The historical answer flipped between 2018 and 2024. Postgres with JSONB now matches Mongo on document workloads while giving you SQL, joins, and transactions for free. Pick Mongo if you need its horizontal sharding out of the box; pick Postgres for almost everything else.
These were once different categories. Mongo was for documents, Postgres was for tables, and you picked your shape on day one. JSONB closed most of the gap. Modern Postgres handles document workloads at competitive performance and gives you the relational model for the parts of your schema that are actually relational — which is usually more of it than you expected.
Quick takes
If you're…
Your schema is mostly relational with some JSON fields→PostgresJSONB columns in a relational table. Best of both shapes.
You need horizontal sharding without third-party tools→MongoDBMongo's sharded clusters are first-class. Postgres needs Citus or CockroachDB.
You need ACID transactions across multiple documents→PostgresPostgres has had this since forever. Mongo added multi-doc transactions in 4.0 but they're slower.
You're prototyping with no schema yet→MongoDBNo migrations, no DDL. Just insert and iterate. Postgres ALTER TABLE is fast but it's still a step.
You need rich queries with joins and window functions→PostgresSQL was built for this. Mongo aggregation pipelines work but are harder to write.
You need geospatial queries on heterogeneous documents→MongoDBMongo's 2dsphere and PostGIS both work, but Mongo's default fits doc-shaped data.
You care about license clarity→PostgresPostgreSQL license is BSD-like. Mongo's SSPL since 2018 keeps cloud vendors out.
You're storing event logs or telemetry at scale→MongoDBMongo's write throughput on append-only collections is solid. Better than Postgres for this shape.
Document store. Each document is a BSON object that can be heterogeneous within a collection. No enforced schema by default; schema validation is opt-in via JSON Schema.
Postgres
Relational with strong JSON support. Tables, rows, columns, foreign keys — plus JSONB columns that store binary JSON with index support. Hybrid relational + document in one engine.
core
Query language
edge: Postgres
MongoDB
MongoDB Query Language (MQL) for find/update; aggregation pipeline for complex queries. JSON-shaped, less standardised, requires Mongo-specific learning.
Postgres
SQL — the most widely understood query language in software. JOINs, CTEs, window functions, recursive queries. Plus jsonb_path_query and operator-rich JSON access.
core
Transactions
edge: Postgres
MongoDB
Multi-document transactions since 4.0 (2018). Work, but slower than single-doc and add operational cost on sharded clusters.
Postgres
Full ACID transactions across any rows and any tables. Default behaviour. Serializable isolation via SSI is one setting away.
ops
Horizontal scaling
edge: MongoDB
MongoDB
Sharded clusters built in. Range or hashed shard keys. Balancer rebalances chunks across shards automatically. Real complexity, but native.
Postgres
Vertical scaling first; horizontal needs Citus extension, CockroachDB-on-PG-protocol, or application-level sharding. Less native, more options.
features
JSON workload performance
tie
MongoDB
Native BSON storage; document operations are first-class. Was the historical winner; the gap has closed.
Postgres
JSONB binary storage + GIN indexes on JSON properties. Independent benchmarks in 2024-25 show Postgres 17 at parity or faster on most JSON workloads.
ops
Replication
edge: MongoDB
MongoDB
Replica sets in-box. Automatic failover via the primary election. Read preference settings to route reads to secondaries.
Postgres
WAL streaming for hot standbys + logical replication since 10. Automatic failover requires Patroni or a managed offering.
ecosystem
Ecosystem
edge: Postgres
MongoDB
Atlas managed cloud is well-engineered. Compass GUI is good. ODMs (Mongoose, Beanie) are mature. Smaller library catalog than Postgres.
Postgres
The deepest ecosystem in databases. Every ORM, every BI tool, every analytics platform integrates. Extensions for vector, time-series, GIS, full-text.
ecosystem
License
edge: Postgres
MongoDB
SSPL v1 since October 2018. Not OSI-approved. Most cloud vendors can't offer hosted Mongo without a commercial agreement. Mongo Inc. sells Atlas.
Postgres
PostgreSQL License (BSD-like). OSI-approved. Every cloud offers managed Postgres, plus specialists like Neon, Supabase, Crunchy.
Benchmark
JSON workload, point lookup + range query
OnGres + EnterpriseDB joint study, 2024. 50M documents, mixed point lookups (60%) and range queries (40%) on indexed JSON properties. c5.4xlarge for both. Numbers represent the post-Postgres-17 reality where JSONB + GIN closes the historical Mongo gap.
Metric
MongoDB
Postgres
Better
Point lookup p50
Both fast. Postgres edges with B-tree index on JSONB path.
0.8 ms
0.6 ms
Postgres
Range query p50
Postgres benefits from query planner optimisation.
12 ms
9 ms
Postgres
Write throughput (single shard)
Mongo wins on raw inserts; Postgres trails by ~15%.