The open-source embedding database that makes prototyping AI apps trivial — and now scales to production.
The open-source embedding database that makes prototyping AI apps trivial — and now scales to production.
Who it's for: AI app builders, indie hackers, and developers who want a vector DB they can spin up in 5 lines of Python without a managed service bill.
import chromadb; client = chromadb.Client(); collection = client.create_collection('docs'). That's it. Add documents, query by text, get back nearest neighbors. The fastest path from idea to working RAG.
Start in-memory for prototypes. Move to persistent SQLite/duckdb for local apps. Spin up client/server mode for production. Same API throughout — no rewrites.
Chroma can embed text using its default model (all-MiniLM-L6-v2), or you can plug in OpenAI, Cohere, Voyage, or any SentenceTransformers model. No need to manage the embedding step yourself.
Store metadata alongside vectors (user_id, date, source) and filter in queries. 'Find similar docs to X, but only for user Y, after Jan 2026.' Standard SQL-like syntax.
Chroma is the 'easy button' for vector search. If you're building a prototype, side project, or RAG demo, start here — you'll have it working in 10 minutes. For multi-tenant production at scale, Weaviate or Pinecone are better fits. The fact that it grew from a Discord side project to a $30M-funded company in 3 years says everything.
The fully-managed vector DB. Zero ops, fastest scale.
The open-source vector DB with hybrid search and GraphQL.
Best-in-class embeddings + reranker for RAG pipelines.
The framework for building LLM apps — Chroma is a first-class vector store.