All comparisonsComparison
pgvector vs Qdrant: choosing your vector store for production RAG.
pgvector and Qdrant are the two most popular choices for production vector storage in Python RAG applications. pgvector extends your existing Postgres; Qdrant is a dedicated vector database. Both are supported in FastAPI AI Kit — here's how to choose.
| Feature | pgvector | Qdrant |
|---|---|---|
| Setup complexity | Low — Postgres extension | Medium — separate service |
| Infrastructure cost | Zero extra — shares Postgres | Additional Qdrant service cost |
| ACID compliance | Full — Postgres transactions | Eventually consistent |
| Scale ceiling | ~1M vectors with HNSW index | Hundreds of millions of vectors |
| Filtering | SQL WHERE clause — full flexibility | Qdrant payload filters — fast |
| Multi-tenancy | Schema or table per tenant | Collections per tenant — clean |
| Backup/restore | Standard Postgres pg_dump | Qdrant snapshot API |
| Managed hosting | Neon, Supabase, AWS RDS | Qdrant Cloud, Railway |
Our verdict
Start with pgvector. It's simpler, cheaper, and ACID-compliant — and handles millions of vectors with the right index. Move to Qdrant when you need filtering on many payload fields, dedicated scaling, or multi-tenancy with clean collection isolation.
The FastAPI AI Kit angle
FastAPI AI Kit ships both. Switch from pgvector to Qdrant with `VECTOR_STORE=qdrant` — no code changes.
Ready to ship your AI backend this weekend?
Join developers who skipped weeks of boilerplate and went straight to building.
No subscriptions · One-time payment · Lifetime updates