MongoDB vs PostgreSQL

Quick verdict

MongoDB is a document database. Data is stored as BSON documents (a binary form of JSON) in collections, documents in the same collection do not need the same fields, and queries are written with the MongoDB Query API (for example find() and aggregation pipelines) rather than SQL. MongoDB Community Server is source-available under the Server Side Public License, and MongoDB, Inc. also sells MongoDB Enterprise Advanced and runs the MongoDB Atlas cloud service. The current release documented in the MongoDB manual is 9.0.

PostgreSQL is an open source object-relational database developed by the PostgreSQL Global Development Group. Data lives in tables with declared columns and constraints, queried with SQL. Alongside its relational core it has two JSON types, json and jsonb, and the documentation recommends jsonb for most applications because it is stored in a decomposed binary form and can be indexed. The current stable release listed on postgresql.org is 18.6, with PostgreSQL 19 in beta.

This page is about these two specific products. For the broader question of relational versus non-relational databases, see SQL vs NoSQL and the article relational database vs NoSQL.

Side by side

Key differences

Data model: documents versus tables with jsonb

In MongoDB the unit of storage is the document, and MongoDB's own transactions guidance says that for many scenarios modelling data appropriately, with embedded documents and arrays, minimises the need for multi-document transactions. You design around the queries your application makes, often storing an order and its lines in one document.

In PostgreSQL you normalise into tables and join them, but you can add a jsonb column for the parts that vary. The documentation notes the trade-offs between the two JSON types: jsonb does not preserve whitespace, key order or duplicate keys, while json keeps an exact copy of the input text but cannot be indexed in the same way.

The same query written both ways, finding shipped orders for UK customers:

// MongoDB (mongosh)
db.orders.find(
  { status: "shipped", "customer.country": "UK" },
  { _id: 0, order_no: 1, total: 1 }
)

-- PostgreSQL (doc is a jsonb column)
SELECT doc->>'order_no' AS order_no,
       doc->>'total'    AS total
FROM   orders
WHERE  doc @> '{"status": "shipped", "customer": {"country": "UK"}}';

Schema validation and integrity

MongoDB documents that schema validation is optional by default. You can add $jsonSchema rules to a collection; with the default validationAction of error, invalid inserts and updates are rejected, or you can choose warn to log them instead. validationLevel controls how rules apply to existing documents. There are no foreign keys between collections, so referential integrity is enforced by the application.

PostgreSQL enforces types, NOT NULL, CHECK, unique and foreign key constraints at the table level. Data inside a jsonb column is not checked against a schema unless you add constraints yourself, so a hybrid design gives you strict columns and flexible JSON side by side.

Joins: $lookup versus SQL joins

MongoDB's $lookup stage performs a left outer join to another collection in the same database. The documentation states that the joined collection can be sharded from MongoDB 5.1, and that $lookup can target a sharded collection inside a transaction from 8.0. It also lists index limitations, for example field-to-field comparisons in $expr cannot use indexes.

// MongoDB: orders with their customer
db.orders.aggregate([
  { $lookup: { from: "customers", localField: "customer_id",
               foreignField: "_id", as: "customer" } }
])

-- PostgreSQL
SELECT o.order_no, c.name
FROM   orders o
LEFT JOIN customers c ON c.id = o.customer_id;

If your reporting depends on many joins across entities, PostgreSQL's SQL is the more natural fit. See the SQL joins guide for the join types PostgreSQL supports.

Transactions and concurrency

MongoDB supports multi-document ACID transactions on replica sets from feature compatibility version 4.0 and on sharded clusters from 4.2. The documentation cautions that a distributed transaction usually costs more than single-document writes and should not replace good schema design, and lists restrictions such as no writes to capped collections and no new collections in cross-shard write transactions. Single-document writes are atomic without a transaction.

PostgreSQL uses multiversion concurrency control (MVCC). Its documentation states that reading never blocks writing and writing never blocks reading, and it offers serializable isolation through Serializable Snapshot Isolation. Every statement runs in a transaction, and multi-table transactions need no special deployment type.

Indexing

MongoDB documents single field, compound, multikey (arrays), geospatial, text, hashed, wildcard and clustered indexes, and a unique index on _id is created automatically. Atlas adds Atlas Search as a separate search capability.

PostgreSQL indexes ordinary columns with B-tree and other index types, and indexes jsonb with GIN. The documentation describes two GIN operator classes: the default jsonb_ops, which supports key-exists operators, and jsonb_path_ops, which is usually much smaller but supports only containment and JSONPath operators.

-- PostgreSQL: GIN index for containment queries on jsonb
CREATE INDEX orders_doc_gin ON orders USING GIN (doc jsonb_path_ops);

Replication and horizontal scaling

MongoDB replica sets have one primary and asynchronous secondaries; if the primary is unreachable for longer than electionTimeoutMillis (10 seconds by default) an eligible secondary calls an election. Sharding spreads collections across shards (each a replica set) using a shard key, with mongos routers and config servers; ranged and hashed sharding are both documented, and collections can be resharded from 5.0. MongoDB also notes that sharding adds infrastructure complexity and that queries without the shard key are broadcast to all shards.

PostgreSQL has built-in streaming replication, but its documentation states that it does not provide the software to detect a primary failure and promote a standby, so automatic failover relies on external tools or a managed service. Declarative partitioning (range, list and hash) splits a table into pieces on the same server. To distribute tables across nodes you add an extension such as Citus, which is AGPL-3.0 licensed and describes itself as turning Postgres into a distributed database.

Pricing and licensing

Licences. MongoDB Community Server releases after 16 October 2018 are licensed under the Server Side Public License (SSPL) v1.0; earlier versions were AGPL v3.0. MongoDB's supported drivers are Apache License 2.0. If the SSPL does not meet your requirements, MongoDB sells commercial licences through MongoDB Enterprise Advanced (pricing by contacting sales). PostgreSQL is released under the PostgreSQL Licence, which the project describes as a liberal open source licence similar to BSD or MIT, with no paid edition from the project itself. We describe licence terms only; check with your own legal advisers before relying on them.

MongoDB Atlas. Listed on the MongoDB pricing page in October 2026, in USD: a Free tier with 512 MB of storage; Flex at 0.011 USD per hour, capped at 30 USD per month, with 5 GB of storage; and Dedicated clusters from 0.08 USD per hour (M10, about 56.94 USD per month as estimated by MongoDB). Atlas usage is billed hourly with monthly invoices.

Managed PostgreSQL. PostgreSQL itself is free. Managed services (for example Amazon RDS for PostgreSQL and Amazon Aurora PostgreSQL-Compatible Edition) are priced by each cloud provider by instance size, storage and region, so there is no single list price to quote.

Pricing checked on the vendors' official pages on 7 October 2026. Prices change; confirm before buying.

Where each one leads

Limitations

When to choose each

When neither is right

• Caching, sessions or simple key-value lookups: an in-memory key-value store is usually a closer fit than either.
• Large analytical workloads (scanning billions of rows for aggregates): a column-oriented engine or a data warehouse is designed for this, rather than an operational database.
• A single-user app, mobile app or test fixture that needs a local file database: SQLite needs no server; see SQLite vs MySQL.
• If you are on Microsoft infrastructure and need relational features, compare SQL Server vs PostgreSQL as well.

Final recommendation

Frequently asked questions

Sources

• MongoDB Community Edition licensing
• MongoDB manual: Transactions
• MongoDB manual: Schema validation
• MongoDB manual: $lookup
• MongoDB manual: Sharding
• MongoDB manual: Replication
• MongoDB Atlas pricing
• PostgreSQL Licence
• PostgreSQL documentation: JSON types
• PostgreSQL documentation: Concurrency control (MVCC)
• PostgreSQL documentation: Table partitioning
• PostgreSQL documentation: Failover
• Citus (GitHub repository)
• Amazon RDS

Checked October 2026.

How we research comparisons: our editorial method.