mysql_native` authentication requests from older client programs that do not have `CLIENT_PLUGIN_AUTH` capability.

**DBA Note:** _This will break connections from many legacy applications and tools. Plan for significant connection string updates across your environment when upgrading._

**Related Removals:**

Due to this change, the following server options and variables have also been removed:

- The `--mysql-native-password` server option
- The `--mysql-native-password-proxy-users` server option
- The `default_authentication_plugin` server system variable

### Storage Engine Removals

Several storage engines have been removed in MySQL 9.0:

- `ARCHIVE` storage engine
- `BLACKHOLE` storage engine
- `FEDERATED` storage engine
- `MEMORY` storage engine
- `MERGE` storage engine
- `performance_schema` and any other storage engine
- `TempTable` and any other storage engine

**DBA Note:** _Expected house cleanup. The removal of MEMORY engine could be concerning for high-performance temporary tables. You'll need to rethink some performance optimization strategies that relied on these specialized engines._

### Important Changes in MySQL 9.3

- Beginning with MySQL 9.3, it is no longer possible to downgrade between individual MySQL Innovation series releases, even within the same series
  - For example, after upgrading to MySQL 9.3.1 (if released), it would not be possible to downgrade back to MySQL 9.3.0

## Is It Worth Upgrading from MySQL 8.0 to 9.0?

When considering whether to upgrade from MySQL 8.0 to MySQL 9.0, it's valuable to compare this transition with the previous major upgrade from MySQL 5.7 to 8.0.

### Scale of Changes

**MySQL 5.7 to 8.0 Upgrade:**

- Represented a fundamental architectural shift with the introduction of the data dictionary
- Completely changed authentication defaults (from mysql_native_password to caching_sha2_password)
- Added major new features like document store, window functions, CTEs, and JSON enhancements
- Introduced InnoDB improvements including auto-increment persistence and undo tablespaces
- Changed default character set from latin1 to utf8mb4

**MySQL 8.0 to 9.0 Upgrade:**

- More evolutionary than revolutionary compared to the 5.7 to 8.0 transition
- Focused on specific feature additions (JavaScript stored programs, VECTOR type) rather than core architectural changes
- Continues the removal of legacy components (storage engines) started in 8.0
- Maintains the same authentication model and character set defaults

**DBA Note:** _The 8.0 to 9.0 upgrade is less disruptive than the 5.7 to 8.0 transition was. The 5.7 to 8.0 upgrade was one of the most challenging in MySQL history due to fundamental architectural changes, while 9.0 builds on the foundation established in 8.0._

### Migration Challenges

**MySQL 5.7 to 8.0 Upgrade Challenges:**

- Required extensive pre-upgrade checks for incompatibilities
- Needed careful handling of the authentication plugin change
- Required addressing deprecated SQL modes and syntax
- Demanded thorough testing of applications due to changes in query optimizer behavior
- Often required character set conversions from latin1 to utf8mb4

**MySQL 8.0 to 9.0 Upgrade Challenges:**

- Still requires following the "no skipping versions" best practice
- Needs verification of applications that rely on removed storage engines
- Requires less extensive pre-upgrade checks compared to 5.7 to 8.0
- Maintains backward compatibility for most application code

**DBA Note:** _The removal of several storage engines in 9.0 is the most significant migration challenge, but only affects applications specifically using ARCHIVE, BLACKHOLE, FEDERATED, MEMORY, or MERGE engines. Most modern applications using InnoDB will face minimal disruption._

### Performance Considerations

**MySQL 5.7 to 8.0 Performance Changes:**

- Generally improved performance for most workloads
- Some initial performance regressions reported for specific use cases
- Significant improvements in high-concurrency environments
- Better utilization of modern hardware

**MySQL 8.0 to 9.0 Performance Changes:**

- No fundamental performance architecture changes
- Specific optimizations for certain query patterns
- VECTOR type adds capabilities for machine learning applications
- No major performance regressions reported

**DBA Note:** _Unlike the 5.7 to 8.0 upgrade which sometimes resulted in unexpected performance changes requiring tuning, the 9.0 upgrade appears to maintain performance characteristics similar to 8.0 with incremental improvements._

### When to Upgrade to MySQL 9.0

The upgrade from MySQL 8.0 to 9.0 represents a more incremental improvement compared to the transformative jump from 5.7 to 8.0. For most users, the decision to upgrade should be driven by specific feature requirements rather than a general need to stay current.

Unlike the 5.7 to 8.0 transition—which became increasingly urgent as 5.7 approached its end-of-life—the move to 9.0 is more optional, especially for those on the 8.0 LTS track, which will continue to be supported for years to come.

If you're currently on MySQL 8.0 LTS and don't require any of the new features introduced in 9.0, there's little urgency to upgrade. It's also worth noting that the MySQL 9.x family has not yet announced an LTS release. For this reason, we recommend holding off on upgrading until an LTS version is officially announced.

## References

1. [MySQL 9.0 Release Notes](https://dev.mysql.com/doc/relnotes/mysql/9.0/en/)
2. [MySQL 9.1 Release Notes](https://dev.mysql.com/doc/relnotes/mysql/9.1/en/)
3. [MySQL 9.2 Release Notes](https://dev.mysql.com/doc/relnotes/mysql/9.2/en/)
4. [MySQL 9.3 Release Notes](https://dev.mysql.com/doc/relnotes/mysql/9.3/en/)



<HintBlock type="info">

This post is maintained by Bytebase, an open-source database DevSecOps tool that can manage all mainstream databases, we have built various database-specific parsers to support features like [SQL Review](https://docs.bytebase.com/sql-review/overview/). We update the post every year.

</HintBlock>

| Update History | Comment          |
| -------------- | ---------------- |
| 2025/04/16     | Initial version. |
| 2025/04/21     | Fix wrong link.  |
| 2026/02/21     | Add pgparser.    |

SQL (Structured Query Language) remains the dominant language for database interactions, powering everything from traditional relational databases to modern data warehouses and analytics platforms. Behind the scenes, SQL parsers play a crucial role in interpreting, validating, and processing SQL statements.

In this blog post, we'll explore the top open source SQL parsers available in 2025, categorizing them into database-specific parsers and generic parsers. We'll also take a look at ANTLR, a powerful parser generator that's commonly used to build custom SQL parsers.

## What is a SQL Parser?

![sql-parser](/content/blog/top-open-source-sql-parsers/sql-parser-arch.svg)

A SQL parser is a software component that reads SQL text and converts it into a structured representation, typically an Abstract Syntax Tree (AST). This structured representation makes it possible to:

- Validate SQL syntax without executing the query
- Format and pretty-print SQL statements
- Analyze query patterns and identify potential issues
- Transform queries for optimization or dialect conversion
- Extract metadata such as table and column references
- Track data lineage and dependencies

SQL parsers are used in a wide range of applications, including:

- Database management systems
- SQL editors and IDEs
- Query optimization tools
- Data lineage and governance platforms
- Migration tools for moving between database systems
- Educational platforms for teaching SQL

## MySQL/MariaDB Parsers

- [TiDB Parser by PingCAP](https://github.com/pingcap/tidb/tree/master/pkg/parser). Written in Go, it's the most widely adopted MySQL parsers, many backend application uses it. The downside is it's a parser for TiDB and has [compatibility limitations](https://docs.pingcap.com/tidb/stable/mysql-compatibility/) such as the lack of stored procedure support.

- [sql-parser in phpMyAdmin](https://github.com/phpmyadmin/sql-parser). Written in PHP, it's a by-product of the mighty phpmyadmin with good MySQL compatibility support.

- [mysql-parser by Bytebase](https://github.com/bytebase/mysql-parser). Written in Go based on ANTLR with good MySQL compatibility support.

## PostgreSQL Parsers

- [libpg_query by pganalyze](https://github.com/pganalyze/libpg_query). This is not a parser by itself. It's a C library that facilitates building the language-binding parsers (note: the language bindings require CGO or equivalent FFI to link with the C library):

  - Python: [pglast](https://github.com/pganalyze/pg_query)
  - Ruby: [pg_query](https://github.com/pganalyze/pg_query)
  - Golang: [pg_query_go](https://github.com/pganalyze/pg_query_go)
  - JavaScript: [psql-parser](https://github.com/launchql/pgsql-parser)
  - Rust: [pg_query.rs](https://github.com/pganalyze/pg_query.rs)

- [pgparser](https://github.com/pgplex/pgparser). A pure Go PostgreSQL parser that translates PostgreSQL's grammar (`gram.y`) into Go using goyacc with no CGO dependency. Its AST is 100% compatible with PostgreSQL's internal Node structures. Targeting PostgreSQL 17, it has been validated against ~45,000 SQL statements from PostgreSQL's regression suite with a 99.6% pass rate.

## Oracle Parsers

- [plsql-parser](https://github.com/bytebase/plsql-parser). Go-based parser based on the [ANTLR Oracle grammar](https://github.com/antlr/grammars-v4/tree/master/sql/plsql).

## SQL Server Parsers

- [tsql-parser](https://github.com/bytebase/tsql-parser). Go-based parser based on the [ANTLR T-SQL grammar](https://github.com/antlr/grammars-v4/blob/master/sql/tsql).

## General-Purpose Parsers

Building a general-purpose SQL parser that robustly supports multiple databases is inherently challenging, as each database system has its unique dialect and syntax variations.

- [sqlparser-rs](https://github.com/apache/datafusion-sqlparser-rs). A rust-based parser which is used by many rust-based database project. Its [readme](https://github.com/apache/datafusion-sqlparser-rs?tab=readme-ov-file#source-locations-work-in-progress) is honest about the compatibility limitation. Still, it's the most promising one.

- [JSqlParser](https://github.com/JSQLParser/JSqlParser). A java-based parser supporting Oracle, SQL Server, MySQL, PostgreSQL, and etc. Though the [supported syntax is limited](https://github.com/JSQLParser/JSqlParser?tab=readme-ov-file#supported-grammar-and-syntax)

- [ZetaSQL](https://github.com/google/zetasql). A unified parser implements the GoogleSQL language, which is used across several of Google's SQL products, including BigQuery, Spanner, F1, BigTable.

## ANTLR as a SQL Parser Generator

While database-specific and generic SQL parsers provide ready-to-use solutions for common SQL dialects, sometimes you need to create a custom parser for a specific SQL dialect or extend an existing one. ANTLR (ANother Tool for Language Recognition) shines in these scenarios, serving as a robust parser generator for SQL grammar.

One of ANTLR's greatest strengths is its ability to generate parsers in multiple target languages. ANTLR uses grammar files (with a .g4 extension) to define language syntax. These grammar files are human-readable and serve as both documentation and code. For SQL parsing, you can either:

- Use existing SQL grammar files from the community
- Modify existing grammars to support specific SQL dialect features
- Create your own grammar from scratch for highly specialized SQL dialects

At Bytebase, we build the database-specific parsers based on the [community grammar file](https://github.com/antlr/grammars-v4). Despite challenges—particularly around [performance](https://www.bytebase.com/blog/how-we-improved-sql-parser-speed-70x/), we've found this to be the most effective approach.

## Final Thoughts

SQL parsers may seem like a niche technical component, but they play a crucial role in database tools, query editors, data lineage systems, and many other applications.

Whether you opt for a database-specific parser for precise dialect support, a generic parser for flexibility across multiple databases, or ANTLR for custom parsing needs, we hope this overview helps you navigate the landscape of SQL parsers and find the right solution for your specific requirements.



## Relational Databases

| Database System      | Default Port | Notes                                                    |
| -------------------- | ------------ | -------------------------------------------------------- |
| MySQL / MariaDB      | 3306         | MySQL's classic port; MariaDB adopted the same           |
| PostgreSQL           | 5432         | Postgres convention since the early days                 |
| Oracle Database      | 1521         | The TNS Listener port; 2483/2484 for TCPS                |
| Microsoft SQL Server | 1433         | Default instance port; named instances use dynamic ports |
| IBM Db2              | 50000        | Standard port; additional services use 50001-50050 range |
| SQLite               | N/A          | File-based database with no network port                 |

## NoSQL Databases

| Database System | Default Port | Notes                                                        |
| --------------- | ------------ | ------------------------------------------------------------ |
| MongoDB         | 27017        | 27018 (shard server), 27019 (config server)                  |
| Redis           | 6379         | Often used with Sentinel (26379) and Cluster (16379)         |
| Cassandra       | 9042         | CQL native transport port; 7000 for inter-node communication |
| Elasticsearch   | 9200         | REST API; 9300 for inter-node communication                  |
| CouchDB         | 5984         | HTTP API; 6984 for HTTPS                                     |
| DynamoDB        | N/A          | AWS service with no direct port access                       |
| HBase           | 16000        | Master port; 16020/16030 for region servers                  |
| Couchbase       | 8091         | Web console; 8092 (API), 8093 (query), 11210 (data)          |
| InfluxDB        | 8086         | HTTP API port; 8088 for RPC service                          |
| ScyllaDB        | 9042         | CQL compatible port (same as Cassandra)                      |

## NewSQL

| Database System      | Default Port | Notes                                               |
| -------------------- | ------------ | --------------------------------------------------- |
| CockroachDB          | 26257        | SQL interface; 8080 for admin UI                    |
| TiDB                 | 4000         | SQL interface; 10080 for status                     |
| ClickHouse           | 9000         | Native protocol; 8123 for HTTP interface            |
| SingleStore (MemSQL) | 3306         | MySQL-compatible port; 9104 for Ops UI              |
| YugabyteDB           | 5433         | 5433 For YSQL; 9042 for YCQL (Cassandra compatible) |
| QuestDB              | 8812         | PostgreSQL wire protocol; 9000 for REST             |

## Graph Databases

| Database System | Default Port | Notes                                           |
| --------------- | ------------ | ----------------------------------------------- |
| Neo4j           | 7687         | Bolt protocol; 7474 for HTTP and 7473 for HTTPS |
| ArangoDB        | 8529         | HTTP API port                                   |
| Amazon Neptune  | N/A          | AWS service without direct port access          |
| JanusGraph      | N/A          | Uses underlying storage ports                   |
| TigerGraph      | 14240        | GraphStudio UI port                             |
| DGraph          | 8080         | HTTP port; 9080 for gRPC                        |

## Vector Databases

| Database System | Default Port | Notes                              |
| --------------- | ------------ | ---------------------------------- |
| Pinecone        | N/A          | Cloud service with API access only |
| Milvus          | 19530        | Data service port                  |
| Weaviate        | 8080         | REST API port                      |
| Qdrant          | 6333         | gRPC port; 6334 for REST API       |
| Chroma          | 8000         | API server port                    |

## Database Management Tools

| Tool            | Default Port | Notes                     |
| --------------- | ------------ | ------------------------- |
| phpMyAdmin      | 80/443       | Web server ports          |
| pgAdmin         | 80/443       | Web server ports          |
| MongoDB Compass | N/A          | Desktop client            |
| DBeaver         | N/A          | Desktop client            |
| Adminer         | 80/443       | Web server ports          |
| Bytebase        | 8080         | Web UI for database CI/CD |



## Introduction

Terraform has firmly established itself as the leading infrastructure as code (IaC) tool, enabling teams to define and provision infrastructure through declarative configuration files.

While Terraform's command-line interface serves as its primary interaction method, UI tools have emerged to address collaboration challenges, visualization needs, and governance requirements in enterprise environments.

This article explores the top four Terraform UI tools in 2025, examining their features, use cases, and how they can enhance your infrastructure management workflows.

## HashiCorp Terraform Cloud

![](/content/blog/top-terraform-ui-tools/hashicorp-terraform.webp)

Terraform Cloud is HashiCorp's official SaaS platform for Terraform, designed to bridge the gap between individual practitioners and enterprise teams.

**Key Features:**

- **Remote Terraform execution** with centralized state management
- **Version-controlled workflows** with integration to GitHub, GitLab, Bitbucket, and Azure DevOps
- **Collaborative plan reviews** with commenting functionality
- **Role-based access control** for enterprise users
- **Policy as Code** enforcement with Sentinel and OPA
- **Cost estimation** for planned infrastructure changes
- **Private module registry** for sharing reusable Terraform modules

**Best For:**

Organizations seeking an official, fully managed solution that scales from individual projects to enterprise-wide deployments. The tight integration with HashiCorp's ecosystem makes it particularly valuable for teams already invested in other HashiCorp tools.

**Pricing:**

Terraform Cloud offers three tiers:

- **Free**: For individuals and small teams (up to 5 users)
- **Team & Governance**: For mid-sized teams requiring collaboration features ($20 per user/month)
- **Business**: For enterprises needing advanced security and compliance features ($70 per user/month)

## Semaphore

![](/content/blog/top-terraform-ui-tools/semaphore.webp)

[Semaphore](https://github.com/semaphoreui/semaphore) is an open-source alternative to Terraform Cloud that provides a web UI for Terraform operations with a focus on team-based workflows and self-hosted deployment.

**Key Features:**

- **Web-based UI** for Terraform plan and apply operations
- **Task-based automation** for complex infrastructure workflows
- **Role-based access control** with fine-grained permissions
- **Audit logging** for compliance and tracking
- **Template-based deployment** with reusable components
- **Integration with Git repositories** for version control
- **Self-hosted architecture** for enhanced security and control

**Best For:**

Organizations that prefer self-hosted solutions with complete control over their infrastructure automation platform. Semaphore is particularly well-suited for teams with security requirements that prevent using cloud-based solutions.

**Pricing:**

Semaphore is open source and free to use, with the following options:

- **Community Edition**: Free and self-hosted
- **Enterprise Support**: Available through consulting partnerships

## Atlantis

![](/content/blog/top-terraform-ui-tools/atlantis.webp)

[Atlantis](https://github.com/runatlantis/atlantis) takes a unique approach by integrating Terraform workflows directly into pull request processes, with a lightweight server that automates Terraform plan and apply operations based on Git events.

**Key Features:**

- **Pull request automation** for Terraform workflows
- **Comment-driven operations** in GitHub, GitLab, or Bitbucket
- **Terraform plan visualization** within pull requests
- **Lock management** to prevent concurrent operations
- **Custom workflow definitions** for different repository structures
- **Webhook integration** with most Git providers
- **Container-based deployment** for easy hosting

**Best For:**

Developer-centric teams that prioritize Git-based workflows and want to integrate infrastructure changes directly into their existing code review processes. Atlantis shines in environments where developers manage infrastructure alongside application code.

**Pricing:**

Atlantis is completely open-source and free to use, with a vibrant community providing support and extensions.

## Scalr

![](/content/blog/top-terraform-ui-tools/scalr.webp)

[Scalr](https://www.scalr.com/) offers a comprehensive Terraform management platform designed for enterprise scale, with robust governance and collaboration features.

**Key Features:**

- **Multi-cloud management** across providers
- **Hierarchical organization** of environments (account, environment, workspace)
- **OPA-based policy engine** for governance
- **VCS integration** with major providers
- **Self-service infrastructure** through service catalog
- **Drift detection** and remediation
- **Advanced role-based access control** with custom roles
- **Private module registry** with approval workflows
- **Comprehensive API** for automation

**Best For:**

Large enterprises with complex organizational structures requiring granular control over Terraform deployments across multiple teams and environments. Scalr's hierarchical approach to infrastructure management makes it ideal for organizations with sophisticated governance requirements.

**Pricing:**

Scalr offers both self-hosted and SaaS options:

- **SaaS**: Starting at $50 per user/month with volume discounts
- **Self-hosted**: Custom enterprise pricing based on deployment size

## Feature Comparison

| Feature             | Terraform Cloud | Semaphore   | Atlantis      | Scalr               |
| ------------------- | --------------- | ----------- | ------------- | ------------------- |
| Deployment Model    | SaaS            | Self-hosted | Self-hosted   | SaaS or Self-hosted |
| State Management    | Built-in        | External    | External      | Built-in            |
| VCS Integration     | Comprehensive   | Basic       | Comprehensive | Comprehensive       |
| Module Registry     | ✅              | ❌          | ❌            | ✅                  |
| Policy Enforcement  | Sentinel/OPA    | Basic       | ❌            | OPA                 |
| Cost Estimation     | ✅              | ❌          | ❌            | ✅                  |
| API Availability    | Full            | Limited     | Limited       | Full                |
| Team Collaboration  | Advanced        | Basic       | PR-based      | Advanced            |
| Free Tier           | ✅ (limited)    | ✅          | ✅            | ❌                  |
| Enterprise Features | Comprehensive   | Basic       | Limited       | Comprehensive       |

## Choosing the Right Tool

When selecting a Terraform UI tool, consider the following factors:

1. **Team Size and Structure**: Larger, distributed teams benefit from the collaboration features in Terraform Cloud or Scalr.

2. **Governance Requirements**: Organizations with strict compliance needs should look to Terraform Cloud or Scalr's policy engine.

3. **Workflow Integration**: Developer-centric teams may prefer Atlantis for its tight integration with the pull request process.

4. **Deployment Preferences**: If you need to keep everything on-premises, Semaphore or Atlantis provide self-hosted options.

5. **Budget Constraints**: Open-source tools like Atlantis and Semaphore offer cost-effective solutions for teams with limited budgets.

## Conclusion

The Terraform ecosystem now offers diverse UI tools to enhance IaC workflows. Terraform Cloud provides the official, polished experience with direct HashiCorp integration. Semaphore offers a robust self-hosted alternative. Atlantis excels with Git-integrated workflows for developer-centric teams. Scalr delivers enterprise governance for complex organizations.

As IaC adoption grows, these tools will become increasingly vital for team collaboration while maintaining proper governance. Choose based on your specific team size, security requirements, existing workflows, and budget constraints.



## Introduction

PostgreSQL's `EXPLAIN` command reveals query execution plans but its raw output can be difficult to interpret. Several open-source tools help visualize these plans, making performance bottlenecks easier to identify.

This article examines the top PostgreSQL EXPLAIN tools in 2025, from built-in extensions to visual interfaces. These tools help database administrators, developers, and analysts optimize query performance across their database operations.

## Built-in PostgreSQL Tools

Before exploring external tools, it's important to understand the powerful built-in capabilities that PostgreSQL offers for query analysis.

### auto_explain

The auto_explain module provides a way to automatically log execution plans for slow queries, making it invaluable for identifying problematic queries in production environments without manual intervention.

#### Key Features

- Automatically logs execution plans for slow-running queries
- Configurable log level and minimum execution time thresholds
- Can capture nested statement execution plans
- Supports logging query parameters for complete context

#### Setup and Configuration

To enable auto_explain, you need to load it in your postgresql.conf file:

```sql
# Load the extension
shared_preload_libraries = 'auto_explain'

# Basic configuration
auto_explain.log_min_duration = '3s'  # Log queries taking over 3 seconds
auto_explain.log_analyze = on         # Include actual runtime statistics
auto_explain.log_buffers = on         # Include buffer usage statistics
auto_explain.log_timing = on          # Include timing information
auto_explain.log_verbose = on         # Use EXPLAIN VERBOSE format
auto_explain.log_nested_statements = on # Include nested statements
```

After changing these settings, a server restart is required. Once enabled, slow queries will automatically appear in your PostgreSQL logs with their execution plans.

#### Use Cases

Auto_explain excels in production environments where manual EXPLAIN execution isn't practical, allowing teams to identify slow queries without modifying application code. It enables gathering performance data over time to spot trends and provides valuable insights when debugging intermittent performance issues that might be difficult to reproduce manually.

### pg_stat_statements

While not directly an EXPLAIN tool, pg_stat_statements complements query analysis by tracking execution statistics across all SQL statements executed by the server.

#### Key Features

pg_stat_statements aggregates comprehensive execution statistics including time, rows processed, and I/O operations while normalizing similar queries to reduce tracking cardinality. It provides both cumulative and per-call statistics for performance trend analysis and separately tracks planning and execution time, offering deeper insight into query optimization opportunities.

#### Setup and Configuration

Enable pg_stat_statements in your postgresql.conf:

```sql
# Load the extension
shared_preload_libraries = 'pg_stat_statements'

# Configure
pg_stat_statements.max = 10000        # Maximum number of statements to track
pg_stat_statements.track = all        # Track all statements
```

After a server restart, create the extension in your database:

```sql
CREATE EXTENSION pg_stat_statements;
```

Query the statistics view to see execution metrics:

```sql
SELECT query, calls, total_exec_time, rows, mean_exec_time
FROM pg_stat_statements
ORDER BY total_exec_time DESC
LIMIT 10;
```

#### Use Cases

- Identifying frequently executed queries for optimization
- Finding queries with high cumulative execution time
- Analyzing query patterns across your application
- Complementing EXPLAIN analysis with real-world execution statistics

## Online Visualizers

While the raw EXPLAIN output provides detailed information, visual representations can make patterns and bottlenecks much easier to identify.

### Postgres Explain Visualizer 2 (pev2)

[Postgres Explain Visualizer 2](https://github.com/dalibo/pev2) (pev2) is the modern successor to the original pev tool, offering an interactive, browser-based visualization of PostgreSQL execution plans.

![pev2](/content/blog/top-open-source-postgres-explain-tool/pev2.webp)

Pev2 provides interactive, collapsible nodes for exploring complex plans with color-coded operation costs and timings. It distinguishes between operation types (scans, joins, etc.), offers filters and highlighting, and supports multiple visualization formats. While powerful as a standalone tool, pev2 reaches its full potential when integrated into comprehensive database management platforms like Bytebase.

Pev2 can be used directly in your browser:

1. Visit the [pev2 demo page](https://dalibo.github.io/pev2/)
2. Run `EXPLAIN (ANALYZE, BUFFERS, FORMAT JSON)` for your query
3. Copy the JSON output and paste it into pev2
4. Explore the visualized execution plan

#### Use Cases

- Analyzing complex query plans with many operations
- Educational tool for understanding query execution
- Comparing different execution strategies visually

## GUI Tools with EXPLAIN

Several database management tools integrate explain visualization capabilities, providing a seamless experience for query development and optimization.

### pgAdmin

[pgAdmin](https://www.pgadmin.org/) is the most popular open-source administration and development platform for PostgreSQL, offering comprehensive management capabilities including a powerful query tool with integrated EXPLAIN visualization.

pgAdmin integrates pev2 visualization in its query tool, offering EXPLAIN execution from the SQL editor. The tool provides visualization formats including graphical, text, and statistics views for individual query analysis.

#### How to Use

To analyze query performance with pgAdmin, connect to your database, open the Query Tool, write your SQL query, then click "Explain" from the toolbar. The execution plan appears in the Explain panel for basic analysis.

#### Use Cases

- Basic PostgreSQL database administration
- Individual query optimization
- General database management tasks

### Bytebase: the Collaborative Database DevSecOps Solution

[Bytebase](https://www.bytebase.com/) offers a comprehensive database DevSecOps platform that seamlessly integrates EXPLAIN visualization with team collaboration features, version control, and change management capabilities.

![bytebase](/content/blog/top-open-source-postgres-explain-tool/bytebase.webp)

Bytebase offers embedded pev2 visualization for query analysis while enabling team collaboration through sharing and discussing query plans. It provides version control for SQL queries and their execution plans, integrates with database change management workflows, and supports cross-environment query performance comparison to optimize databases across development stages.

#### How to Use

[Using Bytebase for query analysis](https://www.bytebase.com/docs/sql-editor/run-queries/#explain-query) is straightforward: connect your PostgreSQL database to the platform, open Bytebase SQL Editor, and write your query. Once ready, right-click the code area and select **Explain Query**.

![bb-explain-entry](/content/blog/top-open-source-postgres-explain-tool/bb-explain-entry.webp)

Simply click the **Visualize Explain** button at bottom right to generate and view the visualized execution plan. The collaborative nature of Bytebase allows you to share these plans with team members or save them for future reference, making it ideal for team-based database optimization.

![bb-explain](/content/blog/top-open-source-postgres-explain-tool/bb-explain.webp)

#### Use Cases

- **Collaborative Query Optimization**: Review and improve query performance as a team
- **Schema Change Analysis**: Track how database changes impact query execution plans
- **Environment Consistency**: Ensure queries perform similarly across dev, test, and prod
- **Performance Auditing**: Document query execution plans for compliance requirements
- **Knowledge Base Building**: Create shared repo of optimized queries and executions

## Comparison

| Tool               | Best For                 | Setup Complexity | Visual Richness  | Change Management | Version Control |
| ------------------ | ------------------------ | ---------------- | ---------------- | ----------------- | --------------- |
| auto_explain       | Production monitoring    | Medium           | Low (text-based) | ❌                | ❌              |
| pg_stat_statements | Statistical analysis     | Medium           | Low (tabular)    | ❌                | ❌              |
| pev2               | Detailed visual analysis | Low              | High             | ❌                | ❌              |
| pgAdmin            | Individual development   | Low              | Medium           | ❌                | ❌              |
| Bytebase           | Team collaboration       | Medium           | High             | ✅                | ✅              |

## Conclusion

PostgreSQL's EXPLAIN functionality truly shines when paired with proper visualization tools. While built-in extensions provide basic monitoring capabilities and standalone tools offer detailed analysis, modern database operations require more comprehensive solutions.

As databases grow larger and teams become more distributed, collaborative tools like Bytebase become essential. By combining powerful visualization with team collaboration, version control, and change management features, Bytebase bridges the gap between individual query optimization and team-based database governance, making it the preferred choice for organizations serious about PostgreSQL performance at scale.

## References

- [PostgreSQL auto_explain Documentation](https://www.postgresql.org/docs/current/auto-explain.html)
- [PostgreSQL pg_stat_statements Documentation](https://www.postgresql.org/docs/current/pgstatstatements.html)
- [Postgres Explain Visualizer 2 (pev2) GitHub Repository](https://github.com/dalibo/pev2)
- [pgAdmin Documentation - Query Tool](https://www.pgadmin.org/docs/pgadmin4/8.14/query_tool.html#explain-panel)
- [Bytebase Documentation](https://www.bytebase.com/docs/)



<HintBlock type="info">

This post is maintained by Bytebase, an open-source database Management Software. We update the post every year.

</HintBlock>

| Update History | Comment          |
| -------------- | ---------------- |
| 2025/03/14     | Initial version. |
| 2025/05/28     | Add Devtron.     |
| 2026/01/19     | Update for 2026. |

## What are the criteria?

When evaluating free and open-source Kubernetes dashboard solutions, several key criteria are essential to consider:

- **Open Source:** The software should be freely available and modifiable, allowing for community contributions and transparency.
- **Free:** The software should be free to use, modify, and distribute.

![star-history](/content/blog/top-open-source-kubernetes-dashboard/star-history.webp)

## Kubernetes Dashboard

![k8s-dashboard](/content/blog/top-open-source-kubernetes-dashboard/k8s-dashboard.webp)

Kubernetes Dashboard ([repo](https://github.com/kubernetes/dashboard)) is the official web-based UI for Kubernetes clusters. It allows users to manage applications running in the cluster and troubleshoot them, as well as manage the cluster itself.

**Key Features**

- **Cluster Overview:** Displays an overview of cluster health, workloads, and resources.
- **Workload Management:** Create, update, and delete Kubernetes objects such as Deployments, StatefulSets, and Jobs.
- **Pod & Container Monitoring:** View logs, resource consumption, and running status of individual pods.
- **Service & Networking Control:** Manage Services, Ingress, and Network Policies.
- **Storage Management:** View and manage Persistent Volumes (PVs) and Persistent Volume Claims (PVCs).
- **Role-Based Access Control (RBAC):** Supports authentication and access control using Kubernetes RBAC.

## Headlamp

![headlamp](/content/blog/top-open-source-kubernetes-dashboard/headlamp.webp)

[Headlamp](https://headlamp.dev/) ([repo](https://github.com/headlamp-k8s/headlamp)) an easy-to-use and extensible Kubernetes web UI, which can also be used as a desktop app. It's developed by Kinvolk (now part of Microsoft). It's a Cloud Native Computing Foundation (CNCF) Sandbox project.

Headlamp was created to blend the traditional feature set of other web UIs/dashboards (i.e., to list and view resources) with added functionality.

**Key Features**

- Vendor-independent / generic Kubernetes UI
- Works in-cluster, or locally as a desktop app
- Multi-cluster
- Extensible through plugins
- UI controls reflecting user roles (no deletion/update if not allowed)
- Clean & modern UI
- Cancellable creation/update/deletion operations
- Logs, exec, and resource editor with documentation
- Read-write / interactive (actions based on permissions)

## Lens

![lens](/content/blog/top-open-source-kubernetes-dashboard/lens.webp)

Designed for Developers and DevOps Engineers, [Lens](https://k8slens.dev/) ([repo](https://github.com/lensapp/lens)) provides an intuitive context-aware UI.

<HintBlock type="info">

Lens Desktop was originally an open-source Kubernetes IDE, but the open-source version has been retired and is no longer actively maintained. The Lens Desktop product is now developed and maintained by Mirantis, and contributions to Lens are now made via the Lens Extension API rather than direct modifications to the core product.

</HintBlock>

**Key Features**

- **Multi-Cluster Management:** Easily manage multiple Kubernetes clusters.
- **Real-Time Monitoring:** Built-in Prometheus for live CPU, memory, and network metrics.
- **Integrated Terminal:** Direct kubectl access with auto-complete.
- **Workload & Resource Management:** View and control pods, deployments, and services.
- **Helm Chart Support:** Install and manage Helm applications.
- **RBAC & Security Insights:** Manage access roles and detect security issues.
- **Lens Extensions API:** Customize with plugins and integrations.
- **Desktop App:** Available for Windows, macOS, and Linux.
- **Event Alerts & Troubleshooting:** Get real-time alerts and logs for debugging.

## Rancher

![rancher](/content/blog/top-open-source-kubernetes-dashboard/rancher.webp)

[Rancher](http://rancher.com/) ([repo](https://github.com/rancher/rancher)) is an open source container management platform built for organizations that deploy containers in production. Rancher makes it easy to run Kubernetes everywhere, meet IT requirements, and empower DevOps teams

<HintBlock type="info">

Rancher was developed by Rancher Labs, founded in 2014, to simplify Kubernetes management. In December 2020, it was acquired by SUSE, which continues to develop and support it as an open-source solution for multi-cluster, cloud-native, and edge Kubernetes management.

</HintBlock>

**Key Features**

- **Multi-Cluster Management:** Centrally manage Kubernetes clusters across cloud, on-prem, and edge.
- **Cluster Provisioning:** Deploy Kubernetes clusters on AWS, Azure, Google Cloud, and bare metal.
- **RBAC & Security:** Centralized Role-Based Access Control (RBAC) and security policies.
- **Built-in Monitoring & Logging:** Integrated Prometheus, Grafana, and Fluentd for insights.
- **Application Deployment:** Manage workloads using Helm charts, Rancher Apps, and GitOps.
- **CIS Benchmarking:** Automate security scans and compliance checks.
- **Rancher Fleet:** GitOps-based multi-cluster application management.
- **Edge Kubernetes Support:** Manage lightweight Kubernetes (K3s) for edge computing.

## KubeSphere

![kubeSphere](/content/blog/top-open-source-kubernetes-dashboard/kubesphere.webp)

[KubeSphere](https://github.com/kubesphere/kubesphere) ([repo](https://kubesphere.io/)) is a distributed operating system for cloud-native application management, using Kubernetes as its kernel. It provides a plug-and-play architecture, allowing third-party applications to be seamlessly integrated into its ecosystem. KubeSphere is also a multi-tenant container platform with full-stack automated IT operation and streamlined DevOps workflows.

**Key Features**

- 🧩 **Extensible Architecture** – Plugin-based and customizable.
- 🕸 **Cluster Provisioning** – Deploy on any infrastructure, including air-gapped setups.
- 🔗 **Multi-Cluster Management** – Centralized control for multiple Kubernetes clusters.
- 🤖 **DevOps & GitOps** – Built-in Argo CD, Jenkins, and CI/CD pipelines.
- 🔎 **Observability** – Integrated monitoring, logging, alerts, and auditing.
- 🌐 **Service Mesh** – Istio-based traffic control and microservices visualization.
- 💻 **App Store** – One-click Helm-based app deployment.
- 💡 **Edge Computing** – Manage applications on edge devices with KubeEdge.
- 🗃 **Storage & Networking** – Supports Ceph, NFS, OpenELB, Calico, Flannel, Kube-OVN.
- 🏢 **Multi-Tenancy & RBAC** – Secure workspaces with fine-grained access control.
- 🧠 **GPU Workload Scheduling** – GUI-based GPU management and monitoring.

## K9s

![k9s](/content/blog/top-open-source-kubernetes-dashboard/k9s.webp)

[K9s](https://k9scli.io/) ([repo](https://github.com/derailed/k9s)) provides a terminal UI (TUI) to interact with your Kubernetes clusters. The aim of this project is to make it easier to navigate, observe and manage your applications in the wild. K9s continually watches Kubernetes for changes and offers subsequent commands to interact with your observed resources.

**Key Features**

- **Terminal-Based UI** – Fast and lightweight, runs entirely in the command line.
- **Real-Time Monitoring** – Auto-refreshing view of pods, deployments, and resources.
- **Context-Aware Navigation** – Quickly switch between namespaces and cluster resources.
- **Built-in Log Viewer** – Live pod logs with filtering and search capabilities.
- **Hotkeys & Shortcuts** – Streamlined workflows for quick actions and debugging.
- **YAML Editing & Execution** – Modify Kubernetes resources without leaving the terminal.
- **Resource Metrics** – View CPU, memory, and network usage directly in the UI.
- **RBAC & Security Insights** – Manage user permissions and policies.
- **Plugin Support** – Extend K9s functionality with custom plugins.
- **Works with Any Kubernetes Distribution** – Compatible with K3s, AKS, EKS, GKE, and more.

## Devtron

![devtron](/content/blog/top-open-source-kubernetes-dashboard/devtron.webp)

[Devtron](https://devtron.ai/) ([repo](https://github.com/devtron-labs/devtron)) is an open-source, Kubernetes-native DevOps platform that gives teams a complete web dashboard for building, deploying, observing and securing their cloud-native applications. By unifying CI/CD, GitOps, observability and security in a single interface, Devtron removes the operational toil of stitching together disparate tools and lets developers focus on shipping reliable code.

**Key Features**

- **Web-Based Kubernetes Dashboard** – Visual, single-pane interface that replaces complex `kubectl` commands and surfaces cluster state, workloads and health at a glance.
- **End-to-End CI/CD & GitOps** – Drag-and-drop pipelines with declarative GitOps synchronisation enable automated, auditable releases from commit to production.
- **Multi-Cluster Visibility** – Manage and deploy workloads across multiple clusters and clouds from one console, with aggregated views of nodes, namespaces and services.
- **AI-Driven Troubleshooting** – Built-in AI analyses pod and application errors to suggest fixes and cut mean-time-to-resolution.
- **Progressive Delivery Strategies** – One-click canary, blue-green and rolling updates powered by Flagger and Istio to deploy changes safely.
- **Integrated DevSecOps** – Automated image, code and manifest scanning with policy gates to stop insecure builds before they ship.
- **Helm & Chart Hub** – Browse, customise and deploy Helm charts, with version management and diffing directly in the console.
- **Real-Time Metrics & Logs** – Stream application logs and view CPU, memory and network metrics without leaving the dashboard.
- **Fine-Grained RBAC & SSO** – Integrates with enterprise identity providers and lets admins define role-based permissions at namespace or resource level.
- **Extensible Ecosystem** – Hooks and integrations connect Devtron with popular tools across the SDLC for cost insights, tracing, alerting and more.



<HintBlock type="info">

This post is maintained by Bytebase, an open-source database Management Software. We update the post every year.

</HintBlock>

| Update History | Comment          |
| -------------- | ---------------- |
| 2025/03/12     | Initial version. |

[HashiCorp Terraform](https://www.terraform.io/) lets you define both cloud and on-prem resources in human-readable configuration files that you can version, reuse, and share. You can then use a consistent workflow to provision and manage all of your infrastructure throughout its lifecycle.

However, when it comes to managing Terraform code, there are many tools that can help. In this post, we will review the top 5 open source Terraform tools for 2025.

## Criteria

1. **Open Source**: The tool must be open source.
1. **Terraform**: The tool must support Terraform. However, HashiCorp transitioned Terraform's license from MPL v2 to BSL, affecting versions beyond v1.5.7. These tools support Terraform v1.5.7 or earlier, and advocate to use [OpenTofu](https://opentofu.org/) instead.

## Digger

![digger](/content/blog/top-terraform-tools/digger.webp)

[Digger](https://digger.dev/) is an open-source CI/CD orchestrator for Terraform. It spins up jobs in your existing CI (GitHub Actions, GitLab CI, etc.) instead of running its own CI-like infrastructure.

**License: Open Source (Apache 2.0)** - Digger is developed by DGGR LIMITED and is available under the Apache 2.0 license. While Digger itself is open source, the company offers an Enterprise Edition with additional features such as drift detection, role-based access control (RBAC) via Open Policy Agent (OPA), and audit log retention.

**Features**

- **CI Integration:** Runs Terraform within existing CI pipelines (GitHub Actions, GitLab CI, etc.)
- **Multi-Cloud Support:** Works with AWS, GCP, Azure, and integrates with various CI platforms
- **PR Automation:** Executes terraform commands based on pull requests with results posted as comments
- **Locking & Concurrency:** Prevents race conditions with PR-level locks
- **Policy & Compliance:** Enforces rules with Open Policy Agent and detects infrastructure drift
- **Plan Management:** Stores and persists plan outputs for review before application

## Checkov

![checkov](/content/blog/top-terraform-tools/checkov.webp)

[Checkov](https://www.checkov.io/) is a static code analysis tool for scanning infrastructure as code (IaC) files for misconfigurations that may lead to security or compliance problems.

Checkov includes more than 750 predefined policies to check for common misconfiguration issues. Checkov also supports the creation and contribution of custom policies. It scans cloud infrastructure provisioned using Terraform/OpenTofu, Terraform plan as well as other IaC solutions such as Cloudformation, Helm charts, Kustomize, Dockerfile, and etc. It detects security and compliance misconfigurations using graph-based scanning.

**License: Open Source (Apache 2.0)** - Checkov is developed by Bridgecrew, which was acquired by Palo Alto Networks. While Checkov itself is open source, it powers the commercial Prisma Cloud Application Security platform.

**Features**

- Over 1000 built-in policies for AWS, Azure and Google Cloud security and compliance
- Multi-platform scanning: Terraform/OpenTofu, CloudFormation, Kubernetes, Dockerfile, and more
- CI/CD integration: Scans Argo Workflows, Azure Pipelines, GitHub Actions, GitLab CI and other pipeline files
- Context-aware policies with graph-based scanning
- Supports multiple policy formats (Python and YAML)
- Detects credentials and secrets using pattern matching and entropy detection
- Evaluates provider settings and variables
- Supports suppression of false positives
- Multiple output formats: CLI, JSON, CSV, SARIF, and more

**Example**

```bash
checkov -f tf.json
Check: CKV_AWS_21: "Ensure all data stored in the S3 bucket have versioning enabled"
	FAILED for resource: aws_s3_bucket.customer
	File: /tf/tf1.json:224-268
	Guide: https://docs.prismacloud.io/en/enterprise-edition/policy-reference/aws-policies/s3-policies/s3-16-enable-versioning

		225 |               "values": {
		226 |                 "acceleration_status": "",
		227 |                 "acl": "private",
		228 |                 "arn": "arn:aws:s3:::mybucket",
```

## Infracost

![infracost](/content/blog/top-terraform-tools/infracost.webp)

[infracost.io](https://infracost.io/) is an open-source cost estimation tool for Terraform.

It enables a shift-left approach for cloud costs by providing cost estimates for Terraform before deployment. Additionally, it can check for FinOps best practices in accordance with the Well-Architected Frameworks of cloud vendors. Infracost works with AWS, Azure and Google Cloud.

**License: Open Source (MIT)** - Infracost is developed by Infracost Inc. and is available under the Apache 2.0 license. While the core Infracost CLI is open source, the company offers additional services through Infracost Cloud, which provides features like centralized management, budget enforcement, and policy automation.

**Features**

- **Cost Estimation:** Estimates the cost of your Terraform code before you run it
- **Cloud Providers:** Supports AWS, Azure, GCP, and Kubernetes
- **CI/CD Integration:** Works with GitHub Actions, GitLab CI, and more
- **Custom Formats:** Supports Terraform plan files and Terraform HCL
- **Detailed Reports:** Provides detailed reports with cost breakdowns

## Terramate

![terramate](/content/blog/top-terraform-tools/terramate.webp)

[Terramate](https://terramate.io/) is an open-source Infrastructure as Code (IaC) orchestration and management platform that turns your IaC into a lightning-fast platform.

It enables teams to build, deploy, manage and observe cloud infrastructure with Infrastructure as Code (IaC) tools such as Terraform, OpenTofu, Terragrunt, Kubernetes and others.

**License: Open Source (MPL 2.0)** - Terramate is developed by Terramate GmbH and is available under the Mozilla Public License 2.0. While the core Terramate CLI is open source, the company offers Terramate Cloud, a commercial platform that provides additional features such as observability, drift detection, asset inventory management, and policy enforcement.

**Features**

- **Stacks**: Groups related infrastructure resources for better management and reduced risk.
- **Code Generation**: Automates config creation to ensure consistency and minimize duplication.
- **Orchestration**: Graph-based execution optimizes deployments by targeting only changed stacks.
- **CI/CD Integration**: Works with GitHub Actions, GitLab CI/CD, and more for automated workflows.
- **Observability & Drift Detection**: Monitors infrastructure changes and ensures alignment.
- **Asset Management**: Centralized inventory of resources across teams and environments.
- **Developer Self-Service**: Enables easy provisioning of production-grade infrastructure.

## Terragrunt

![terragrunt](/content/blog/top-terraform-tools/terragrunt.webp)

[Terragrunt](https://terragrunt.gruntwork.io/) is a flexible orchestration tool that allows Infrastructure as Code to scale. It is a thin wrapper around Terraform that provides extra tools for working with multiple Terraform modules.

**License: Open Source (MIT)** - Terragrunt is developed by Gruntwork and is available under the MIT License. While Terragrunt itself is open source, Gruntwork offers commercial support and additional services through their platform, which provides a comprehensive suite of infrastructure as code (IaC) tools and modules.

**Features**

- **DRY Code Management:** Reuse Terraform/OpenTofu code across environments, reducing duplication.
- **Simplified Backend Configs:** Centralizes state management settings for easier maintenance.
- **Multi-Module Execution:** Runs commands across multiple modules for streamlined deployments.
- **Hooks & Automation:** Supports pre/post execution hooks for extended automation.
- **Dependency Graph Execution:** Ensures proper execution order for interdependent modules.
- **Structured Logging & Telemetry:** Supports JSON logs and OpenTelemetry for better observability.



Database schema comparison tools are essential for PostgreSQL developers and DBAs who need to identify differences between database environments and synchronize them effectively. In this article, we'll examine the top tools for comparing and syncing Postgres schemas in 2025.

## pg-schema-diff (Library)

[pg-schema-diff](https://github.com/stripe/pg-schema-diff) is an open-source schema comparison library created by Stripe. Unlike traditional GUI tools, pg-schema-diff is designed to be incorporated into your own applications or workflows.

Key features of pg-schema-diff include:

- Pure Golang implementation with zero native dependencies
- Generates SQL migration scripts to transform one schema into another
- Schema safety mechanisms to prevent data loss
- Highly customizable through a robust API
- Supports migrations between PostgreSQL versions

Despite requiring programming knowledge for integration and being limited to PostgreSQL databases only, pg-schema-diff is an excellent choice for development teams building schema comparison functionality into their custom tools or CI/CD pipelines—especially those working with TypeScript or JavaScript.

## Liquibase (CLI)

[Liquibase](https://docs.liquibase.com/commands/inspection/diff.html) is a command-line tool with database-independent library in its core for managing and applying database schema changes through a code-first approach. It excels in version-controlled database deployments using XML, YAML, JSON, or SQL formats, making it particularly valuable for developers with strong programming backgrounds.

![liquibase](/content/blog/top-postgres-schema-compare-tools/liquibase.webp)

Liquibase supports multiple database types beyond just Postgres. Its powerful command-line interface enables seamless automation and integration with most CI/CD pipelines ideal for DevOps environments. The tool generates detailed changelogs that document schema differences, providing clear visibility into database modifications. Users can choose between the open-source edition for basic needs or upgrade to the commercial version for additional capabilities. The [diff command](https://www.liquibase.com/blog/comparing-two-states-database-schema) compares two database schemas and reports the differences which can then be captured in changeset files, enabling proper version control and systematic deployment processes.

Liquibase does present some challenges though. The tool has a steeper learning curve compared to GUI alternatives, requiring technical familiarity to use effectively. The command-line interface may prove challenging for team members without technical expertise.

## pgAdmin (GUI Client)

[pgAdmin](https://www.pgadmin.org/docs/pgadmin4/8.14/schema_diff.html) is the most popular open-source administration and development platform for PostgreSQL. Its Schema Diff tool allows users to compare schemas between two databases visually.

pgAdmin offers an intuitive graphical interface for comparing schemas with side-by-side visualization of differences. Users can generate SQL scripts to synchronize schemas directly within the tool. As part of pgAdmin's comprehensive PostgreSQL management suite, the schema comparison functionality integrates seamlessly with other database management capabilities. Being free and open source makes it accessible to teams of all sizes without additional licensing costs.

![pgadmin](/content/blog/top-postgres-schema-compare-tools/pgadmin.webp)

Just as pg-schema-diff, pgAdmin works exclusively with Postgres databases, which potentially limits its usefulness in heterogeneous database environments. The comparison functionality is relatively basic compared to specialized tools, lacking some of the advanced features found in dedicated schema comparison solutions. Additionally, pgAdmin's visual approach makes it less suitable for automation in continuous integration and deployment pipelines.

pgAdmin is ideal for database administrators who want a quick visual way to compare schemas without investing in additional tools, especially if they're already using pgAdmin for other Postgres management tasks.

## Bytebase (All-in-one Platform)

[Bytebase](https://www.bytebase.com/) is a database DevSecOps platform that offers [schema synchronization](https://docs.bytebase.com/change-database/synchronize-schema/) as part of its broader database change management capabilities.

![bytebase](/content/blog/top-postgres-schema-compare-tools/bytebase.webp)

Bytebase provides a comprehensive suite of tools centered around database change management with an intuitive visualization system that clearly highlights schema differences. Its platform enables one-click schema synchronization between environments while maintaining detailed change history tracking and seamless version control integration. For organizations with formal processes, Bytebase incorporates approval workflows for schema changes, ensuring proper governance throughout the development lifecycle.

![bytebase-sync](/content/blog/top-postgres-schema-compare-tools/bytebase-sync.webp)

Unlike some tools limited to specific database systems, Bytebase supports multiple database types including PostgreSQL, MySQL, and others. The platform is fully GitOps-ready, allowing teams to manage database changes alongside application code. Its schema sync feature is particularly powerful, enabling teams to compare and synchronize database schemas across different environments (dev, staging, production) with built-in safeguards and approval processes.

While highly capable, Bytebase is more focused on enterprise database change management than just comparison, which may mean it offers more functionality than needed for simple comparison tasks. This makes Bytebase ideal for development teams and organizations seeking a comprehensive database DevOps solution with governance features, rather than just a standalone comparison tool.

## Summary

Each of these tools offers different approaches to PostgreSQL schema comparison:

| Tool           | Type       | Best For                           | Automation | User Interface      |
| -------------- | ---------- | ---------------------------------- | ---------- | ------------------- |
| pg-schema-diff | Library    | Developers building custom tooling | High       | None (programmatic) |
| Liquibase      | CLI        | DevOps teams                       | High       | Command-line        |
| pgAdmin        | GUI Client | PostgreSQL DBAs                    | Low        | Graphical           |
| Bytebase       | Platform   | DevOps teams                       | High       | Web-based           |

Your choice should depend on several factors:

- Whether you need a visual interface or prefer command-line/programmatic tools
- If you need to integrate schema comparisons into CI/CD pipelines
- Whether you work exclusively with PostgreSQL or multiple database types
- If you need additional features like version control, approval workflows, etc.

## Further Readings

- [Top MySQL Schema Compare Tool to Diff and Sync Database](/blog/top-mysql-schema-compare-tools/)
- [Securing Database Change: From Bastion to Integrated Platforms](/blog/top-database-change-management-tools/)



<HintBlock type="info">

This post is maintained by Bytebase, an open-source database Management Software. We update the post every year.

</HintBlock>

| Update History | Comment          |
| -------------- | ---------------- |
| 2025/03/10     | Initial version. |

Infrastructure as Code (IaC) tools have revolutionized how organizations deploy and manage their cloud infrastructure, enabling teams to automate provisioning and ensure consistency across environments. In this guide, we explore the top IaC solutions for 2025 that help DevOps and platform engineering teams automate infrastructure at scale.

## Criteria

We evaluate IaC tools based on:

1. **Adoption**: Industry popularity and community support.
1. **Capabilities**: Core features and syntax approach.
1. **Ecosystem**: Cloud provider support and integrations.
1. **Licensing Model**: Open-source availability versus commercial offerings.

## Terraform

[HashiCorp Terraform](https://www.terraform.io/) is the GOAT of IAC. It lets you define both cloud and on-prem resources in human-readable configuration files that you can version, reuse, and share. You can then use a consistent workflow to provision and manage all of your infrastructure throughout its lifecycle. Terraform can manage low-level components like compute, storage, and networking resources, as well as high-level components like DNS entries and SaaS features.

**License: Commercial (Business Source License)** - Terraform was open-source under the Mozilla Public License (MPL) until August 2023, when HashiCorp changed Terraform's license to the Business Source License (BSL). This licensing change led to the creation of [OpenTofu](https://opentofu.org/), an open-source fork of Terraform maintained by the Linux Foundation.

Terraform uses configuration files (written in HashiCorp Configuration Language, HCL) to describe the desired infrastructure state. It then automatically provisions and manages resources based on that configuration.

**Features**

- Declarative Configuration: Defines infrastructure using HCL, focusing on what to build rather than how.
- Infrastructure as Code (IaC): Version-controlled, reusable, and Git-friendly infrastructure management.
- Multi-Cloud & Multi-Provider: Supports AWS, Azure, GCP, Kubernetes, VMware, and more.
- Execution Plan (terraform plan): Previews changes before applying, reducing risks.
- State Management: Tracks infrastructure changes via a state file (terraform.tfstate).
- Dependency Handling: Manages resource creation order automatically.
- Modular & Reusable: Supports Terraform modules for efficient, reusable configurations.
- Immutable Infrastructure: Ensures consistency by replacing resources when needed.
- Automation Integration: Works with Ansible, Chef, Puppet for additional provisioning.

**Example**

```hcl
# Define the AWS provider
provider "aws" {
  region = "us-east-1"
}

# Create an EC2 instance
resource "aws_instance" "example" {
  ami           = "ami-0c55b159cbfafe1f0"  # Amazon Linux 2 AMI (change based on region)
  instance_type = "t2.micro"              # Small, free-tier eligible instance

  tags = {
    Name = "MyTerraformInstance"
  }
}

# Output the instance public IP
output "public_ip" {
  value = aws_instance.example.public_ip
}
```

## Pulumi

[Pulumi](https://www.pulumi.com/) enables developers to define, deploy, and manage cloud infrastructure using real programming languages like TypeScript, Python, Go, C#, and Java instead of domain-specific languages (DSL) like Terraform's HCL. This approach allows you to use the full power of the programming language to manage your infrastructure once restricted by the Terraform DSL.

**License: Open Source (Apache 2.0) with Commercial Offerings** - Pulumi's core functionality is open source, while their enterprise features are available through paid plans.

Pulumi supports multiple cloud providers, including AWS, Azure, Google Cloud, Kubernetes, and on-premises environments, offering a modern approach to IaC with native support for software engineering best practices.

**Features**

- Real programming languages: TypeScript, Python, Go, C#, and Java.
- Multi-Cloud & Multi-Provider: Supports AWS, Azure, Google Cloud, Kubernetes, and on-premises environments.
- Native support for software engineering best practices.
- Supports Terraform modules for efficient, reusable configurations.
- Immutable Infrastructure: Ensures consistency by replacing resources when needed.
- Automation Integration: Works with Ansible, Chef, Puppet for additional provisioning.

**Example**

```js
import * as pulumi from '@pulumi/pulumi';
import * as aws from '@pulumi/aws';

// Create an AWS EC2 instance
const instance = new aws.ec2.Instance('myInstance', {
  ami: 'ami-0c55b159cbfafe1f0', // Amazon Linux 2 AMI (change based on region)
  instanceType: 't2.micro', // Small, free-tier eligible instance
  tags: { Name: 'MyPulumiInstance' },
});

// Export the instance public IP
export const publicIp = instance.publicIp;
```

## Argo CD

[Argo CD](https://github.com/argoproj/argo-cd) is a declarative, GitOps continuous delivery tool for Kubernetes.

**License: Open Source (Apache 2.0)** - Argo CD is a Cloud Native Computing Foundation (CNCF) graduated project.

Argo CD follows the GitOps pattern of using Git repositories as the source of truth for defining the desired application state. It supports multiple configuration formats including Kustomize, Helm charts, Jsonnet, and plain YAML manifests. [Akuity](https://www.akuity.io/) is a commercial offering that extends Argo CD with additional features.

**Features**

- Automated deployment and syncing of applications to target environments
- Multi-cluster management capabilities
- Automated configuration drift detection and visualization
- Web UI with real-time application activity monitoring
- Robust access controls with SSO integration and RBAC policies
- Advanced deployment strategies with PreSync, Sync, and PostSync hooks
- CLI for automation and CI integration

![argocd](/content/blog/top-infrastructure-as-code-iac-tools/argocd.webp)

## Crossplane

[Crossplane](https://www.crossplane.io/) is an open source Kubernetes extension that transforms your Kubernetes cluster into a universal control plane.

**License: Open Source (Apache 2.0)** - Crossplane is a Cloud Native Computing Foundation (CNCF) incubating project.

Crossplane lets you manage anything, anywhere, all through standard Kubernetes APIs. Crossplane can even let you order a pizza directly from Kubernetes. If it has an API, Crossplane can connect to it.

With Crossplane, platform teams can create new abstractions and custom APIs with the full power of Kubernetes policies, namespaces, role based access controls and more. Crossplane brings all your non-Kubernetes resources under one roof.

**Features**

- Declarative configuration.
- Unify application and infrastructure configuration and deployment.
- One source of truth for infrastructure configuration and setup.
- Automate operational tasks with reconciling controllers.
- Built with high levels of extensibility.
- A strong separation of concerns.

![crossplane](/content/blog/top-infrastructure-as-code-iac-tools/crossplane.webp)

## Checkov

[Checkov](https://www.checkov.io/) is a static code analysis tool for scanning infrastructure as code (IaC) files for misconfigurations that may lead to security or compliance problems.

**License: Open Source (Apache 2.0)** - Checkov is developed by Bridgecrew, which was acquired by Palo Alto Networks. While Checkov itself is open source, it powers the commercial Prisma Cloud Application Security platform.

Checkov includes more than 750 predefined policies to check for common misconfiguration issues. Checkov also supports the creation and contribution of custom policies.

It scans cloud infrastructure provisioned using **Terraform, Terraform plan, Cloudformation, AWS SAM, Kubernetes, Helm charts, Kustomize, Dockerfile, Serverless, Bicep, OpenAPI, ARM Templates, or OpenTofu** and detects security and compliance misconfigurations using graph-based scanning.

It performs Software Composition Analysis (SCA) scanning which is a scan of open source packages and images for Common Vulnerabilities and Exposures (CVEs).

Checkov also powers **Prisma Cloud Application Security**, the developer-first platform that codifies and streamlines cloud security throughout the development lifecycle. Prisma Cloud identifies, fixes, and prevents misconfigurations in cloud resources and infrastructure-as-code files.

**Features**

- Over 1000 built-in policies for AWS, Azure and Google Cloud security and compliance
- Multi-platform scanning: Terraform, CloudFormation, Kubernetes, Dockerfile, Serverless, ARM, and more
- CI/CD integration: Scans Argo Workflows, Azure Pipelines, GitHub Actions, GitLab CI and other pipeline files
- Context-aware policies with graph-based scanning
- Supports multiple policy formats (Python and YAML)
- Detects credentials and secrets using pattern matching and entropy detection
- Evaluates provider settings and variables
- Supports suppression of false positives
- Multiple output formats: CLI, JSON, CSV, SARIF, and more

**Example**

```bash
checkov -f tf.json
Check: CKV_AWS_21: "Ensure all data stored in the S3 bucket have versioning enabled"
	FAILED for resource: aws_s3_bucket.customer
	File: /tf/tf1.json:224-268
	Guide: https://docs.prismacloud.io/en/enterprise-edition/policy-reference/aws-policies/s3-policies/s3-16-enable-versioning

		225 |               "values": {
		226 |                 "acceleration_status": "",
		227 |                 "acl": "private",
		228 |                 "arn": "arn:aws:s3:::mybucket",
```

## DNSControl

DNS configuration mistakes often draw the most blood. Look at [Meta](https://engineering.fb.com/2021/10/04/networking-traffic/outage/), [Cloudflare](https://blog.cloudflare.com/cloudflare-outage-on-july-17-2020/). So you really need to consider IaC for DNS management.

[DNSControl](https://github.com/StackExchange/dnscontrol) is a system for maintaining DNS zones - Infrastructure as code for DNS!

**License: Open Source (MIT)** - DNSControl was developed by Stack Exchange and is freely available under the MIT license.

It has two parts: a domain specific language (DSL) for describing DNS zones plus software that processes the DSL and pushes the resulting zones to DNS providers such as Route53, Cloudflare, and Gandi. It can send the same DNS records to multiple providers. It even generates the most beautiful BIND zone files ever. It runs anywhere Go runs (Linux, macOS, Windows). The provider model is extensible, so more providers can be added.

**Features**

- Less error-prone than editing a BIND zone file.
- More reproducible than clicking buttons on a web portal.
- Easily switch between DNS providers.
- Adopt CI/CD principles to DNS!
- Adopt PR-based updates.
- Variables save time.
- Macros.
- Control Cloudflare from a single source of truth.
- Keep similar domains in sync with transforms and other features.
- It is extendable.

**Example**

```js
// define our registrar and providers
var REG_NAMECOM = NewRegistrar('name.com');
var r53 = NewDnsProvider('r53');

D(
  'example.com',
  REG_NAMECOM,
  DnsProvider(r53),
  A('@', '1.2.3.4'),
  CNAME('www', '@'),
  MX('@', 5, 'mail.myserver.com.'),
  A('test', '5.6.7.8'),
);
```



## From DevOps to Platform Engineering

**DevOps** emerged around 2009 as a cultural and professional movement focused on breaking down silos between development and operations teams. It emphasized collaboration, automation, and shared responsibility for the entire software lifecycle.

**Platform Engineering**, on the other hand, emerged more recently as organizations realized that while DevOps principles were valuable, their implementation often led to cognitive overload for developers. Instead of expecting every developer to become an expert in infrastructure, monitoring, and operations, platform engineering focuses on creating internal developer platforms (IDPs) that abstract away complexity.

The evolution looked something like this:

- Traditional IT: Separate dev and ops teams with conflicting goals
- DevOps: Integrated teams sharing responsibilities and tools
- Platform Engineering: Specialized teams building self-service platforms that empower developers

The key distinction is that DevOps engineers primarily focus on enabling automation and collaboration, while platform engineers focus on building products for internal developer consumption.

## Months 1-2: Technical Basics

<HintBlock type="info">

For platform engineering tools, check out our 2-part series guide:

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

</HintBlock>

### Infrastructure as Code (IaC)

Learn declarative tools like [Terraform](https://www.terraform.io/). Start with small projects, gradually building to complex, modular infrastructure definitions. Practice state management and version control for infrastructure.

_Further Reading: [Terraform Best Practices](https://www.terraform-best-practices.com/)_

### Containerization

Move beyond basic Docker to understand image optimization, [multi-stage builds](https://docs.docker.com/build/building/multi-stage/), and security scanning. Create standardized Dockerfiles and container patterns for different application types.

### Networking

Learn concepts like service mesh implementations, ingress control, and network policies. Learn how to design secure, scalable networking for containerized applications.

### Orchestration

Dive into Kubernetes fundamentals and architecture. Set up a cluster, deploy applications, and understand the control plane. Explore Kubernetes custom resources and operators.

_Further Reading: [Kubernetes Learning Path](https://kubernetes.io/docs/tutorials/kubernetes-basics/)_

### Cloud Platforms

Develop working knowledge of at least one major cloud provider (AWS, Azure, or GCP). Learn core services for compute, networking, storage, and identity. Practice building environments using both console and IaC.

### Developer Empathy

Shadow application developers to understand their workflows and pain points. Identify repetitive tasks that could be automated or simplified.

## Month 3-4: Real-world Practice

### CI/CD Pipelines

Build automated delivery pipelines that support the entire software lifecycle. Create standardized templates that enforce quality gates while remaining customizable.

### Observability

Implement observability solutions using tools like Prometheus, Grafana, and the ELK stack. Design dashboards that provide actionable insights for both platform and application teams.

_Further Reading: [Observability Glossary](https://www.honeycomb.io/getting-started/observability-glossary)_

### Security

Embed security throughout the platform with robust authentication, RBAC, and automated vulnerability scanning. Learn to implement security as code using tools like [OPA](https://www.openpolicyagent.org/).

_Further Reading: [Vault by HashiCorp](https://developer.hashicorp.com/vault/tutorials), [Container Security by Snyk](https://snyk.io/learn/container-security/)_

### Technical Documentation

Practice writing clear, accessible documentation for the systems you build. Focus on both "how-to" guides and architectural decision records.

_Further Reading: [Google's Technical Writing Courses](https://developers.google.com/tech-writing), [The Documentation System](https://docs.divio.com/documentation-system/)_

## Month 5-6: Platform Thinking

### API Design

Practice designing APIs that are easy to understand, self-documenting, and follow consistent patterns.

_Further Reading: [Google API Improvement Proposal](https://google.aip.dev/1)_

### Service Architecture

Study modern service patterns and communication models. Understand how to design maintainable microservices with clear boundaries and contracts. Practice designing resilient, scalable systems. Learn to evaluate tradeoffs between different architectural approaches and document your decisions.

_Further Reading: [AWS Architecture Center](https://aws.amazon.com/architecture/)_

### Cross-team Collaboration

Actively engage with security, operations, and development teams to understand their requirements and constraints. Practice being a technical translator between groups.

## Ongoing

### Abstraction Design

Work on creating the right level of abstraction for developer interfaces. Practice hiding complexity while providing necessary control points.

### Self-service & Golden Paths

Build self-service capabilities that empower teams while ensuring compliance. Design opinionated workflows that make the right way the easy way.

_Further Reading: [Backstage](https://backstage.io/docs/overview/what-is-backstage/)_

### Metrics and Measurement

Establish metrics that demonstrate platform value, from reliability indicators to developer productivity measurements. Use data to identify which capabilities deliver the most impact.

_Further Reading: [DORA Metrics Explained](https://cloud.google.com/blog/products/devops-sre/using-the-four-keys-to-measure-your-devops-performance)_

### Cost Optimization

Implement cloud spending controls through tagging strategies, right-sizing resources, and spot instance usage. Create transparency around resource consumption and help teams understand their cost impact.

_Further Reading: [AWS Cost Optimization Pillar](https://docs.aws.amazon.com/wellarchitected/latest/cost-optimization-pillar/welcome.html), [Kubernetes Cost Optimization](https://www.kubecost.com/kubernetes-cost-optimization/)_

### Technical Roadmapping

Create a vision for platform evolution that balances immediate needs with long-term objectives. Learn to prioritize features based on impact and communicate the roadmap to stakeholders.

_Further Reading: [Netflix Paved Road](https://netflixtechblog.com/full-cycle-developers-at-netflix-a08c31f83249)_

## AI and Continuous Learning

The platform engineering space is evolving at breakneck speed, particularly as AI reshapes how we build and operate systems. What looks cutting-edge today may be table stakes tomorrow. AI-powered infrastructure management, intelligent observability, and generative code platforms are already transforming how platform teams operate. Today's platform engineers must embrace a mindset of continuous learning—not just completing this 6-month roadmap and stopping, but establishing habits that keep you at the forefront of the field.



Open-source database management tools are critical for organizations handling sensitive data. With transparent codebases that can be audited, modified, and secured to exact specifications, these solutions offer unparalleled control and trust. This article examines the top open-source database management solutions for 2025.

<HintBlock type="info">

This post is maintained by Bytebase, an open-source database Management Software. We update the post every year.

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| Update History | Comment          |
| -------------- | ---------------- |
| 2025/03/04     | Initial version. |

## What are the criteria?

When evaluating open-source database management software, several key criteria are essential to consider:

- **Open Source:** The software should be freely available and modifiable, allowing for community contributions and transparency.
- **Multi-database Support:** The ability to manage and interact with multiple types of databases is crucial for versatility.
- **Cross-platform:** Compatibility with various operating systems ensures broader usability and integration.
- **Feature-rich:** Comprehensive features that cater to diverse database management needs, from basic operations to advanced functionalities.

![star-history](/content/blog/top-open-source-database-management-software/star-history.webp)

## Beekeeper Studio

[Beekeeper Studio](https://www.beekeeperstudio.io/) is a database manager and SQL GUI. It is a desktop app that is easy to use. It's a visual alternative to command line tools like psql or mysql, while also providing a bunch of useful extra features.

#### Supported databases

It supports 10+ databases like PostgreSQL, SQLite, MySQL, MariaDB, TiDB, SQL Server and etc.

#### Pricing

- **Beekeeper Studio** - The full version of Beekeeper Studio.
- **Beekeeper Studio Community Edition** - A free and open source version of Beekeeper Studio that supports fewer databases and contains fewer features.

#### Key Features

- **SQL Editor:** Quickly iterate on a SQL query, view and visualize results, and share with a colleague.
- **Data Browser:** A spreadsheet like interface to view, navigate, search, and edit your data.
- **Table Creator & Editor:** An easy to use no-code interface to create and alter tables, indexes, foreign keys, and more.
- **Database Import & Export:** Convert data to/from CSVs, JSON, and more.
- **Online Sync & Collaboration:** Work across multiple devices, or share your connections and queries with others.

![beekeeper](/content/blog/top-open-source-database-management-software/beekeeper.webp)

## Adminer

[Adminer](https://www.adminer.org/) is a database management in a single PHP file, while its editor - Admin Editor is both easy-to-use and user-friendly database data editing tool written in PHP. It is suitable for common users, as it provides high-level data manipulation.

#### Supported databases

It supports 10+ databases like MySQL, PostgreSQL, MariaDB, SQLite, Elasticsearch, SimpleDB, Firebird, ClickHouse and etc.

#### Pricing

Free.

#### Key Features

- Database Connection and Management
- Index and Foreign Key Managemen
- Data Manipulation and Export
- Process and User ManagementProcess and User Management

![adminer](/content/blog/top-open-source-database-management-software/adminer.webp)

## HeidiSQL

[HeidiSQL](https://www.heidisql.com/) is free software for people who work with databases, and aims to be intuitive to use. Once connected, you can edit data and structures in these databases. Invented in 2002 by Ansgar, HeidiSQL is still a popular tool for MariaDB and MySQL, and actively maintained.

#### Supported databases

It supports 7 databases like MariaDB, MySQL, Microsoft SQL, PostgreSQL, SQLite, Interbase and Firebird.

#### Pricing

Free.

#### Key Features

- Create and edit tables, views, stored routines, triggers and scheduled events.
- Generate nice SQL-exports, compress these afterwards, or put them on the clipboard.
- Export from one server/database directly to another server/database
- Export table rows as CSV, HTML, XML, SQL, LaTeX, Wiki Markup and PHP Array

![heidisql](/content/blog/top-open-source-database-management-software/heidisql.webp)

## Bytebase

[Bytebase](https://www.bytebase.com/) is an all-in-one solution for database development lifecycle management. It replaces fragmented database tools and unifies the workflow in a single place including schema migrations, adhoc changes, just-in-time database access and data masking.

#### Supported databases

It supports 20+ mainstream databases like MySQL, PostgreSQL, SQL Server, Oracle, MongoDb and etc.

#### Pricing

- **Bytebase Community Plan** - The free version of Bytebase that supports fewer features.
- **Bytebase Pro Plan** - The pro version of Bytebase that supports more team collaboration features.
- **Bytebase Enterprise Plan** - The enterprise version of Bytebase that supports more enterprise-level security and compliance features.

#### Key Features

- **Database CI/CD:** Batch change, schema synchronization, SQL review, custom approval, 1-click rollback, change history and more.
- **GitOps:** Manage database schema changes using Git.
- **SQL Editor:** Query, share results, export data, AI assistant and more.
- **Database Permission:** Manage database permissions and roles, e.g. just-in-time database access.
- **Dynamic Data Masking:** Mask sensitive data in your database based on semantic type rules.
- **Single Sign-On:** Securely sign in to Bytebase using your existing identity provider.
- **Terraform Provider:** Automate configuration with Terraform.
- **API:** Automate database management with the Bytebase API.

![bytebase](/content/blog/top-open-source-database-management-software/bytebase.webp)


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