Vector databases · Chroma (Chroma, Inc.)

Chroma

Open-source embedding database designed for simple local-first development, with a managed serverless cloud tier.

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Chroma is an open-source embedding database designed to be easy to start with, commonly used by developers prototyping retrieval-augmented generation and LLM applications who want vector storage without standing up a cluster. It runs embedded in-process or as a lightweight local server for development, storing embeddings, documents and metadata together, and supports filtering alongside similarity search. For production, Chroma Cloud offers a serverless managed version with usage-based billing for writes, storage, queries and network egress, plus a Team tier with collaboration and compliance features. Chroma emphasizes a minimal API surface and Python/JavaScript-first developer experience over the breadth of enterprise features found in Milvus or Weaviate, making it a common early choice before teams outgrow it and migrate to a larger-scale vector database.

At a glance

Vendor Chroma (Chroma, Inc.)
Pricing model Free tier + paid plans
Free tier Yes
Deployment Cloud, Self-hosted
Open source Yes (Apache-2.0)
Best for Developers prototyping LLM/RAG applications who want the simplest path from local development to production.

Pricing

Self-hosted/embedded use is free; Chroma Cloud's Starter tier is usage-based with included free credits, Team adds a flat monthly fee plus usage, and Enterprise is custom-quoted.

Plan Price Notes
Starter $0/month + usage 10 databases, 10 team members, community support; usage billed per GiB written/stored/queried
Team $250/month + usage 100 databases, 30 team members, SOC 2, $100 in included credits
Enterprise Custom quote Unlimited databases/members, dedicated or BYOC clusters, SLAs

Prices read from the vendor's own page on September 21, 2026. Vendors change prices; check the source before you budget.

Features

  • Embedded or local-server deployment for development
  • Combined vector, document and metadata storage
  • Metadata filtering alongside similarity search
  • Serverless managed cloud option
  • Python and JavaScript-first client libraries
  • Simple minimal API surface

Integrations

Profile last reviewed September 21, 2026

Alternatives

Qdrant Open-source vector database written in Rust, focused on filtered similarity search with a free managed cloud tier. Weaviate Open-source vector database with built-in hybrid (vector + keyword) search, available self-hosted or as managed cloud. Pinecone Fully managed, cloud-only vector database for similarity search and retrieval-augmented generation at scale. LanceDB Open-source, embedded vector database built on the Lance columnar format for multimodal AI data.

Chroma in the index now

Terms to know

Machine learning Building models that learn patterns from data to make predictions or decisions, rather than following hand-coded rules. Retrieval-augmented generation (RAG) An architecture that retrieves relevant documents at query time and feeds them to a language model so its answers are grounded in sources. Vector database A database optimized to store numeric embeddings and quickly find the ones most similar to a given query vector.

Related guides

How to choose a vector database Pick a vector database by deployment model, scale and search type, not by benchmark charts — most teams' real constraint is who runs the cluster.