AWS RDS vs Aurora: Which Managed Database Is Right for You?

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**Quick Answer:** Aurora wins for HA-critical, read-heavy workloads requiring fast failover. RDS wins for simpler, low-I/O workloads where Aurora's I/O pricing adds unnecessary cost.

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## Freshness Check (June 2026)

Use this page as a decision framework, then validate the latest pricing, quotas, and region-level feature parity in AWS documentation before final sign-off.

This page was refreshed against official AWS announcements and service documentation published in the last 12 months. Confirm region support, quotas, and pricing before final architecture sign-off.

- [AWS What's New](https://aws.amazon.com/about-aws/whats-new/)

Amazon RDS and Aurora are both managed relational database services, but they are architecturally distinct products with different cost models, availability characteristics, and performance profiles. Aurora is often described as "RDS but faster" — that framing misses the nuance that matters for production architecture decisions.

This comparison focuses on the technical and economic differences that affect real workloads, including Aurora's I/O pricing model that surprises teams when their first monthly bill arrives.

## Architecture Differences

The most important difference between RDS and Aurora is the storage layer.

Standard RDS uses Amazon EBS volumes attached to the database instance. Replication (for Multi-AZ deployments) is handled at the storage level using synchronous EBS mirroring to a standby instance in a second Availability Zone. The storage grows in fixed increments and is bound to the instance's EBS configuration.

Aurora uses a purpose-built distributed storage system that is decoupled from the compute layer. Aurora storage automatically replicates data 6 ways across 3 Availability Zones — this happens at the storage level, not the instance level. Aurora storage auto-scales in 10 GB increments up to 128 TiB. The compute (instances) and storage are independently scalable.

| Characteristic    | RDS (Provisioned)                              | Aurora                                     |
| ----------------- | ---------------------------------------------- | ------------------------------------------ |
| Storage backend   | EBS volumes                                    | Distributed Aurora storage                 |
| Replication       | Multi-AZ EBS mirror (optional)                 | Always 6-way, 3-AZ (built-in)              |
| Storage scaling   | Manual or auto in 10 GB increments             | Auto-scales, up to 128 TiB                 |
| Read replicas     | Up to 5 (separate storage per replica)         | Up to 15 (shared storage volume)           |
| Failover time     | ~30-120 seconds (Multi-AZ)                     | ~30 seconds (Aurora Replica)               |
| Storage cost      | Based on EBS gp3/io1 pricing                   | Per-GB-month + I/O charges                 |
| I/O charges       | No per-I/O charge                              | Yes (Aurora Standard) / No (I/O-Optimized) |
| Engines supported | MySQL, PostgreSQL, MariaDB, Oracle, SQL Server | MySQL-compatible, PostgreSQL-compatible    |

## Performance Comparison

Aurora's distributed storage architecture provides concrete advantages for specific workload patterns:

- **Read scaling:** Aurora Replicas share the same underlying storage as the primary. Replication lag is typically under 100ms (often milliseconds). RDS read replicas replicate via binary log replication, which introduces higher lag under write load.
- **Failover speed:** Aurora fails over to a read replica in ~30 seconds because the replica is already attached to the same storage. RDS Multi-AZ failover involves remounting EBS to the standby, typically taking 30–120 seconds.
- **Crash recovery:** Aurora's distributed storage architecture means the database does not need to replay a redo log after a crash. Recovery is near-instant.

For write-heavy single-instance workloads, RDS and Aurora perform similarly. The Aurora storage layer's 6-way write amplification means more I/O operations per write — which is why the I/O cost model matters.

## Cost Breakdown

This is where Aurora's real cost differs from marketing comparisons.

**Aurora Standard (per-I/O pricing):**

- Instance: ~20% premium over equivalent RDS instance
- Storage: $0.10/GB-month
- I/O requests: $0.20 per million requests
- Backup storage: $0.021/GB-month (beyond free tier)

**Aurora I/O-Optimized (no per-I/O charge):**

- Instance: ~30-40% premium over equivalent RDS instance
- Storage: $0.225/GB-month
- I/O requests: Included (no charge)
- Backup storage: $0.021/GB-month

**RDS Provisioned (PostgreSQL/MySQL, gp3 storage):**

- Instance: Baseline pricing
- Storage: $0.115/GB-month (gp3)
- I/O requests: No per-I/O charge
- Backup storage: $0.095/GB-month (beyond free tier)

**Estimated monthly cost — db.r6g.large, 100 GB storage, us-east-1:**

| Configuration        | Instance | Storage | I/O (500M/mo) | Total |
| -------------------- | -------- | ------- | ------------- | ----- |
| RDS Multi-AZ         | ~$185    | $23     | $0            | ~$208 |
| Aurora Standard      | ~$222    | $10     | $100          | ~$332 |
| Aurora I/O-Optimized | ~$296    | $22.50  | $0            | ~$319 |

At 500 million I/O operations per month, Aurora Standard is more expensive than both RDS and Aurora I/O-Optimized. At 1 billion+ I/O operations per month, Aurora I/O-Optimized starts to deliver savings versus Aurora Standard, and its higher instance cost may still be less than RDS Multi-AZ for highly available deployments.

## High-Availability Features

| Feature                    | RDS Multi-AZ              | Aurora                            |
| -------------------------- | ------------------------- | --------------------------------- |
| Automatic failover         | Yes (~30-120s)            | Yes (~30s with replica)           |
| Read replicas              | Up to 5, separate storage | Up to 15, shared storage          |
| Cross-region replicas      | Read replicas             | Aurora Global Database            |
| RPO                        | Near-zero (synchronous)   | Near-zero (6-way synchronous)     |
| RTO                        | ~30-120 seconds           | ~30 seconds                       |
| Multi-region active-active | No native support         | Aurora Global Database (< 1s RPO) |
| Serverless option          | No                        | Aurora Serverless v2              |

Aurora Global Database is a differentiated feature with no direct RDS equivalent — it replicates data to up to 5 secondary regions with typical replication lag under 1 second, enabling near-zero RPO cross-region disaster recovery and read traffic distribution to geographically distributed users.

## When RDS Is the Right Choice

RDS is the appropriate choice when the workload does not justify Aurora's cost or complexity premium.

**Choose RDS when:**

- Your workload is development, testing, or early-stage production (Aurora's minimum cost is higher)
- You need Oracle or SQL Server (Aurora does not support these engines)
- Traffic and I/O volumes are low — Aurora Standard's per-I/O charges add cost without proportional benefit
- You want the simplest pricing model without I/O charges
- A single-AZ deployment is acceptable and Multi-AZ cost is not justified
- You need a specific engine version not yet available in Aurora (Aurora PostgreSQL versions lag slightly)

## When Aurora Is the Right Choice

**Choose Aurora when:**

- High availability with fast failover is a requirement (the 30-second vs 2-minute failover difference matters)
- You need more than 5 read replicas for read scaling
- Cross-region replication with sub-second RPO is required (Global Database)
- I/O workloads are high enough to justify the storage architecture benefits
- You want storage to auto-scale without manual provisioning
- Aurora Serverless v2 is attractive for variable workload patterns

## Moving from RDS PostgreSQL to Aurora PostgreSQL

Aurora MySQL-compatible and Aurora PostgreSQL-compatible clusters are wire-compatible drop-ins for applications, **but Postgres migration tooling has to respect WAL / LSN continuity**. MySQL-heavy teams still lean on snapshot-restore promotion or [**AWS Database Migration Service (DMS)**](https://aws.amazon.com/dms/), while PostgreSQL teams adding **logical replication** should read this section before production cutover.

### Pick the migration shape that matches your outage budget

Each path solves a different bottleneck: brute-force downtime, replication lag, topology testing, schema filtering, or **Major version uplift** concurrent with migration.

| Path                                                         | Reach for it when…                                                                                                                                           | Typical trade-offs                                                                                                                              |
| ------------------------------------------------------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------ | ----------------------------------------------------------------------------------------------------------------------------------------------- |
| **Snapshot restore + cutover**                               | You accept a maintenance window or can tolerate longer read-only phases                                                                                      | Simplest Ops story; validates Aurora **I/O-Optimized vs Standard** upfront; downtime concentrated in cutover rehearsal                          |
| **Aurora read replica from RDS**                             | You want AWS-managed physical replication with **minimal application change** during steady state                                                            | **Single reader topology** mirrors default replica shape — fine for parity testing on one reader, weaker for validating **multi reader** fleets |
| **DMS CDC**                                                  | Mix of engines/schemas, selective tables, heterogeneous sources, ops already standardized on **DMS**                                                         | Extra pipeline cost and operational surface; excels at bounded datasets and monitoring via DMS metrics                                          |
| **Seed + logical replication (native publisher/subscriber)** | Must mirror **multiple readers**, rehearse failover under Aurora layout, optionally **bundle a major Postgres upgrade**, or carve **partial schemas/tables** | Requires correct **seed LSN** bookkeeping — where RDS→Aurora diverges architecturally                                                           |

For **PostgreSQL-specific** prerequisites, conflict handling, and the exact seeded-handshake sequence, defer to **[Migrating RDS for PostgreSQL to Aurora PostgreSQL using seeded logical replication — AWS Database Blog](https://aws.amazon.com/blogs/database/migrating-amazon-rds-for-postgresql-to-amazon-aurora-using-seeded-logical-replication/)**.

### Why “restore snapshot → start logical replication” surprises teams

PostgreSQL logical replication resumes from positions in the **write-ahead log (WAL)** tracked as **log sequence numbers (LSNs)**. RDS for PostgreSQL rotates WAL locally with textbook PostgreSQL semantics. Aurora persists transaction logs through its **six-way distributed storage**. After you restore an RDS snapshot into Aurora, the target cluster starts a fresh LSN lineage that **does not line-number the same WAL stream** your source still speaks.

Mis-set seed LSNs produce **duplicate key explosions** when the subscriber replays inserts that already landed in your snapshot — or silent inconsistency without primary keys. Run **ahead**, and subsets of rows simply never arrive. That is why AWS documents **seeded logical replication**, not assumption-driven slot creation alone.

Treat the official post above as canonical for implementation; use this comparison to steer **commercial** sizing (instances, Aurora I/O-Optimized breakout, failover rehearsal) alongside that runbook.

### Before you cut over

Work through Aurora **parameter group diffs**, **connection limits** versus RDS Proxy sizing, Aurora **billing guardrails**, and scripted **fail-forward / fail-back** rehearsals. Engagement teams routinely pair these checks with Observability dashboards on replication lag slots and **Promotion guardrails**.

## Related Comparisons

Explore other technical comparisons:

- [DynamoDB vs RDS](/compare/dynamodb-vs-rds/)
- [Aurora Serverless vs Provisioned](/compare/aws-aurora-serverless-vs-aurora-provisioned/)

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