pgvector vs Weaviate
A side-by-side comparison of pgvector and Weaviate, two Vector DB tools, drawn from Ignaite's continuously-verified listings.
Compared from listings verified as of
At a glance
| Attribute | pgvector | Weaviate |
|---|---|---|
| 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 | Weaviate |
| 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
Weaviate
Built-in vectorizer modules embed text inline — raw text in, vectors out — so you skip running a separate embedding pipeline.
- Hybrid BM25 + vector search
- Self-hostable or managed cloud
- GraphQL and REST APIs
- Resource-heavy at large scale
- Module config has a learning curve
- Managed tier costs add up
- Newer than some lexical engines
When to pick which
Across the signals we compare, pgvector and Weaviate 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.