MongoDB vs Postgres for MVPs: Which to Pick

The argument that no longer holds

The classic case for MongoDB was schema flexibility — you don't know your data model yet, so don't commit to one. That was persuasive when the alternative meant a migration for every column.

Postgres has jsonb now. You can store a schemaless document in a column, index inside it with GIN, and query its fields — while keeping real columns, foreign keys, and transactions for the parts of your model that are stable. You no longer have to choose one or the other for the whole database.

That's the crux: most products have a stable core (users, organizations, subscriptions) and a fluid edge (event payloads, settings blobs, third-party responses). Postgres handles both. MongoDB asks you to treat the stable core as though it were fluid.

What you give up with MongoDB

Joins. MongoDB has $lookup, but it's not a substitute for a query planner working across indexed relations. The usual answer is denormalization, which means the same fact lives in several documents and you own keeping them consistent.

Referential integrity. No foreign keys. Nothing stops an order pointing at a customer that no longer exists except your application code, and your application code has bugs.

Transactions across documents. Supported since 4.0, but with performance caveats and a design that discourages relying on them. In Postgres, multi-row transactions are the normal case.

When MongoDB is the right call

Genuinely document-shaped data with no cross-entity queries — CMS content, product catalogs with wildly varying attributes, event logs.

Very high write throughput on independent documents, where horizontal sharding is the point and you never need to join.

Your team knows it deeply and the product is simple enough that the model won't grow relational. Team familiarity is a real engineering input, not a cop-out.

The failure mode we get called in for

The pattern is consistent: a team picks MongoDB for flexibility, the product succeeds, and the data turns out to be relational after all. Now there are four collections that must stay in sync, a nightly reconciliation job, and a reporting requirement that can't be answered without pulling everything into application memory.

The fix is a migration to Postgres, which is expensive precisely because the product is now working and can't stop.

Our default

Postgres, with jsonb for the fluid parts. It's the choice that stays correct as the product grows, and it's the one where being wrong is cheapest to reverse.