Architecting Multi-Tenant Databases on AWS: Silo vs. Pool Models for SaaS

As I continue developing a white-label web ERP with a Next.js and React frontend, the architectural focus has shifted from edge routing to backend data isolation. When designing the database layer for a multi-tenant SaaS, ensuring data privacy while maintaining cost-efficiency is a delicate balance.

I am currently evaluating two primary models for deploying the relational backend on AWS.

The first is the Pool Model (Shared Database, Shared Schema). All tenant data resides in a single Amazon RDS instance and is separated logically by a tenant_id column. This is highly cost-effective and simplifies schema migrations, but it introduces the risk of "noisy neighbors" and requires strict row-level security (RLS) to prevent data leaks.

The second is the Silo Model (Isolated Databases). This involves provisioning a separate RDS instance—or utilizing Amazon Aurora Serverless v2 for independent auto-scaling—for each tenant. This provides maximum security, compliance, and performance isolation, but drastically increases infrastructure costs and deployment complexity.

A middle ground is the Bridge Model (Shared Database, Separate Schemas), which often balances isolation and cost during the growth phase.

For architects running multi-tenant platforms on AWS: which database isolation strategy do you prefer for production environments? Have you found Aurora Serverless effective for managing the unpredictable workloads of individual tenants without breaking the budget?