This post is maintained by Bytebase, an open-source database DevSecOps tool that can manage both Amazon RDS and DynamoDB. We update the post every year.
| Update History | Comment |
|---|---|
| 2025/05/19 | Initial version. |
Amazon's flagship database services cater to different needs:
- Amazon RDS: Managed relational databases (SQL)
- DynamoDB: Serverless NoSQL solution
This comparison helps you choose the right service for your 2025 projects.
Amazon RDS Overview
Amazon RDS is a fully managed service that simplifies deploying and scaling relational databases in the cloud. Since its 2009 launch, it has evolved to support six major engines: MySQL, PostgreSQL, MariaDB, Oracle, SQL Server, and Amazon Aurora (MySQL/PostgreSQL compatible).
RDS automates routine tasks like provisioning, backups, patching, and recovery, freeing developers to focus on building applications. It also offers high availability, automated backups, and flexible scaling for compute and storage. With native engine support and a consistent management experience, RDS is ideal for teams with SQL expertise or relational workloads.
Amazon DynamoDB Overview
Amazon DynamoDB is a serverless, fully managed NoSQL database launched in 2012, built for internet-scale applications. Based on Amazon's internal Dynamo system, it addresses the scalability challenges of traditional relational databases.
DynamoDB handles all infrastructure tasks - no provisioning, patching, or scaling required — and delivers single-digit millisecond performance at any scale. It supports both key-value and document data models, making it ideal for high-traffic apps, gaming, and IoT.
Designed for availability and partition tolerance, it also supports strong consistency when needed. Its flexible schema enables rapid development, and deep AWS integration (with services like Lambda, AppSync, and Kinesis) makes it a top choice for serverless and event-driven architectures.
Comparison Table
| Feature | Amazon RDS | Amazon DynamoDB |
|---|---|---|
| Type | Fully managed relational database service | Serverless NoSQL key-value and document database |
| Database Model | Relational (SQL) | Key-value and document (NoSQL) |
| Supported Engines | MySQL, PostgreSQL, MariaDB, Oracle, SQL Server, Aurora | DynamoDB (proprietary) |
| Scaling | Vertical scaling with instance resizing, read replicas for horizontal read scaling | Automatic, unlimited horizontal scaling |
| Data Structure | Structured data with schema enforcement | Schema-flexible with required primary key |
| Query Capabilities | Full SQL support with complex joins, aggregations, and transactions | Limited query expressions with PartiQL support |
| Global Distribution | Cross-region read replicas | Global Tables for multi-region replication |
| Pricing Model | Instance-based with storage and I/O charges | Pay-per-request or provisioned capacity |
| Security | AWS IAM, VPC, encryption at rest and in transit | AWS IAM integration, encryption at rest and in transit |
| Performance | Millisecond to sub-millisecond response times | Single-digit millisecond response times |
| Use Cases | Traditional applications requiring relational data model and SQL | High-scale applications with simple access patterns |
| 2025 Updates | Zero-ETL integration, Graviton3-based instances | Reduced on-demand pricing, improved global tables |
Key Comparison Points
Data Model and Schema Flexibility
Amazon RDS uses a traditional relational model with fixed schemas, requiring tables, columns, and relationships to be defined upfront. This ensures strong data integrity through constraints, transactions, and normalization. While ideal for complex relationships, schema changes can be time - consuming and may require downtime. Some engines like PostgreSQL support JSON, but the model remains primarily relational.
Amazon DynamoDB uses a flexible NoSQL model combining key-value and document structures. Items can have different attributes, with only a primary key required. This enables agile schema evolution without migrations. Data is stored as JSON - like documents with up to 32 nested levels, but each item is limited to 400KB. However, without native relationships, consistency must be handled in application logic, often requiring denormalization.
Summary: RDS is ideal for structured, relational data with strict integrity needs. DynamoDB is better for applications requiring schema flexibility and hierarchical data modeling. Choose based on whether consistency or agility is the priority.
Deployment and Management
Amazon RDS runs on provisioned infrastructure, where you choose instance types, storage, and scaling options. While many tasks are automated, you still manage sizing and performance. Deployment options include Single-AZ, Multi-AZ for high availability, and Read Replicas for scaling. Vertical scaling often requires downtime. As of 2025, RDS offers better auto-scaling but still needs more manual oversight than serverless solutions.
Amazon DynamoDB is fully serverless — no servers to manage or provision. It scales automatically and handles all maintenance behind the scenes. You can choose between:
- Provisioned Capacity (with optional auto-scaling)
- On-Demand Capacity (pay-per-request, scales instantly to zero)
This model offers minimal operational burden and is ideal for variable workloads, though with less infrastructure control.
Summary: DynamoDB simplifies operations with zero infrastructure management, perfect for dynamic workloads. RDS offers more control and flexibility but requires ongoing management and capacity planning.
Scalability and Performance
Amazon RDS scales vertically (larger instances), horizontally (read replicas), and by increasing storage. Performance depends on instance type, with top-tier options offering strong results. However, it has limits:
- Write scaling is tied to the primary instance
- Vertical scaling usually requires downtime (except Aurora)
- Cross-region replication adds latency
RDS works well for steady workloads but has constraints at massive scale.
Amazon DynamoDB offers near-infinite horizontal scaling. It automatically partitions data and adjusts throughput, delivering consistent single-digit millisecond performance — even at millions of requests per second. Features include:
- Auto-scaling for traffic spikes
- No size or request volume limits
- Global Tables for low-latency, multi-region replication
Designing effective partition keys is key to avoiding performance bottlenecks.
Summary: DynamoDB excels in scalability and is ideal for write-heavy or unpredictable workloads. RDS is reliable for moderate, predictable workloads but can't match DynamoDB’s scale-out capabilities.
Query Capabilities
Amazon RDS offers full SQL support, enabling complex queries with joins, subqueries, aggregations, window functions, and stored procedures. This makes it ideal for analytics, reporting, and multi-table operations. However, performance can degrade at scale without careful optimization and indexing.
Amazon DynamoDB supports key-based access and simple queries on primary keys or indexes. While PartiQL adds SQL-like syntax, it lacks support for joins, subqueries, and complex aggregations. Scan operations are available but costly and not recommended for large datasets.
Summary: RDS excels at complex querying and in-database analytics. DynamoDB is optimized for fast, simple access patterns at scale. Choose based on your query complexity and whether analytics are handled in the database or elsewhere.
Pricing Models
Amazon RDS uses an instance-based pricing model, charging for:
- Instance hours (based on size/type)
- Storage and IOPS
- Backups (free up to database size)
- Data transfer (free inbound, tiered outbound)
Costs apply even when underutilized. Savings options include Reserved Instances (up to 72% off) and Graviton-based instances (20–40% cheaper). Example: A db.m5.large in US East costs ~$0.171/hour on-demand or ~$0.048/hour with a 3-year RI.
Amazon DynamoDB offers:
- On-Demand: Pay-per-request (~$0.25 per million reads/writes)
- Provisioned: Pay per RCU/WCU-hour
Other charges: $0.25/GB-month for storage, $0.10/GB-month for backups, and data replication for Global Tables. As of late 2024, on-demand and replication prices were cut by up to 67%.
Free Tier includes 25 GB storage and 25 RCUs/WCUs or equivalent on-demand usage.
Summary: RDS is cost-efficient for steady workloads, especially with reservations. DynamoDB is better for unpredictable or bursty traffic, with lower costs for scaling to zero or massive throughput. Recent price cuts make DynamoDB more appealing across more scenarios.
Global Distribution and High Availability
Amazon RDS supports high availability through:
- Multi-AZ deployments (synchronous standby)
- Cross-region read replicas (asynchronous)
- Automated failover for failures
This setup offers low RPO/RTO within a region, but cross-region latency and the lack of active-active writes limit global application design. Writes are only allowed in the primary region.
Amazon DynamoDB’s Global Tables enable multi-active, multi-region replication with:
- Sub-second latency
- Automatic conflict resolution
- 99.999% SLA availability
Even without Global Tables, data is always replicated across three AZs by default. In 2025, improvements further reduced latency and enhanced conflict handling.
Summary: DynamoDB provides seamless global scalability and high availability with less complexity. RDS is reliable within a region but needs more effort for multi-region setups and doesn’t support active-active writes.
Security and Compliance
Amazon RDS offers strong security features:
- VPC isolation and IAM authentication
- TLS encryption in transit, KMS encryption at rest
- Automated patching and engine-specific controls (e.g., PostgreSQL Row-Level Security)
- Broad compliance (HIPAA, PCI, SOC, ISO)
Its managed model ensures timely security updates across supported engines.
Amazon DynamoDB provides:
- IAM-based access control and VPC endpoints
- KMS encryption at rest and optional client-side encryption
- Full compliance with major standards
- Reduced infrastructure attack surface due to its serverless nature
However, it lacks some advanced, engine-specific security features.
Summary: Both services meet enterprise-grade security needs. RDS offers deeper engine-level controls, while DynamoDB simplifies security through its serverless architecture.
Use Cases and Best Fits
Amazon RDS Best Fits:
- Traditional Enterprise Applications: Systems requiring complex transactions, joins, and established SQL interfaces
- Content Management Systems: WordPress, Drupal, and similar platforms built on relational databases
- ERP and CRM Systems: Enterprise software with complex data relationships
- Financial Applications: Banking, trading, and payment systems requiring ACID transactions
- Data Warehousing: When using PostgreSQL or MySQL for smaller analytical workloads
- Applications with Complex Reporting: Systems needing sophisticated queries and aggregations
RDS is ideal when your data naturally fits a relational model, your team has SQL expertise, or you're migrating existing applications from on-premises databases.
Amazon DynamoDB Best Fits:
- High-Scale Web Applications: Social networks, content platforms, and services with millions of users
- Mobile Backends: APIs serving mobile applications with unpredictable traffic patterns
- Gaming Applications: Leaderboards, user profiles, and game state storage
- IoT Applications: Ingesting and processing data from millions of devices
- Real-time Bidding: Ad tech platforms requiring consistent low-latency
- Session Storage: Web session management requiring high availability
- Microservices: Architectures needing independent, scalable data stores
DynamoDB excels when scale, performance, and operational simplicity are priorities over complex query capabilities.
Summary: The choice between RDS and DynamoDB should be driven primarily by your data model, scale requirements, and query patterns rather than specific industry verticals. Many sophisticated applications use both services for different components, leveraging the strengths of each.
2025 Updates and New Features
Amazon RDS
- Zero-ETL to Redshift: Automatically syncs RDS changes to Redshift for analytics — no ETL needed. Priced at ~$2.00/GB of CDC data.
- Graviton3 Adoption: Graviton3 instances are now default for new deployments, offering up to 40% better price - performance over x86.
- Enhanced Multi-AZ: Supports two readable standbys, doubling read capacity while maintaining HA.
- Performance Boosts: Read/write performance improvements across all engines, with major gains in PostgreSQL and MySQL.
- AI Monitoring: New anomaly detection and automated tuning recommendations for proactive issue resolution.
Amazon DynamoDB
- Lower On-Demand Pricing: Throughput and Global Tables prices dropped up to 67%, improving cost-efficiency for variable workloads.
- Global Tables Upgrades: Better conflict resolution, lower latency, and improved consistency across regions.
- PartiQL Enhancements: More SQL-like features added, while preserving DynamoDB's performance.
- Advanced TTL: Granular expiry policies and S3 archiving for expired items.
- Free Tier Expansion: Now includes 25 GB storage and 200 million monthly requests, ideal for small apps and testing.
Cost Comparison Examples
Scenario 1: Small Web Application
Requirements:
- 50 GB storage
- 20 TPS writes, 100 QPS reads
RDS (MySQL, US East):
- db.t3.medium: ~$49.64/month
- 50 GB SSD: ~$5.75/month
- Total: ~$55.39/month
DynamoDB (On-Demand):
- Writes: ~$12.96
- Reads: ~$64.80
- Storage: ~$12.50
- Total: ~$90.26/month
Summary: RDS is more cost-effective for small, predictable workloads.
Scenario 2: Variable-Traffic Application
Requirements:
- 100 GB storage
- 5 TPS avg, 500 TPS peak (writes)
- 25 TPS avg, 2,500 TPS peak (reads)
RDS (MySQL, US East):
- db.r5.xlarge (provisioned for peak): ~$329.96
- 100 GB SSD: ~$11.50
- Total: ~$341.46/month
DynamoDB (On-Demand):
- Writes: ~$19.28
- Reads: ~$96.39
- Storage: ~$25.00
- Total: ~$140.67/month
Summary: DynamoDB is more cost-efficient for spiky workloads, scaling on demand without idle overprovisioning.
Conclusion
In 2025, the choice between Amazon RDS and DynamoDB depends on your workload needs:
Choose RDS if you need:
- Complex relationships and full SQL support
- Predictable workloads
- Familiarity with relational databases
Choose DynamoDB if you need:
- High scalability and performance
- Unpredictable or bursty traffic
- Global, serverless, low-maintenance architecture
- Flexible schema with simple access patterns
Both services have improved, but their strengths remain clear: RDS for relational workloads, DynamoDB for scale and simplicity. Many teams use both to get the best of both worlds.
