PostgreSQL and MongoDB are two of the most popular databases for new projects. PostgreSQL is a relational database that also handles JSON documents very well. MongoDB is a document database that has added transactions and schema validation over the years. The gap is smaller than it used to be, so the choice comes down to your data, your queries and your team.

At a glance

PostgreSQL MongoDB
Data model Tables and rows, plus JSONB documents Collections of BSON documents
Schema Defined (with flexible JSONB columns) Flexible (optional validation)
Query language SQL MongoDB Query API, aggregation pipeline
Joins Native, efficient $lookup (limited; designs avoid it)
Transactions Full ACID, always Multi-document ACID (replica sets)
Horizontal scaling Replicas; sharding via extensions (Citus) Native sharding
Licence PostgreSQL licence (permissive) SSPL (Community Server)
Extensions PostGIS, pgvector, TimescaleDB… Atlas features (search, vector) in the cloud service

Data model

PostgreSQL shines when data is relational: customers, orders, products, invoices, all linked by foreign keys with guaranteed integrity. JSONB columns cover the flexible parts (product attributes, settings, payloads). See PostgreSQL JSONB.

MongoDB shines when data is naturally document-shaped, and read as a whole: a blog post with embedded comments, a product with nested variants, event logs, content with varying structure. Designing around documents (embedding related data) avoids joins entirely.

Queries

SQL is universal: every developer, BI tool and ORM speaks it, and complex reporting (joins, window functions, CTEs) is straightforward. MongoDB’s query API and aggregation pipeline are powerful, and feel natural from JavaScript, but cross-collection analytics are harder.

-- PostgreSQL: revenue per country
SELECT c.country, sum(o.total)
FROM orders o JOIN customers c ON c.id = o.customer_id
GROUP BY c.country ORDER BY 2 DESC;
// MongoDB: same, if country is embedded in orders
db.orders.aggregate([
  { $group: { _id: "$customer.country", revenue: { $sum: "$total" } } },
  { $sort: { revenue: -1 } }
]);

Consistency and transactions

PostgreSQL has had full ACID transactions, constraints and foreign keys from the start. MongoDB supports multi-document transactions on replica sets, but its design encourages single-document atomic updates, with integrity rules enforced by the application.

For money, inventory and anything where consistency across records matters, PostgreSQL is the safer default.

Scaling

  • Vertical scaling (a bigger server) takes both very far. Most applications never need more.
  • Read scaling: both support replicas. See PostgreSQL replication and MongoDB replica sets.
  • Write scaling and huge datasets: MongoDB has built-in sharding. PostgreSQL needs Citus or application-level sharding. See database sharding.

Performance

Neither is universally faster. MongoDB is quick for reading and writing whole documents by key, while PostgreSQL excels at complex queries, joins and aggregations. Data modeling and indexing matter far more than the engine. See MongoDB indexes and database indexing basics.

Operations on a VPS

PostgreSQL MongoDB
Install Distribution packages or PGDG repo MongoDB’s own repo
Memory Shared buffers + OS cache WiredTiger cache (about 50% of RAM by default)
Backups pg_dump, pgBackRest, PITR mongodump, filesystem snapshots, PITR via oplog
HA Patroni and similar Replica sets (built in)

MongoDB’s replica sets make automatic failover easy to set up. PostgreSQL HA needs extra tooling. See installing PostgreSQL and installing MongoDB on Ubuntu.

Licensing

PostgreSQL’s licence is permissive: use it anywhere, including in commercial services. MongoDB Community Server uses the SSPL, which is fine for running your own applications, but restricts offering MongoDB itself as a service. FerretDB provides a MongoDB-compatible API on top of PostgreSQL, for teams that want the MongoDB protocol with a PostgreSQL backend.

Which to choose

Situation Choose
Business data with relationships (orders, billing, users) PostgreSQL
Mostly relational, with some flexible fields PostgreSQL + JSONB
Content or catalogs with highly variable structure MongoDB (or PostgreSQL + JSONB)
Event or log ingestion at large scale with sharding MongoDB
Geospatial, vector search, time series in one place PostgreSQL + extensions
Team knows one well That one

If you’re unsure, PostgreSQL with JSONB covers the widest range of needs with one system. See MongoDB vs MySQL and MySQL vs PostgreSQL.

  • PostgreSQL
  • MongoDB
  • Comparison