vector databases
for Product teams starting RAG
ChromaDB vs pgvector: which is better for Product teams starting RAG?
TL;DR for Product teams starting RAG
ChromaDB is the fastest way to spin up a local or serverless vector store, while pgvector lets Postgres teams add similarity search without new infrastructure.
Key Differences
| Feature | ChromaDB | pgvector |
|---|---|---|
| Deployment footprint | ChromaDB approach | pgvector approach |
| Scaling approach | ChromaDB approach | pgvector approach |
| Ease of integration | ChromaDB approach | pgvector approach |
| SQL support | ChromaDB approach | pgvector approach |
| Ecosystem maturity | ChromaDB approach | pgvector approach |
| Managed hosting options | ChromaDB approach | pgvector approach |
Pricing Snapshot
Both open source; Chroma Cloud is in managed preview with usage-based pricing (2025-10-13); pgvector cost matches your Postgres hosting (2025-10-13)
Last reviewed: 2025-10-13
ChromaDB
Choose ChromaDB if:
- You want a drop-in vector database today
- You prefer Python-first workflows
- You need rapid local prototyping
Pros
- + Simple developer-first API
- + Persistence by default
- + Great for local prototypes
- + Python and JavaScript clients
- + Active open-source community
Cons
- - Primarily single-node today
- - Manual scaling for high availability
- - Fewer enterprise governance features
- - Limited observability tooling
pgvector
Choose pgvector if:
- You already run Postgres
- You need joins and ACID guarantees
- You plan to keep vectors alongside relational data
Pros
- + Runs inside Postgres
- + ACID transactions and SQL joins
- + Easy to deploy on managed Postgres
- + Supports hybrid similarity and filters
- + Works with familiar tooling
Cons
- - Needs tuning for very large collections
- - Slower than specialized vector databases
- - No built-in sharding
- - Requires Postgres 14 or newer
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