Chroma vs pgvector — which vector database wins for your brief, in 2026
Two vector engines, side by side. Chroma is embedded vector database for ai apps. runs in-process like sqlite, prototype-first. pgvector is vector search inside postgres. the default for teams already on postgres or supabase. The verdict, the criteria, and the honest take below.
ALL VECTOR-DB COMPARISONS →Verdict in one paragraph
Both embedded options for prototype-to-mid-scale RAG. Chroma wins on Python-first DX and the LangChain / LlamaIndex tutorial coverage. pgvector wins on production maturity, joining vectors with relational data, and the ability to scale into real Postgres infrastructure. For prototypes, Chroma. For anything destined for production, pgvector.
Score across the criteria: Chroma 2 · pgvector 4
Side by side
Decision criteria
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Which is the right pick for a Python prototype?
Chroma
Chroma's Python SDK and LangChain integration make it the path of least resistance for AI prototyping.
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Which is the right pick for production?
pgvector
Postgres is production-mature. Chroma in production-mode is workable but newer and less battle-tested.
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Which lets you query vectors + relational data?
pgvector
Single SQL query. Chroma is vectors-only.
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Which is easier to run embedded?
Chroma
Chroma in-process is genuinely SQLite-like — start it from Python, no service.
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Which has the bigger ecosystem?
pgvector
Postgres ecosystem is unmatched. Chroma's ecosystem is the AI-tooling layer.
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Which scales further?
pgvector
pgvector with proper Postgres scales meaningfully past Chroma in production.
What Chroma is best for
- Prototype RAG apps where adding any infrastructure is overhead
- Single-tenant Python tools embedded in another app
- Small to mid-scale workloads under ~5M vectors
Read the full Chroma entry: /vector-databases/chroma/
What pgvector is best for
- RAG apps already running on Postgres / Supabase
- Workloads under 50 million vectors where horizontal sharding is not yet the constraint
- Teams that want vector + relational data in one query (vector + JOIN)
- Cost-sensitive deployments — the bill is whatever Postgres costs
Read the full pgvector entry: /vector-databases/pgvector/
The vector-store choice is the easy half — your retrieval design is the hard one
The hard half is your chunking, your hybrid retrieval, your reranking, your eval loop. The 30-min call is where you describe your corpus and your constraints; I tell you whether Chroma or pgvector (or something else) is your fit.