Pyyan / Compare / Atlas Vector Search vs pgvector vs Pinecone vs Milvus
Atlas Vector Search vs pgvector vs Pinecone vs Milvus
4 of 5Vector Databases · verified 13 Aug 2026
| Specification | Atlas Vector Search | pgvector | Pinecone | Milvus |
|---|---|---|---|---|
| Summary | Vectors beside the documents they came from. | Vectors inside the database you already run. | Managed, and the smoothest to operate. | Built for billions of vectors. |
| Index | HNSW | HNSW, IVFFlat | Proprietary | HNSW, IVF, DiskANN, GPU |
| Hybrid search | Yes | Yes, with SQL | Yes | Yes |
| Hosting | Managed | Self-host or any managed Postgres | Managed only | Self-host and managed |
| Licence | Proprietary | PostgreSQL | Proprietary | Apache 2.0 |
| p50 latency | ~20ms | ~15ms | <10ms | ~10ms |
| Category | Vector Databases | Vector Databases | Vector Databases | Vector Databases |
| Official | MongoDB ↗ | PostgreSQL ↗ | Pinecone ↗ | Zilliz ↗ |
Highlighted rows are where these differ.
Atlas Vector Search
- No second datastore to keep in sync
- Same argument as pgvector, different database
Best for teams already on MongoDB.
pgvector
- One database for vectors, rows and joins
- The honest default: reach for something else only when this stops working
Best for almost everyone, until scale says otherwise.
Pinecone
- Sub-10ms p50 with nothing to maintain
- Costs draw scrutiny at scale; Notion moved away and cut spend ~60%
Best for teams who do not want to run a database.
Milvus
- Distributed architecture with GPU index support
- Heavier to operate than the alternatives
Best for very large corpora.