PostgreSQL

Most business systems have data with real structure and real relationships — customers who place orders, policies that have claims, accounts that have transactions — and those relationships carry rules that matter: a payment shouldn't exist without an order, a balance shouldn't go negative because two updates ran at once. PostgreSQL's relational model and transactional guarantees enforce that structure at the database layer, rather than leaving it entirely to application code to get right every time.

It's also more than a plain relational database in practice. Native support for JSON columns, full-text search, geospatial queries (via PostGIS), and vector similarity search means a single PostgreSQL instance can often cover data needs that would otherwise require running two or three specialised databases side by side. For teams without the operational capacity to run and maintain multiple data stores, that consolidation is a genuine practical advantage, not just a convenience.

The trade-off is schema flexibility. A relational schema has to be designed and migrated deliberately, which is more overhead than a document store's schema-less writes when data is genuinely unstructured or changes shape frequently and unpredictably. For most business data — which has a reasonably stable shape even as fields get added over time — that overhead buys correctness and query power that a document store doesn't provide in the same way.

We also don't treat it as the automatic answer to every storage problem. For workloads that are genuinely document-shaped, have no meaningful relationships to enforce, or need to scale writes horizontally in ways that outgrow what a single relational instance handles comfortably, another data store is frequently a better architectural fit — and we scope the decision to the workload rather than to habit.