Chroma vs pgvector
A side-by-side comparison of Chroma and pgvector, two Vector DB tools, drawn from Ignaite's continuously-verified listings.
Compared from listings verified as of
At a glance
| Attribute | Chroma | pgvector |
|---|---|---|
| Category | Vector DB | Vector DB |
| Pricing (differs) | FREEMIUM | FREE |
| License (differs) | Open core | Open source |
| Deployment (differs) | Hybrid | Self-host |
| Platforms | API | API |
| Model support | Model-agnostic | Model-agnostic |
| Vendor (differs) | Chroma | pgvector community |
| Capabilities (differs) |
|
|
The honest brief
Chroma
Runs embedded inside your Python process — the lowest-friction way to prototype RAG before you need a server at all.
- Pip-install, embedded in-process
- Minimal setup for prototyping
- Open-source
- Hosted option when you outgrow local
- Not built for massive scale
- Fewer enterprise features than rivals
- Python-centric ergonomics
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
When to pick which
Both cover Vector search.
Pick Chroma if you need Embeddings.
- Embeddings (secondary capability)
They also differ on:
- Pricing
- FREEMIUM · FREE
- License
- Open core · Open source
- Deployment
- Hybrid · Self-host