Postgres vs MongoDB
The boring relational workhorse vs the document store you thought you wanted in 2014. Eleven years later, the data is in. Here’s the honest comparison and the clear pick.
Pick Postgres. Almost always. Postgres has eaten everything MongoDB once won on — JSON support, horizontal scale, replication, ease of use — while keeping the things relational databases have been good at for forty years: joins, transactions, foreign keys and a query planner that doesn’t lie to you. MongoDB is still a perfectly fine database for genuinely document-shaped data, but the set of products that are actually document-shaped is much smaller than the set of products that get talked into it.
Anything with users, orders, organisations, permissions, reports, search or money. In other words: ~95% of products being built right now.
Genuinely document-shaped data with no joins (CMS content trees, IoT telemetry, event logs), or you need horizontal write scale across many regions and don’t want to run Citus.
Side-by-side, line by line
Postgres here is mainline 16+. MongoDB is 7.x with the modern aggregation pipeline and multi-document transactions enabled.
| Feature | Postgres | MongoDB |
|---|---|---|
| Data model | EdgeRelational + JSONB. The best of both. | Document (BSON). Great for nested objects, painful for relationships. |
| Joins | EdgeFirst-class. Hash joins, merge joins, nested loops — query planner picks. | $lookup exists. It works. Performance is "fine" up to a point. |
| Transactions | EdgeACID, serialisable isolation, default and well understood. | Multi-doc transactions added in 4.0. Functional, but discouraged for hot paths. |
| Schema | EdgeStrict. Migrations are explicit. JSONB columns for flex. | Flexible. Schema validation optional and often skipped. |
| Indexing | EdgeB-tree, GIN, GiST, BRIN, HNSW (pgvector), partial, expression. | B-tree, hash, geo, text, wildcard, vector. Solid set. |
| Full-text search | Built-in tsvector + GIN. Good enough to skip Elastic for many apps. | EdgeAtlas Search (managed) is genuinely excellent. Self-host: $text only. |
| Vector / AI search | Edgepgvector with HNSW. Excellent. Rivals dedicated vector DBs. | Atlas Vector Search. Equivalent in Atlas only. |
| Horizontal write scale | Single primary by default. Citus / Neon / Aurora for sharding. | EdgeSharding built in. Genuinely scales horizontally without surgery. |
| Replication | Streaming replication. Logical replication. Mature. | EdgeReplica sets. Automatic failover. Excellent. |
| Aggregations / analytics | EdgeSQL + window functions + CTEs + materialised views. Unmatched. | Aggregation pipeline. Powerful but a different language for every query. |
| Tooling & ORMs | EdgePrisma, Drizzle, SQLAlchemy, ActiveRecord — every ecosystem has one. | Mongoose, Prisma. Smaller pool of mature options. |
| Operational sanity | EdgeBoring. Predictable. Well-understood failure modes. | Self-managed clusters need real expertise. Atlas hides most of it. |
Postgres
- Joins, transactions, foreign keys — all the things that protect data integrity.
- JSONB columns mean you get document flexibility when you actually need it.
- pgvector turns it into a competent vector database for free.
- Full-text search good enough to skip a separate Elastic cluster for most apps.
- Tooling, ORMs and hires are everywhere.
- Boring. Forty years of well-understood behaviour.
- Single-primary writes. You scale up before you scale out.
- Sharding requires extensions (Citus) or third parties (Neon, AlloyDB).
- Migrations need discipline. Add a column wrong and prod will limp.
- Connection pooling needs PgBouncer or equivalent in serverless.
MongoDB
- Sharding built in — genuinely horizontal write scale.
- Aggregation pipeline is powerful for analytical queries on documents.
- Atlas is a polished managed service with great vector search.
- Schema flexibility lets you ship features fast in early-stage products.
- Geospatial indexing and queries are first-class.
- Relational data feels wrong every single time you try to model it.
- Multi-doc transactions exist but slow you down on hot paths.
- Schemaless tends to mean "schema in your application code" — and getting it wrong.
- Aggregation pipeline syntax is a language you have to learn separately.
- Atlas pricing climbs steeply once you need vector search at volume.
Postgres
- You have users, orgs, roles or anything that joins.
- You're building SaaS, B2B, fintech, ecommerce — any product with money or relationships.
- You need full-text search but don't want to run a separate Elastic cluster.
- You're building AI features and want vector search alongside your data.
- You want predictable failure modes and tooling that's been stable for decades.
- You haven't got a watertight reason to pick anything else.
MongoDB
- Your data is genuinely tree-shaped (CMS content, IoT telemetry, event logs).
- You need to scale writes horizontally across regions from day one.
- You're ingesting high-volume time-series or log data.
- You're happy on Atlas and want their managed vector search and Atlas Search.
- Your team has deep MongoDB expertise and Postgres would slow them down.
- You've modelled the data and joins genuinely don't appear.
What I do on real projects
I’ve picked Postgres for every product I’ve built since 2019. The only MongoDB codebase I currently maintain is a legacy one I inherited, and migrating it to Postgres is the single biggest engineering improvement on the roadmap. Schema-on-read looked clever in the early days; eleven years later, every "schemaless" codebase I’ve seen has just had its schema in slightly worse handwriting, scattered across its application code with no one source of truth.
JSONB closes the door on most of the historical reasons to pick MongoDB. If you want to store unstructured event payloads, dynamic form responses, or "user_settings", JSONB columns in Postgres handle it cleanly, you can index inside the JSON, and your relational neighbours still get foreign keys. You get document flexibility when you need it without giving up the rest of the database.
MongoDB still has a real argument when the data is genuinely document-shaped and the access pattern is "fetch one tree". A modern CMS, a configuration store, an IoT telemetry ingestion pipeline — all reasonable. Outside those niches, picking MongoDB in 2026 is mostly a sign that someone on the team read the wrong blog post in 2014.
The honest conclusion
Postgres has won, and the gap is widening. JSONB, pgvector, logical replication, Citus and an extension ecosystem mean it does almost everything MongoDB used to be picked for, while keeping the relational fundamentals. Pick Postgres unless you can write down — in one sentence — the specific document-shaped problem that justifies the trade-off.
Still not sure which to pick?
I make these calls every week on real projects. If you want a straight answer for your stack — not a list of trade-offs — get in touch.