Chroma vs pgvector: which embeddable vector store wins in 2026?
Chroma is embeddable + has a cloud + AI-native ergonomics. pgvector lives inside Postgres so your vectors share a DB with your relational data. Pick Chroma for AI-first dev workflow, pgvector for Postgres-native stacks.
At a glance
| Dimension | Chroma | pgvector |
|---|---|---|
| Form factor | Embeddable + cluster + cloud | Postgres extension |
| Setup complexity | Lightest in the category | Just enable extension in existing PG |
| Performance at scale | Strong with clusterWIN | Solid; Postgres limits apply |
| Filtering with relational data | Limited | First class — full SQLWIN |
| Hybrid search | Strong nativeWIN | Possible with tsvector + pgvector |
| Ops overhead | Own service | Zero if you already run PostgresWIN |
| Best for | AI-first dev workflow, prototyping | Postgres-native production, Supabase users |
Verdict
Chroma is the right pick for AI-first development workflows where embeddability and clean ergonomics matter more than Postgres integration. pgvector is the right pick when you already run Postgres and want your vectors to live next to relational data — no new service, full SQL for filtering, simplest production. For most teams already on Supabase or Postgres, pgvector. For pure AI workflows starting fresh, Chroma.
When to pick which
Pick Chroma
Embeddable dev workflow, prototyping, AI-first stacks.
Pick pgvector
Postgres-native stacks, Supabase users, simplest ops.
FAQ
Chroma or pgvector for production?
Both work. Pgvector for Postgres-native production; Chroma cluster for AI-first production.
Cheapest of the two?
Pgvector if you already run Postgres — free; Chroma local mode is free.
Best for embedded search?
Chroma — purpose-built for in-process dev workflows.
Last updated: 2026-06-01.