Open-source AI application database with vector, full-text, and metadata search — designed to be embeddable, easy to run locally, and now offered as Chroma Cloud with usage-based serverless pricing from $5/month.
Open-source AI application database with vector, full-text, and metadata search — designed to be embeddable, easy to run locally, and now offered as Chroma Cloud with usage-based serverless pricing from $5/month.
Chroma is the open-source 'AI application database' that became popular as the easiest way to run a local vector store while prototyping a RAG app — a single pip install chromadb and a few lines of Python and you have persistent vector search with metadata filters. The project has since matured into a production system: it ships an embedded mode for in-process use, a client-server mode for single-node deployments, and Chroma Cloud, a fully managed serverless offering. Chroma indexes vector, full-text (BM25), and metadata fields together, so a single query can filter on structured fields, search on keywords, and rank by vector similarity. Chroma Cloud's published pricing starts at a $5/month minimum on the Starter plan, with pay-as-you-go usage charges of roughly $2.50/GiB-month written, $0.33/GiB-month stored, $0.0075/TiB queried, and $0.09 per 1M tokens of integrated embedding. Higher tiers (Team and Enterprise) add SOC 2, SSO, longer retention, and custom contracts. Chroma is OSS-first under Apache 2.0, integrates natively with LangChain, LlamaIndex, Haystack, and the OpenAI Assistants pattern, and exposes a Pythonic API that has made it the de facto vector DB for tutorials, notebooks, and small-to-mid production apps.
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Chroma is the easiest vector database to get started with, perfect for prototyping and small-scale RAG applications. Its simplicity is both its greatest strength and limitation — teams often outgrow it as data scales up.
Combines dense vector similarity, sparse BM25/SPLADE retrieval, full-text trigram and regex search, and metadata filtering in a single query API — eliminating the need to operate separate search systems for hybrid retrieval.
Chroma Cloud is built on object storage with automatic data tiering, claiming up to 10x cost reduction compared to vector DBs that keep all indexes in memory or on SSD. Scales transparently with data volume and traffic.
Forks let teams branch a collection for A/B tests, staged rollouts, or reproducible experiments — bringing git-like workflows to retrieval indexes, which most vector databases don't support natively.
Engineered for low-latency queries across billions of multi-tenant indexes, making it well-suited for SaaS applications that need isolated per-user or per-org knowledge bases without provisioning separate clusters.
Run Chroma as an in-process Python/TypeScript library for local prototypes, self-host it on your own infrastructure, or use the managed Chroma Cloud — with the same API across all deployment modes.
Official client libraries for Python, TypeScript, and Rust, plus a command-line tool for development workflows. Native integrations with LangChain, LlamaIndex, and other LLM frameworks.
Chroma Cloud is SOC 2 Type II compliant, providing the security baseline required for production AI workloads handling sensitive customer data.
$0 (Apache 2.0)
$5/month minimum + usage
From ~$34/month
Custom
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Chroma has expanded well beyond its original role as a simple embedding database. The platform now offers a dedicated Sync product for keeping external data sources continuously indexed, an Agent-focused product line, and a managed Database service on Chroma Cloud. The retrieval engine has grown to support sparse vector search (BM25 and SPLADE) alongside dense vectors, plus trigram and regex full-text search — making hybrid retrieval a first-class feature rather than an integration project. Dataset forking has been introduced for git-like versioning, A/B testing, and rollouts of retrieval indexes. The cloud platform is now SOC 2 Type II compliant, and the team has emphasized object-storage-backed architecture with automatic tiering for up to 10x cost savings versus traditional vector DBs. Adoption has crossed 15M+ monthly downloads and 27K+ GitHub stars, reinforcing Chroma's position as a default open-source choice for AI retrieval.
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