pgvector vs Qdrant
A side-by-side comparison of pgvector and Qdrant, two Vector DB tools, drawn from Ignaite's continuously-verified listings.
Compared from listings verified as of
At a glance
| Attribute | pgvector | Qdrant |
|---|---|---|
| Category | Vector DB | Vector DB |
| Pricing (differs) | FREE | FREEMIUM |
| License (differs) | Open source | Open core |
| Deployment (differs) | Self-host | Hybrid |
| Platforms | API | API |
| Model support | Model-agnostic | Model-agnostic |
| Vendor (differs) | pgvector community | Qdrant |
| Capabilities (differs) |
|
|
The honest brief
pgvector
Keeps vectors in your existing Postgres, so you JOIN against relational data and back it all up together.
- No new database to operate
- JOIN embeddings with relational data
- Free and open source
- Works on Supabase, Neon, any managed Postgres
- Scales worse than dedicated vector DBs
- Tuning HNSW/IVFFlat is on you
- No built-in hybrid search out of the box
Qdrant
Rust single-binary you can self-host, with payload filtering strong enough that teams pick it for metadata-heavy search.
- Open source, written in Rust
- Self-host or managed cloud
- Strong payload/metadata filtering
- Predictable latency at scale
- More ops than fully-managed rivals
- Smaller ecosystem than Pinecone
- Advanced features lean on managed cloud
When to pick which
Across the signals we compare, pgvector and Qdrant differ on pricing, license, and deployment:
- Pricing
- FREE · FREEMIUM
- License
- Open source · Open core
- Deployment
- Self-host · Hybrid
Their capability lists differ in recorded depth — compare the full lists above before deciding.