PG Vector vs MongoDB Atlas Vector Search
Compare deployment options, cost efficiency, and features to choose the right vector database for your application.
Database Comparison
Two competitive vector databases, closely matched.
Both PG Vector and MongoDB Atlas Vector Search are powerful vector databases designed for efficient similarity search and storage. They show comparable performance across key metrics.
Key similarities:
- MongoDB Atlas Vector Search offers more deployment options
- PG Vector is more cost-effective
- PG Vector has more permissive licensing
- MongoDB Atlas Vector Search has 4 more strengths
PG Vector
PG Vector is a popular Postgres extension that adds vector search capabilities directly inside a traditional relational database. It is ideal for teams that want to keep embeddings, metadata, and application data in one system without operating a separate vector database.
MongoDB Atlas Vector Search
MongoDB Atlas Vector Search is a native vector database capability built directly into MongoDB Atlas, eliminating data synchronization between operational and vector databases. It enables hybrid queries combining vector similarity with MongoDB's powerful document model, metadata filtering, aggregation pipelines, and geospatial search—all within a single unified platform.
Feature Comparison
Infrastructure & Technical Details
| Feature | PG Vector | MongoDB Atlas Vector Search |
|---|---|---|
| Deployment | Self-Hosted, Managed Cloud | Managed Cloud, Self-Hosted (Enterprise), Community Edition |
| Cost | Free (extension), infra cost only | Free tier: M0 (512MB); Flex: $8-$30/mo; Dedicated: starts $57/mo (M10); Community: Free |
| License | PostgreSQL | SSPL (self-managed) / Proprietary (Atlas Cloud) |
| Index Types | Flat, IVFFlat, HNSW | Hierarchical Navigable Small World (HNSW-like), Quantized indexes |
| Cloud Providers | AWS RDS, Azure Postgres, GCP AlloyDB, Supabase, Timescale | AWS, Azure, GCP |
| Regional Flexibility | high | high |
| Strengths | 9 | 13 |
| Weaknesses | 7 | 10 |