MongoDB vs PostgreSQL

Two sides of the database decision: managed database and run it yourself. When each fits, what it costs, who moves from one to the other, and what makers who chose it say.

Ask your AI about this, with this page as the source:ChatGPT ↗Claude ↗Perplexity ↗

Which fits you

Choose MongoDB if
  • Document-shaped data and teams already fluent in Mongo.

Use it whenYour team already knows Mongo, or your records vary so much in shape that a fixed schema gets in the way.

Trade-offNo joins or schema by default, so relational data and migrations take discipline you'd get for free in Postgres.

Choose PostgreSQL if
  • You already run your own server and want a flat monthly cost

Use it whenYou already run a server, or want extensions and settings a managed provider doesn't offer.

Trade-offBackups, upgrades, failover and connection pooling are yours to set up and watch.

At a glance

MongoDBPostgreSQL
Used by131 makers' products · 148 open-source projects74 makers' products · 542 open-source projects
Cost at default usagedatabase size 8 GB, data transfer 50 GB, document or row reads 10 million reads, document or row writes 2 million writes$58/mo Atlas Dedicated M10—
Moved to it on GitHubpull requests since Oct 202423 from PostgreSQL46 from MongoDB
Downloads14.5M/wk−45% vs npm45.1M/wk+18% vs npm
PricingFree Atlas tier; paid clusters by size. · paid from $0.011/hourFree and open source under the PostgreSQL License; you pay only for the server or managed service that runs it.
Free tierYesYes
Open sourceNo · self-hostableYes · self-hostable
Incidents, 90 daysfrom its status page19 (7 major)no public status feed

Who moves from one to the other

Public pull requests on GitHub since Oct 2024 whose title says "MongoDB to PostgreSQL" or the reverse — real code changes, by developers in general rather than makers only.

PostgreSQL → MongoDB23 PRs
All matching pull requests on GitHub ↗
MongoDB → PostgreSQL46 PRs
All matching pull requests on GitHub ↗

What makers say

Makers on using it for database, from Product Hunt and Starter Story interviews, each linked to the source. Products with a page of their own and fuller notes first.

On MongoDB
Shoutout to MongoDB for providing a powerful, flexible database solution that scales with our needs. Your platform helps us manage and analyze vast amounts of data seamlessly!
Exploit Alarm, the makerSep 2026 ↗
…nd technology infrastructure: Amazon Web Services (AWS) , Google Analytics , Qovery , and MongoDB Email Marketing: Sendgrid Social Media: Buffer for automation and Canva for design Freela…
Finvault, the makerOct 2022 ↗
…>Salesforce , Hubspot , HeyMantle Customer Support: Intercom Insights: MixPanel Database: MongoDB Hosting: AWS Internal Communications: Slack Creative Design: Figma + Canva Video recordin…
TxtCart®, the makerAug 2024 ↗
82 more on the MongoDB page →
On PostgreSQL
Postgres is rock-solid. It handles complex data relationships gracefully and gives us the querying power we need as we scale. For relational data, it’s hands down the best choice for us.
VisionAR, the makerSep 2026 ↗
We picked PostgreSQL over MongoDB and dedicated vector databases because one proven database handles our relational data, JSON and agent memory, with no extra stores to run.
Semos.ai Manager Agents, the makerSep 2026 ↗
Postgres is so nice to work with. pg_dump was a lifesaver when we had to move database providers. Extensions like pgvector make it the best database for building AI apps.
CamelAI, the makerSep 2026 ↗
48 more on the PostgreSQL page →

Loved and watch-outs

Themes that recur in makers' words and Hacker News comments, each linked to what it summarises.

MongoDB
Most loved
  • The flexible document model lets fast-changing products evolve their data without rigid migrations. PHPH 2PH 3HN
  • Atlas starts on a free tier and handles scaling, backups and monitoring, so there is little database to manage. PHPH 2PH 3PH 4
  • Atlas Vector Search keeps embeddings next to regular documents, so RAG and agent data combine vector and structured queries. PHHN
Watch-outs
  • Its history of unsafe defaults and lost writes left a lasting reputation that still keeps many developers away. HNHN 2HN 3HN 4
  • Anything beyond simple reads and writes is hard, and aggregation pipelines feel primitive next to SQL, which costs more over time. HNHN 2
  • Indexes are kept in memory, so larger datasets demand a lot of server RAM. HN
On Product Hunt: 5.0★, 68 reviews · mentioned most: flexible schema, NoSQL database, scalable database
PostgreSQL
Most loved
  • It is rock-solid, keeps data consistent with strong transactions, and handles complex relational queries well. PHPH 2PH 3PH 4
  • With pgvector, embeddings live next to relational data, so AI features need no separate vector database. PHPH 2PH 3PH 4
  • Extensions and features like PostGIS, JSON support and advanced indexing cover geospatial and semi-structured data too. PHPH 2PH 3
Watch-outs
  • Built-in full-text search lacks corpus-wide relevance ranking like BM25 and can need very large indexes. HNHN 2
  • It has no loose index scan, so SELECT DISTINCT over large tables needs manual workarounds. HNHN 2
  • Timestamp versus timestamptz and AT TIME ZONE behavior are easy to get wrong, with edge-case bugs around daylight saving. HNHN 2HN 3
On Product Hunt: 5.0★, 77 reviews · mentioned most: reliability, performance, scalability

Who uses each

What makers pair each with

With MongoDB
SupabaseBackend platformPostgres plus auth, storage and realtime in one project.vs MongoDB →vs PostgreSQL →
ConvexBackend platformRealtime, collaborative apps written entirely in TypeScript.
FirebaseBackend platformMobile apps that need offline sync and client-side SDKs.
NeonManaged databaseServerless Postgres that scales to zero, with a branch per preview deploy.vs PostgreSQL →
PlanetScaleManaged databaseApps that expect heavy scale and want zero-downtime schema changes.
TursoManaged databaseSQLite at the edge, or one database per customer.vs PostgreSQL →