Postgres vs MongoDB for AI Apps
Why pgvector is eating the world.
TL;DR
For AI apps, Postgres with pgvector is superior to MongoDB. Keeping your vector embeddings in the same database as your operational data reduces latency, complexity, and cost.
Vector Database Comparison
| Metric | Postgres (pgvector) | MongoDB Atlas Vector |
|---|---|---|
| Join Capability | Native SQL Joins | Complex Aggregations |
| Latency | Low (Same connection) | Higher (Separate index) |
| Ecosystem | Universal SQL | Proprietary Query Language |
| Cost | Included in DB | Separate Tier |
The Vector Revolution
AI apps need to store vectors (embeddings). MongoDB Atlas has vector search, but it separates your operational data from your vector data. Postgres with pgvector keeps them together.
Why Neon?
Neon separates storage from compute. This means you can branch your database like git. For testing AI models on production data without breaking production, this is a superpower.
Deploy pgvector instantly
Vibe Pilot sets up your Neon database with pgvector pre-configured.
Start Generating →