For SaaS companies building multi-tenant applications, choosing the right database infrastructure is a pivotal decision — one that impacts scalability, performance, cost, and maintainability. Two of the most popular managed databases on AWS for PostgreSQL workloads are Amazon RDS for PostgreSQL and Amazon Aurora PostgreSQL. While both offer fully managed experiences and PostgreSQL compatibility, their differences can significantly affect your architecture and operational efficiencies.
This guide aims to break down the distinctions between Postgres RDS and Aurora from the lens of a multi-tenant SaaS provider, helping you make an informed decision based on your company’s unique needs.
Understanding the Basics
- Amazon RDS for PostgreSQL: A fully managed PostgreSQL service that runs the native PostgreSQL engine, offering backups, patching, automatic failover, and monitoring.
- Amazon Aurora PostgreSQL: A proprietary AWS database compatible with PostgreSQL, boasting high performance and scalability, built on a distributed, fault-tolerant storage system.
Multi-Tenancy Models and What They Demand
When architecting SaaS products, you’ll usually adopt one or more of the following multi-tenancy database models:
- Shared schema: All tenants share the same tables and schema, differentiated via a tenant ID.
- Isolated schema: Each tenant has its own schema within a shared database.
- Isolated database: Each tenant has its own dedicated database instance.
Each model has its own implications for scalability, security, manageability, and resource allocation. Let’s explore how RDS and Aurora handle these concerns.
1. Performance and Scalability
RDS: While RDS offers solid performance for many use cases, it generally scales vertically—meaning you upgrade the instance size to accommodate more load. Read replicas are available, but replication has more lag due to traditional PostgreSQL replication mechanisms.
Aurora: Aurora’s architecture decouples compute and storage. It offers up to 15 read replicas with sub-10ms replication lag, autoscaling storage (up to 128 TB), and faster failovers. Aurora can handle significantly higher loads with less administrative overhead.
Verdict: If you expect unpredictable load spikes or rapid growth across tenants, Aurora’s elastic scalability is a better fit.
2. High Availability and Disaster Recovery
RDS: Supports Multi-AZ deployments for high availability by replicating synchronously to a standby in another availability zone. RTO and RPO are typically within minutes. Backups and snapshots are automated, but point-in-time recovery may cause downtime during restoration.
Aurora: Offers built-in fault-tolerance with six-way replication across three AZs. Aurora also provides continuous backup to S3 and supports backtracking, which allows rolling back without restoring from backup.
Verdict: For a business-critical SaaS app with downtime-sensitive tenants, Aurora’s enhanced durability and faster failover make it more robust.
3. Cost Considerations
RDS: Generally more cost-effective for smaller or predictable workloads. Instance pricing follows standard EC2-based models, and you pay separately for storage.
Aurora: Aurora offers better performance, but at a premium. However, its Aurora Serverless v2 option allows for more granular cost control by scaling compute based on load. This can be a game-changer for SaaS apps with sporadic workloads.
Verdict: RDS might be the economical choice for low to moderate workloads, but if your app benefits significantly from elastic compute and storage, Aurora may ultimately reduce TCO.
4. Architecture Flexibility for Tenants
In a multi-tenant world, your architecture must easily accommodate new tenants and higher numbers of them with minimal rework.
RDS: While you can use a shared or isolated schema model effectively within a single RDS instance, provisioning separate database instances per tenant (for high isolation) becomes cumbersome. RDS is slower in provisioning and scaling databases dynamically.
Aurora: Aurora Serverless and Aurora Global Databases enable more flexibility. Aurora Serverless allows on-demand tenant onboarding while managing compute utilization, and Aurora Global Databases offer cross-region replication — a must for SaaS platforms with a global reach.
Verdict: Aurora presents advantages for dynamic or global SaaS environments needing flexible tenant isolation strategies.
5. Maintenance and Monitoring
RDS: Provides automated patching, upgrades, and CloudWatch integration. It also integrates well with AWS tools like Amazon CloudTrail and Performance Insights.
Aurora: Offers similar capabilities, but adds deep performance monitoring via Advanced Monitoring and Query Plan Insights. Aurora’s self-healing storage reduces the operational burden of disk replacements or corruption issues.
Verdict: Both are solid for managed operations, but Aurora provides additional tooling for fine-grained performance insights.
6. Tenant Data Isolation and Security
RDS: Security-wise, RDS supports encryption at rest and in transit, IAM authentication, VPC isolation, and parameter group tuning. For data isolation, shared schema models require more application logic for access control.
Aurora: Offers all the above and more. Aurora supports role-based access controls, multiple schema and database options under a single cluster, and enhanced auditing with integrations to CloudTrail.
Verdict: While both platforms are secure, Aurora’s support for automation and fine-grained control offers more capability for secure, multi-tenant SaaS deployments.
7. Limitations and Gotchas
RDS: Even though it’s “managed,” it still comes with limitations around slow failovers, limited read replicas, and tighter caps on concurrent connections. RDS is also more sensitive to spikes in workload, which could affect all tenants in a shared schema.
Aurora: Some PostgreSQL extensions may not be supported. Also, despite advertised compatibility, you’re still running an AWS-controlled version of PostgreSQL. Migrating off Aurora later could introduce some friction.
Verdict: Know your feature dependencies. Aurora is great for scale, but scrutinize any reliance on specific PostgreSQL behaviors or extensions.
Recommendations: When to Choose What?
Here’s a simplified comparison to help guide selection:
| Scenario | Recommended Option |
|---|---|
| Startups with predictable workloads | RDS for PostgreSQL |
| Apps with global tenants and scaling needs | Aurora PostgreSQL |
| Highly isolated tenant architecture | Aurora (with separate clusters or Serverless v2) |
| Cost-sensitive MVPs or beta environments | RDS for PostgreSQL |
| High performance analytics and read-heavy loads | Aurora PostgreSQL |
Conclusion
Choosing between RDS for PostgreSQL and Aurora for a multi-tenant SaaS product is not a one-size-fits-all decision. RDS is simpler, cheaper, and works well for smaller environments or where full PostgreSQL compatibility is required. On the other hand, Aurora offers a robust suite of features tailored for performance, scalability, and high availability — qualities that align well with growing SaaS platforms and diverse tenant needs.
Ultimately, the right choice depends on your tenant architecture, growth curve, and operational complexity tolerance. By evaluating based on factors like scalability, tenant isolation, and cost control, you can align your database strategy with your long-term SaaS vision.