Overview
Pick Postgres for a new product. JSONB plus GIN indexes covers the document workloads that drove MongoDB adoption in the 2010s, and you keep joins, transactions, and a real type system. Reach for MongoDB only when the schema is genuinely unknown at write time and the working set is large enough that a single Postgres primary cannot hold it. The 2026 default is Postgres for app data; MongoDB is a specialized tool. See postgres for the Postgres rule set and postgres-jsonb for the JSONB patterns.
When Postgres wins
Postgres is the default for app data, analytical queries, mixed structured and semi-structured workloads, and anything that benefits from joins.
- JSONB gives the same document ergonomics with GIN indexes for containment queries, partial indexes on JSON paths, and
jsonb_setfor surgical updates. - Joins: real foreign keys, real referential integrity, real
JOINperformance. MongoDB’s$lookupis fine for occasional reports but loses to a Postgres join on every dimension that matters. - Transactions are the default. MongoDB multi-document transactions exist but are slower and rarely used in idiomatic code.
- One database for relational and document data; no second store, no dual-write bug class.
- Strong type system, generated columns, partial indexes, expression indexes, materialized views, and a 30-year extension ecosystem (pgvector, PostGIS, partman).
- Operational cost: managed Postgres on Neon, Supabase, RDS, or Aurora is cheaper than MongoDB Atlas at every tier under 1 TB.
When MongoDB wins
MongoDB is the right pick in narrow cases. Each one has to be genuinely true, not just plausible.
- Schemaless writes from many producers where the shape changes weekly and you cannot stop to migrate. Event firehoses, mobile sync inboxes, analytics raw layer.
- Horizontal sharding past 5 TB of hot data where a single Postgres primary plus read replicas no longer fits. MongoDB’s sharded cluster is the canonical answer; Citus is the Postgres alternative.
- Existing MongoDB operator depth on the team: replica sets, Atlas tuning, Mongoose patterns. The muscle memory has real value.
- Embedded mobile or IoT with Atlas Device Sync as the conflict-resolution layer; this is a product, not a feature you can build on Postgres in a sprint.
- Aggregation pipeline workloads that already ship with Atlas Search and Atlas Vector Search and would cost engineering time to port.
Trade-offs at a glance
| Dimension | Postgres | MongoDB |
|---|---|---|
| Data model | Relational plus JSONB | BSON documents |
| Schema | Declared; migrations enforced | Implicit; per-document |
| Joins | Native, performant | $lookup; works, but slower |
| Transactions | Default; cheap | Multi-document; expensive |
| Consistency | Strong by default | Tunable per operation |
| Indexing | btree, GIN, GiST, BRIN, partial, expr | btree, text, geo, wildcard, hashed |
| Horizontal scale | Citus or app-level sharding | Native sharded cluster |
| Vector search | pgvector | Atlas Vector Search |
| Full-text search | tsvector plus GIN | Atlas Search (Lucene-backed) |
| Hosting cost (1TB) | Lower on Neon, Supabase, RDS | Higher on Atlas |
| Hiring | Universal SQL skill | Smaller pool; Mongoose churn |
| License | PostgreSQL (BSD-style) | SSPL |
Migration cost
MongoDB-to-Postgres is well-trodden and usually worth it once the team hits a join-heavy workload. Postgres-to-MongoDB is rare.
- MongoDB to Postgres: design the relational schema, then either store documents in JSONB to start and flatten over time, or flatten on day one. Use a streaming change feed to dual-write during the cutover. Plan one engineer-month per 100 collections plus a week of query-by-query verification.
- Postgres to MongoDB: only justified by sharding pain a Citus cluster cannot solve. Rewrite all joins as denormalized documents or
$lookupaggregations, and accept the consistency trade-offs. Plan two to four engineer-months for a non-trivial app. - Cheaper path: stay on Postgres, push hot blobs (sessions, analytics) to a JSONB column or a separate KV store like Redis or Cloudflare KV.
Recommendation
- New SaaS or B2B app: Postgres. No exceptions worth listing.
- App that already uses JSON-heavy payloads (CMS, form builder, settings): Postgres with JSONB; see postgres-jsonb.
- Mobile-first app with offline sync as a hard requirement: MongoDB Atlas with Device Sync, or a CRDT layer on top of Postgres.
- Existing MongoDB cluster, no scale pain: stay until a project genuinely benefits from joins, then port that workload.
- Multi-TB time-series or event stream: Postgres with TimescaleDB or partitioning, not MongoDB.
- Embedded single-writer use case: see sqlite-vs-postgres.