Pyyan / Compare / LanceDB vs pgvector vs Qdrant vs Milvus
LanceDB vs pgvector vs Qdrant vs Milvus
4 of 5Vector Databases · verified 13 Aug 2026
| Specification | LanceDB | pgvector | Qdrant | Milvus |
|---|---|---|---|---|
| Summary | Embedded, multimodal, columnar. | Vectors inside the database you already run. | The fastest of the purpose-built stores. | Built for billions of vectors. |
| Index | IVF-PQ, HNSW | HNSW, IVFFlat | HNSW | HNSW, IVF, DiskANN, GPU |
| Hybrid search | Yes | Yes, with SQL | Yes | Yes |
| Hosting | Embedded and managed | Self-host or any managed Postgres | Self-host and managed | Self-host and managed |
| Licence | Apache 2.0 | PostgreSQL | Apache 2.0 | Apache 2.0 |
| p50 latency | Varies | ~15ms | 4ms | ~10ms |
| Category | Vector Databases | Vector Databases | Vector Databases | Vector Databases |
| Official | LanceDB ↗ | PostgreSQL ↗ | Qdrant ↗ | Zilliz ↗ |
Highlighted rows are where these differ.
LanceDB
- Lance columnar format, versioned like a table
- Runs in-process, no service to operate
Best for multimodal data and local apps.
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.
Qdrant
- Written in Rust; 10 to 25% faster than Weaviate or Milvus on common workloads
- Strong filtering alongside vector search
- Self-host or managed, same engine
Best for latency-sensitive retrieval.
Milvus
- Distributed architecture with GPU index support
- Heavier to operate than the alternatives
Best for very large corpora.