---
title: "MongoDB vs Postgres"
slug: "mongodb-vs-postgres"
category: "comparisons"
tags: ["comparisons", "mongodb", "postgres", "database", "document-database"]
status: "stable"
last_updated: 2026-05-14
summary: "Pick Postgres for almost every new workload; its JSONB covers most document patterns. Pick MongoDB only for true schemaless writes at horizontal-shard scale."
related: ["[[comparisons/postgres-vs-mysql]]", "[[backend/postgres]]", "[[backend/postgres-jsonb]]", "[[backend/postgres-indexes]]", "[[comparisons/sqlite-vs-postgres]]", "[[backend/index]]"]
---

## Overview

Pick Postgres for a new product. JSONB plus GIN indexes covers the document workloads that drove MongoDB adoption in the 2010s, and you keep joins, transactions, and a real type system. Reach for MongoDB only when the schema is genuinely unknown at write time and the working set is large enough that a single Postgres primary cannot hold it. The 2026 default is Postgres for app data; MongoDB is a specialized tool. See [[backend/postgres]] for the Postgres rule set and [[backend/postgres-jsonb]] for the JSONB patterns.

## When Postgres wins

Postgres is the default for app data, analytical queries, mixed structured and semi-structured workloads, and anything that benefits from joins.

- JSONB gives the same document ergonomics with GIN indexes for containment queries, partial indexes on JSON paths, and `jsonb_set` for surgical updates.
- Joins: real foreign keys, real referential integrity, real `JOIN` performance. MongoDB's `$lookup` is fine for occasional reports but loses to a Postgres join on every dimension that matters.
- Transactions are the default. MongoDB multi-document transactions exist but are slower and rarely used in idiomatic code.
- One database for relational and document data; no second store, no dual-write bug class.
- Strong type system, generated columns, partial indexes, expression indexes, materialized views, and a 30-year extension ecosystem (pgvector, PostGIS, partman).
- Operational cost: managed Postgres on Neon, Supabase, RDS, or Aurora is cheaper than MongoDB Atlas at every tier under 1 TB.

## When MongoDB wins

MongoDB is the right pick in narrow cases. Each one has to be genuinely true, not just plausible.

- Schemaless writes from many producers where the shape changes weekly and you cannot stop to migrate. Event firehoses, mobile sync inboxes, analytics raw layer.
- Horizontal sharding past 5 TB of hot data where a single Postgres primary plus read replicas no longer fits. MongoDB's sharded cluster is the canonical answer; Citus is the Postgres alternative.
- Existing MongoDB operator depth on the team: replica sets, Atlas tuning, Mongoose patterns. The muscle memory has real value.
- Embedded mobile or IoT with Atlas Device Sync as the conflict-resolution layer; this is a product, not a feature you can build on Postgres in a sprint.
- Aggregation pipeline workloads that already ship with Atlas Search and Atlas Vector Search and would cost engineering time to port.

## Trade-offs at a glance

| Dimension          | Postgres                              | MongoDB                                |
| ------------------ | ------------------------------------- | -------------------------------------- |
| Data model         | Relational plus JSONB                 | BSON documents                         |
| Schema             | Declared; migrations enforced         | Implicit; per-document                 |
| Joins              | Native, performant                    | `$lookup`; works, but slower           |
| Transactions       | Default; cheap                        | Multi-document; expensive              |
| Consistency        | Strong by default                     | Tunable per operation                  |
| Indexing           | btree, GIN, GiST, BRIN, partial, expr | btree, text, geo, wildcard, hashed     |
| Horizontal scale   | Citus or app-level sharding           | Native sharded cluster                 |
| Vector search      | pgvector                              | Atlas Vector Search                    |
| Full-text search   | `tsvector` plus GIN                   | Atlas Search (Lucene-backed)           |
| Hosting cost (1TB) | Lower on Neon, Supabase, RDS          | Higher on Atlas                        |
| Hiring             | Universal SQL skill                   | Smaller pool; Mongoose churn           |
| License            | PostgreSQL (BSD-style)                | SSPL                                   |

## Migration cost

MongoDB-to-Postgres is well-trodden and usually worth it once the team hits a join-heavy workload. Postgres-to-MongoDB is rare.

- MongoDB to Postgres: design the relational schema, then either store documents in JSONB to start and flatten over time, or flatten on day one. Use a streaming change feed to dual-write during the cutover. Plan one engineer-month per 100 collections plus a week of query-by-query verification.
- Postgres to MongoDB: only justified by sharding pain a Citus cluster cannot solve. Rewrite all joins as denormalized documents or `$lookup` aggregations, and accept the consistency trade-offs. Plan two to four engineer-months for a non-trivial app.
- Cheaper path: stay on Postgres, push hot blobs (sessions, analytics) to a JSONB column or a separate KV store like Redis or Cloudflare KV.

## Recommendation

- New SaaS or B2B app: Postgres. No exceptions worth listing.
- App that already uses JSON-heavy payloads (CMS, form builder, settings): Postgres with JSONB; see [[backend/postgres-jsonb]].
- Mobile-first app with offline sync as a hard requirement: MongoDB Atlas with Device Sync, or a CRDT layer on top of Postgres.
- Existing MongoDB cluster, no scale pain: stay until a project genuinely benefits from joins, then port that workload.
- Multi-TB time-series or event stream: Postgres with TimescaleDB or partitioning, not MongoDB.
- Embedded single-writer use case: see [[comparisons/sqlite-vs-postgres]].

## Related

- [[backend/postgres]]
- [[backend/postgres-jsonb]]
- [[comparisons/postgres-vs-mysql]]
- [[comparisons/sqlite-vs-postgres]]
- [[backend/index]]