Chroma
FreemiumThe open-source search infrastructure for AI, built for fast, serverless vector, full-text, and metadata retrieval.
Chroma is an open-source data infrastructure for AI applications that stores embeddings, documents, and metadata in one system. It supports dense vector search, sparse lexical search, full-text search, and metadata filtering so developers can build retrieval pipelines without maintaining separate search backends. Chroma runs locally for prototyping, self-hosted for production control, or as a managed serverless service through Chroma Cloud.
What is it
Chroma is an open-source embedding database and search infrastructure designed to power retrieval in AI applications.
What it can do
It stores vectors, documents, and metadata together; supports semantic, lexical, full-text, regex, and filtered search; and scales from an in-memory prototype to a distributed cloud deployment.
Who is it for
AI developers, machine-learning engineers, and product teams building RAG, semantic search, recommendation, and multi-modal retrieval systems who want a simple API and flexible deployment options.
Key Features
Vector Search — Semantic similarity retrieval
Stores high-dimensional embeddings and queries them with approximate nearest neighbor search for finding semantically related content.
Sparse Vector Search — Lexical retrieval with BM25 and SPLADE
Captures exact-term and keyword-style relevance without relying solely on dense embeddings.
Full-Text and Regex Search — Keyword and pattern matching
Runs trigram-based full-text search and regular-expression queries over raw documents when lexical precision matters.
Metadata Filtering — Pre- and post-filtering by attributes
Attaches arbitrary key-value metadata to records and filters results before or after vector ranking.
Use Cases
Retrieval-augmented generation for LLM apps
Ground large language model responses in private documents by retrieving relevant context before generation, reducing hallucinations.
Semantic enterprise search
Build internal search over wikis, documentation, and knowledge bases so teams find answers by meaning rather than exact keywords.
Recommendation engines
Match users or items by embedding similarity to surface relevant products, content, or connections.
Multi-modal asset search
Index images, audio clips, and text together so creative and media teams search assets by content, not just filenames.
Pricing plans
Frequently Asked Questions
Discussion
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