Postgres vs MongoDB
One question decides most projects: is a record something you read on its own, or something that only makes sense joined to five other things?
The verdict
Choose Postgres when records relate to each other and rules must hold across them, such as orders, payments and permissions. Choose MongoDB when each document is written and read as a whole and its shape varies, such as product catalogues, event data or content.
The old framing, structured against unstructured, no longer settles anything. These days Postgres stores JSON documents and indexes inside them, while MongoDB supports transactions across documents.
What still differs is where each one is comfortable. Postgres expects you to describe relationships up front. MongoDB expects data accessed together to be stored together, which is its own documented core principle.
How we compared these, and our bias
Verified
State the bias first: we run Postgres. This site is built on it, four of our recent builds use Supabase, which is Postgres underneath, and our engineers reach for it by default. Read the Postgres column knowing that.
So every claim about MongoDB here comes from MongoDB's own current documentation rather than from our habits. We have quoted its guidance where it disagrees with a common assumption. Both databases are mature and either can run a serious business. Anyone telling you one is simply better has not asked what you are building.
- Data shape
- Whether records are read alone or joined, and how often the shape changes.
- Rules that must hold
- What has to stay true across more than one record, and what breaks if it does not.
- Operational burden
- What a small team has to run, monitor and recover in production.
- Documented vendor guidance
- What each project recommends for its own product, taken from current docs rather than from third-party benchmarks.
The dimensions that change the decision
Checked against both projects' documentation on 11 August 2026.
| Dimension | PostgreSQL | MongoDB |
|---|---|---|
| Data model | Tables and rows, with relationships declared up front. | Documents in collections. Fields may differ between documents in the same collection. |
| Changing the shape | A schema change, applied as a migration everyone runs. | Write the new field. MongoDB's docs describe improving the model as you develop. |
| Combining records | Joins are native and the query planner handles them. | Best avoided. Store data accessed together in one document instead. |
| Rules across records | Transactions are ordinary. Read Committed is the default isolation level. | Supported across documents, but MongoDB warns they cost more than single-document writes. |
| Enforcing correctness | The database refuses bad data: types, foreign keys, constraints. | Optional validation, applied where you choose to add it. |
| AI and vector search | pgvector stores embeddings beside the rest of your data. | Vector search is available in Atlas, its managed service. |
| Getting it wrong | Migrations become slow and scary on very large tables. | Duplicated data drifts out of sync, and fixing it is application work. |
PostgreSQL
Strong at
- The database enforces the rules, so a bug in one service cannot leave an order without a customer.
- Joins mean each fact is stored once, which is why reports stay possible as the product grows.
- It handles JSON documents too, so a flexible corner of the data does not require a second database.
- Extensions cover search, time series and embeddings, which keeps one system to operate.
- Every hosting provider offers it, and the knowledge is easy to hire.
Weak at
- Schema changes are real work, and on a large table a careless migration causes an outage.
- Modelling up front is slower when you genuinely do not yet know the shape of the data.
- Scaling writes beyond one primary means sharding, which is not built into the ordinary setup.
- Deeply nested or wildly varying records are awkward, even with JSON support.
MongoDB
Strong at
- Documents map directly to objects in application code, which MongoDB names as a benefit of its flexible schema.
- A record read as one unit needs no joins, so the common query stays fast and simple.
- Fields can differ between documents, which suits catalogues and data arriving from many sources.
- Sharding for horizontal scale is part of the product rather than an add-on.
- Early iteration is quick when the shape is still moving.
Weak at
- Correctness moves into your application. Nothing stops a document that contradicts another one.
- Data stored together also gets duplicated, and keeping copies in step is your job.
- MongoDB itself warns that distributed transactions are not a replacement for effective schema design.
- Questions nobody planned for are harder, because the model was shaped around known access patterns.
- Flexible schemas quietly become undocumented ones, and the rules end up spread across the codebase.
Which one fits your data?
Three questions about what you are storing, not which one you have used before.
Does a record make sense on its own?
What happens if two records disagree?
How much of your data is free-form?
Every outcome
- PostgreSQL
- Records refer to each other and something must stay true across them. Money, bookings, permissions and stock all live here, and the database enforces the rule rather than trusting every service to.
- MongoDB
- Each document is read and written whole, and its shape varies between records. Storing what is accessed together in one place is the model working as designed.
- Postgres, with documents inside it
- Most of the data relates, and one part is genuinely free-form. Postgres stores JSON and indexes it, which is usually better than running a second database for one corner of the product.
- OrderThe record a customer placed.
- PostgresRows in tables, joined on read.
- MongoDBOne document holding the lot.
- Price changePostgres: one row. Mongo: every copy.
- ReportJoins are cheap on one side only.
Neither layout is wrong. The cost shows up later, on the question you had not thought of when you chose.
Rules that settle it
Pick on the shape of the data and the cost of it being wrong. Both databases are fast enough, and performance arguments rarely survive contact with a real workload.
- 01Choose Postgres if money, stock or access rights are involved and two records must never disagree.
- 02Choose Postgres if people will ask questions of the data that nobody has thought of yet.
- 03Choose MongoDB if each document is self-contained and the fields differ from one to the next.
- 04Choose MongoDB if the shape is still moving weekly and nothing depends on it holding steady.
- 05Choose neither if the real problem is a spreadsheet with three hundred rows. A database is not the fix for that.
Common questions
01Can you use Postgres and MongoDB together?
Yes, and it is common in larger systems. Transactional records live in Postgres while high-volume documents or events go to MongoDB. The cost is two systems to back up, monitor and staff, so it is worth doing only when one database is genuinely a bad fit for part of the workload.
02Is MongoDB faster than Postgres?
Neither is faster in general, and the honest answer depends on the query rather than the product. Fetching one self-contained document is quick in MongoDB. Combining data from several places is quick in Postgres. Benchmarks that ignore your access pattern predict nothing about your application.
03Does Postgres handle JSON well enough to skip MongoDB?
For most projects, yes. Postgres stores JSON documents, indexes fields inside them, and queries them alongside ordinary columns. The reason to run MongoDB is a workload that is documents nearly all the way down, or a need for its sharding model.
04How hard is it to migrate from MongoDB to Postgres?
Moving the data is the easy half. The work is deciding which duplicated copies become the single source of truth, and rewriting the application rules the database will now enforce. Expect the migration to surface inconsistencies that were already there and invisible.
05Which is better for an AI product?
Either can store embeddings, so this rarely decides it. Postgres does it with the pgvector extension, keeping vectors beside the records they describe, and MongoDB offers vector search in Atlas. Choose on the rest of your data, because the AI part is usually the smaller half of the system.
