Postgres vs. MongoDB: a Complete Comparison in 2026

Why Comparing Postgres and MongoDB#

Postgres is the developers' favorite database. In the 2025 Stack Overflow survey, 55.6% of respondents used it, more than any other database, and it was the most admired database for the fourth year in a row. MongoDB sits at 24%.

ICYDK, MongoDB used to be the database everyone wanted to learn, topping the "most wanted" list for several years in the late 2010s. Both are still climbing. In the October 2026 DB-Engines ranking, Postgres is #4 and gained 45 points year over year, MongoDB is #5 and gained 7, while the big three (Oracle, MySQL, SQL Server) all lost ground.

MongoDB is widely perceived as a NoSQL database, the opposite of the SQL clan that includes Postgres. But they keep converging:

• MongoDB becomes more like a traditional RDBMS: multi-document ACID transactions, secondary indexes, and now schema validation that the database actually guarantees.
• Postgres keeps improving its JSON capabilities (indexing, SQL/JSON, JSON_TABLE), which makes people wonder whether MongoDB becomes obsolete.

In 2018, folks at The Guardian wrote a lengthy post about migrating MongoDB to Postgres. A lot of things have changed since then, but one thing still holds true: it's always painful to migrate databases.

At Bytebase, we work with both databases extensively since the Bytebase product needs to integrate with both. Our founders also led the engineering of Google Cloud SQL, one of the largest hosted database services.

Based on our operating experience, below we give an extensive comparison between Postgres and MongoDB from the following dimensions:

• License
• Data Model
• JSON Support
• Performance
• Reliability
• Scalability
• Usability
• Operability
• Ecosystem
• AI

Unless otherwise specified, the comparison below is between the current major releases, Postgres 18 vs. MongoDB 9.0. Postgres 19 is at release candidate (RC1, October 15, 2026) and not GA yet. We also use Postgres instead of PostgreSQL throughout the article, though we know the latter is the official name, which is considered as the biggest mistake in Postgres History.

License#

• Postgres is released under the PostgreSQL license, a liberal Open Source license similar to the BSD or MIT licenses.

• MongoDB Community Edition has been released under the Server Side Public License (SSPL) since October 2018. MongoDB Inc. created the license to prevent others from offering a competing MongoDB service, and it is not OSI-approved. The new self-managed search engine (mongot) ships under SSPL too. MongoDB Inc. also sells a commercial license, Enterprise Advanced.

Most companies use databases to support their internal infrastructure, and both Postgres and MongoDB permit this usage.

Data Model#

Postgres is a relational database management system. It stores data in tables with predefined columns and data types. Relationships between tables are established using foreign keys.

MongoDB is a document-oriented database: data is stored as documents in a collection. Each document is a JSON-like structure that can contain nested fields and arrays. MongoDB is designed to handle unstructured and semi-structured data.

Postgres also supports JSON columns, so one can use Postgres the MongoDB way, defining a table as:

CREATE TABLE my_collection (
   id BIGINT GENERATED ALWAYS AS IDENTITY PRIMARY KEY,
   data JSONB
);

JSON Support#

Both MongoDB and Postgres are very capable of handling JSON. MongoDB stores JSON using its own BSON format, while Postgres uses JSONB. For those interested, there is a lengthy discussion around whether to choose BSON or JSONB in Postgres. Postgres 17 added the SQL/JSON standard functions, including JSON_TABLE, which turns a JSON document into rows you can join like any other table.

MongoDB has 2 advantages:

1. Built-in schema validator. Postgres has none for JSONB; you write CHECK constraints or install an extension like pg_jsonschema. MongoDB 9.0 goes further with a constraint validation level, which applies the rules to every write and guarantees every existing document already satisfies them. Before 9.0, adding a validator said nothing about the documents already in the collection.
2. Its integration with the Node.js/frontend ecosystem. MongoDB is a favored choice among full-stack developers who commonly use Node.

Performance#

Performance is primarily determined by the access pattern. If an operation involves different entities, MongoDB is usually faster because data is de-normalized and doesn't require costly joins between tables. On the other hand, Postgres is more capable of handling complex queries thanks to SQL and its sophisticated query optimizer.

Both vendors keep posting bigger numbers. Postgres 18 added asynchronous I/O and skip scan for multicolumn B-tree indexes. MongoDB claims 9.0 is up to 35% faster on reads and 30% faster on updates than 8.0, but did not publish the methodology. Benchmark your own workload before believing either.

Reliability#

MongoDB was known to be less reliable because it didn't support ACID transactions in the early days. That changed in steps. MongoDB acquired WiredTiger in 2014 and made it the default storage engine in 3.2. Multi-document ACID transactions landed in 4.0 (2018) for replica sets and 4.2 (2019) for sharded clusters. Since 5.0, the default write concern is majority. Today, from the transaction perspective, MongoDB is as solid as Postgres.

MongoDB provides built-in automatic failover via its replica set. Postgres needs 3rd party solutions, and Patroni is the de-facto standard.

Scalability#

MongoDB scales out, while Postgres scales up.

MongoDB is a distributed database supporting automatic sharding, and you can change the shard key later (resharding). For Postgres, people usually scale up the single node first and defer the sharding solution as late as possible. Of course, sharding Postgres is doable, with an extension like Citus or in the application, as Notion did.

Usability#

MongoDB doesn't enforce schema upfront and has an easy learning curve. Back in 2010, 10gen (the original name of the company behind MongoDB) released the first official MongoDB Node.js driver. It gave developers a simple API for querying, indexing, and aggregation from Node.js. MongoDB is still a go-to choice for full-stack developers because of its ease of use.

Postgres, as a relational database, enforces schemas. And even among the relational database group, Postgres is more rigorous than peers like MySQL.

MongoDB used to be the database new learners picked. That is no longer true: in the 2025 Stack Overflow survey, 39.2% of people learning to code used Postgres and 28.5% used MongoDB (MySQL still leads that group).

On the other hand, MQL (MongoDB Query Language) is less powerful than SQL in handling complex queries.

Operability#

Running a multi-node MongoDB is easier than running a multi-node Postgres, since sharding and failover are already handled by MongoDB itself. On the other hand, if you run both databases on a single node, those distributed features in MongoDB become overhead.

MongoDB 9.0 also adds a few guardrails worth knowing before you upgrade: a single operation is capped at 1 GB or 20% of server memory (whichever is greater), and there is a default cap of 10,000 concurrently open multi-document transactions. Queries that worked on 8.x can now fail with ExceededMemoryLimit.

A variety of 3rd party vendors provide Postgres hosting services. MongoDB is different: its license means a 3rd party can only offer MongoDB as a service through a deal with MongoDB Inc. (Alibaba Cloud and Tencent Cloud have one). Amazon DocumentDB and Azure Cosmos DB only speak the MongoDB API. MongoDB's own Atlas is more polished than the equivalent Postgres hosting options. It starts with a free 512 MB tier, then Flex (capped at $30/month) and dedicated clusters from about $57/month. After all, it's hard for a 3rd party to beat a 1st party cloud service.

Ecosystem#

Postgres has an extensible architecture and is still maintained by the community. The Postgres ecosystem is thriving, with a plethora of extensions making it more capable of handling different workloads than other databases. And thanks to its liberal license and solid architecture, almost every application platform offering a hosted database chooses Postgres. From Heroku in the early days to Supabase, Neon, Render, and Fly.io.

MongoDB, one of the most successful commercial open source businesses, takes a more commercialized approach. Whenever a good solution emerges in the ecosystem, MongoDB Inc. considers acquiring it to make it an integral part of the overall MongoDB product line. Notable acquisitions include:

• WiredTiger (2014) for the storage engine.
• mLab (2018) for the hosting service.
• Realm (2019) for the mobile offering. Not every acquisition sticks: MongoDB shut down Atlas Device Sync and the Device SDKs in September 2025, and the on-device Realm database lives on only as an open source project.
• Voyage AI (2025) for embedding and reranking models.

Though Postgres and MongoDB take polarized approaches to cultivating the ecosystem, both are widely successful.

AI#

Postgres leverages the pgvector extension for vector operations, and it has become the default vector store for AI apps that already run on Postgres.

MongoDB provides built-in vector search. It used to be Atlas-only, which was a real argument for pgvector if you self-host. That argument is gone: search and vector search became generally available for self-managed MongoDB Community and Enterprise in mid-2026. With Voyage AI, MongoDB also sells the embedding models, so it covers both producing and storing the vectors.

Postgres or MongoDB#

There are obvious domains where one database is more suitable than the other:

• Choose Postgres if your application has complex business logic and requires complex queries.
• Choose MongoDB if your application has a simple data model and handles web scale data.

Yet, the majority of application use cases fall between them and both databases are very capable:

• MongoDB as a document database naturally provides 1st class JSON support. Postgres also has the best JSON support among all open source relational databases.
• For full-stack developers, MongoDB used to be the go-to choice because of its excellent Node.js integration. Postgres has been attracting them as every major 3rd party application platform offers a managed Postgres service.
• For backend developers, MongoDB became appealing ever since it added transaction support. Who doesn't want to avoid a stressful schema change procedure and enjoy a faster iteration cycle?
• MongoDB Atlas has built one of the most polished hosted database services. Many 3rd party vendors, from large to small, provide great Postgres hosting solutions.

Overall, Postgres is a more versatile database. It adopts the relational model, provides comprehensive SQL capability, carries an extensible architecture, and is driven by an enthusiastic community.

MongoDB is a holistic database solution. It popularized the document model, provides built-in scaling and high availability, offers an integral developer experience, and is driven by a sharp-minded for-profit business entity.

If choosing between Postgres and MySQL is hard, then choosing between Postgres and MongoDB is no easier. And as both databases are heading upward, the choice will only become harder 🤷‍♂️.

It's also common that Postgres and MongoDB co-exist inside an organization. And if you want to manage the database development lifecycle for both of them, please check out Bytebase.

References#

• Bye bye Mongo, Hello Postgres
• Ask HN: Is MongoDB obsolete when Postgres and SQLite provide JSON types?
• Proposal: Add JSON support for Postgres
• Discussion for using BSON or JSONB for Postgres
• MongoDB 9.0 release notes
• Herding elephants: Lessons learned from sharding Postgres at Notion