Multi-tenant data architectures in Postgres have evolved into three main approaches: shared schema, schema-per-user, and database-per-user. This post introduces a series focused on the database-per-user pattern, explaining its historical trade-offs (cost, operational complexity) and why modern cloud tools like Neon make it more viable. Key use cases include regulatory data residency requirements, geographic latency optimization, and solving the noisy neighbor problem. The post also covers two deployment models — isolated vs. shared application environments — comparing their trade-offs around data isolation, onboarding, versioning, and operational complexity. It introduces the concept of a control plane for automating database fleet management at scale.
