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Amazon Aurora

Amazon Aurora is a MySQL- and PostgreSQL-compatible relational database whose distributed cluster storage, replicas, and managed features target cloud-native availability and performance.

Explore pricing models, common use cases, infrastructure support, and the AWS services that commonly work with Amazon Aurora.

Amazon Aurora pricing and cost programs

Pricing model: Managed relational database usage

On-Demand
Available
Reserved Instances or reserved capacity
Available
Savings Plans
Available
Spot
Not applicable

Billing dimensions: Instance or ACU time · Storage and I/O · Backups · Data transfer

Programs and modes: On-Demand Instances · Reserved Instances · Database Savings Plans · Aurora Serverless

Provisioned and Serverless configurations use different capacity dimensions and commitment coverage.

Free Tier: Service-specific — verify current offers

Pricing reviewed 2026-07-25. Reviewed against the linked official AWS pricing page. Recheck regional rates and program terms before purchase.

Official AWS pricing

Official AWS sources reviewed 2026-07-21.

Why implement Amazon Aurora?

  • Separates compute from a distributed storage volume that replicates data across three Availability Zones and grows automatically within service limits.
  • Supports multiple readers, fast managed failover, Serverless v2, Global Database, backtracking for supported configurations, and MySQL or PostgreSQL compatibility.
  • Automates database-host patching, storage replication, continuous backups, point-in-time recovery, monitoring, and replica orchestration.

How to implement Amazon Aurora

  1. Assess application, extension, SQL, version, parameter, migration, and failover compatibility, then select provisioned or Serverless v2 compute and any global topology.
  2. Create the encrypted cluster in private subnets with security and parameter groups, a writer plus production readers in appropriate Availability Zones, backups, deletion protection, and managed secrets.
  3. Use cluster, reader, or custom endpoints intentionally, configure pooling and DNS-aware reconnects, migrate and validate data, then test reader scaling, maintenance, failover, restore, and regional recovery.

Amazon Aurora best practices

  • Run at least one reader in another Availability Zone for production failover, choose promotion tiers deliberately, and test that applications reconnect and retry safe transactions.
  • Use the cluster endpoint for writes and reader endpoint for read scaling, keep transactions short, tune queries and indexes, and use RDS Proxy or pooling for connection-heavy workloads.
  • Encrypt data and connections, use least privilege and managed secrets, retain and test backups, keep versions current, and monitor database load, memory, storage, lag, latency, and failover events.

Amazon Aurora use cases and server impact

  • Highly available MySQL- or PostgreSQL-compatible applications
  • Elastic relational workloads with Serverless v2
  • Cross-Region relational systems with Aurora Global Database

Replaces relational clusters, shared storage, replicas, backups, and much failover automation, while application compatibility, SQL tuning, connection behavior, and recovery design still matter.

Official implementation resources

Commonly paired AWS services