Chroma vs pgvector
Comparing two vector database platforms on pricing, features, free tier, and trade-offs.
Quick summary
Chroma — The AI-native open source embedding database. Chroma is an open-source embedding database designed for simplicity, with Python-first DX, perfect for prototyping and small-to-medium RAG apps.
pgvector — Vector similarity search for Postgres. pgvector is an open-source Postgres extension that adds vector similarity search to any Postgres database. Runs anywhere Postgres runs (Supabase, Neon, RDS).
Feature comparison
| Feature | Chroma | pgvector |
|---|---|---|
| Pricing model | Freemium | Free |
| Starting price | Usage-based | Free (just Postgres) |
| Free tier | Yes | Yes |
| Open source | Yes | Yes |
| Type | Hybrid | Postgres extension |
| Free Tier | Self-host unlimited | Unlimited |
| Serverless | No | No |
| Self-hosted | Yes | Yes |
| Multi-tenant | No | Yes |
| Hybrid Search | No | Yes |
| Max Dimensions | 16384 | 16000 |
| Metadata Filtering | Yes | Yes |
Chroma
The AI-native open source embedding database
Pros
- Dead simple Python API
- Fast local development
- Fully open source
- Great for prototyping
Cons
- Less mature at production scale
- No hybrid BM25 search
- Managed cloud relatively new
pgvector
Vector similarity search for Postgres
Pros
- Use your existing Postgres
- No new infrastructure
- Transactional guarantees with vectors
- Free — pay only for Postgres
Cons
- Slower than purpose-built vector DBs at scale
- Index build times grow with data
- Not ideal for 100M+ vectors
Which should you choose?
Choose Chroma if you value open source and want the option to self-host, and a free tier is important for your stage. Choose pgvector if you value open source and want the option to self-host, and a free tier is important for your stage.