Recommendation
Use pgvector when you already depend on PostgreSQL and want to keep vector retrieval close to relational data. Consider Qdrant when a dedicated vector-search service better fits indexing, scaling and operations.
Benchmark
Test realistic embeddings, filters, recall, latency, index build and update load. Verify plugin/extension availability before selecting pgvector on managed hosting.
Structured comparison
| Criterion | Qdrant ↗ | pgvector ↗ | PostgreSQL ↗ |
|---|---|---|---|
| Purpose | Vector database for similarity search. | Vector similarity search extension for PostgreSQL. | Open-source relational database with advanced SQL. |
| Pricing model | open-source | open-source | open-source |
| Deployment | Self-hosted / managed | PostgreSQL extension | Self-hosted / managed |
| Best for | AI apps storing and querying embeddings as vectors. | Applications combining relational data and embedding similarity search. | Applications needing relational integrity, SQL and extensibility. |
| Limitations | Applications with no embedding or similarity search needs. | Hosted databases that do not allow the needed extension version. | Teams without the operational capacity to run or select a managed database. |
Editorial note
Examples are starting points, not production security audits. Confirm dependencies, versions and pricing using linked vendor documentation.