The table below evaluates the primary technical, architectural, and financial differences between the two managed database services:
| Feature | Amazon RDS | Amazon Aurora |
|---|---|---|
| Service Type | Managed relational database service | Cloud-native managed relational database |
| Database Engines | MySQL, PostgreSQL, MariaDB, Oracle, SQL Server | MySQL- and PostgreSQL-compatible |
| Architecture | Traditional database architecture | Distributed cloud-native architecture |
| Storage | Uses Amazon EBS volumes | Distributed, shared SSD-backed storage |
| Storage Replication | Multi-AZ uses synchronous replication to a standby instance | Six copies of data across three Availability Zones |
| Failover | Automatic failover to the standby instance | Automatic failover using Aurora Replicas and distributed storage |
| Storage Scaling | Requires manual scaling; limit varies by engine/configuration | Automatically scales storage up to 128 TB for supported configurations |
| Compute Scaling | Requires instance resizing or other scaling mechanisms | Aurora Serverless can automatically adjust compute capacity based on demand |
| Performance | Suitable for standard database workloads | Designed for higher throughput and lower latency |
| MySQL Performance | Standard MySQL performance | Up to 5× the throughput of standard MySQL in AWS comparisons |
| PostgreSQL Performance | Standard PostgreSQL performance | Up to 3× the throughput of standard PostgreSQL in AWS comparisons |
| Read Replicas | Up to 5 read replicas | Up to 15 Aurora Replicas |
| Replication Lag | Can occur under heavy workloads | Typically very low because replicas use shared storage |
| Optimized Reads | Not applicable | Aurora Optimized Reads can improve query performance for supported workloads |
Choosing the Optimal Database Solution
The choice between AWS RDS and Amazon Aurora is governed by budget parameters, performance requirements, and engine dependencies.
1. When to Choose AWS RDS
- Engine Requirements: The application specifically requires Oracle, Microsoft SQL Server, or MariaDB backends.
- Budget Constraints: Budget limitations favor predictable, lower baseline instance costs for standard, non-critical database applications.
- Moderate Traffic: The web application exhibits steady, predictable read/write patterns that do not require ultra-low latency or massive horizontal replica scaling.
- Engine-Level Control: The database administrator requires fine-grained control over specific database parameters and localized engine configuration options.
2. When to Choose Amazon Aurora
- Peak Scalability Requirements: The application expects volatile spikes in transaction traffic, making self-scaling storage up to 128 TB or Aurora Serverless compute scaling essential.
- High-Performance Needs: Workloads demand maximum data ingestion speeds, high throughput, and exceptionally low-latency reads across globally distributed users.
- Low Replication Lag: Real-time reporting tools and analytics applications require near-instant synchronization across up to 15 read replicas.
- Self-Healing Resiliency: The deployment demands automated, non-disruptive data block repairs and rapid, sub-30-second failovers to maintain high availability.