Chroma
Developer-first embedding database that runs anywhere
Chroma is profiled here as a RAG Framework tool for engineering teams. Read about features, pricing, and how it compares to related options in the tools directory.
Description
Chroma is an open-source embedding database created in 2022 by Jeff Huber and Anton Troynikov, built so the path from pip install to a working retrieval app takes minutes. It runs in-process for notebooks and prototypes or as a client-server deployment, persisting data locally through SQLite. Chroma Cloud, launched in 2025, adds a serverless hosted option billed on usage. The company rewrote the core in Rust ahead of the cloud launch, carrying one API from laptop prototypes through distributed production deployments. Query results return documents, embeddings, and metadata together, which keeps application code short.
Key Capabilities:
In-process, client-server, and serverless cloud deployment modes
Vector, full-text, and metadata search in one query API
Collection-based API with four core operations
Multimodal embedding support
First-class LangChain and LlamaIndex integrations
Apache 2.0 license with Python and TypeScript clients
Alternative tools
- Docling
Open-source document conversion built for RAG pipelines
- Unstructured
Turn raw documents into LLM-ready structured data
- Voyage AI
Retrieval-optimized embedding and reranking models
- Qdrant
Rust-based vector search engine with rich filtering
- Weaviate
Open-source vector database with native hybrid search
- Pinecone
Managed vector database for production retrieval workloads