Chroma
FreeOpen-source embedding database for AI applications. The simplest way to build LLM apps with embeddings, designed for developer productivity and ease of use.
What does this tool do?
Chroma is an open-source vector database purpose-built for AI applications that need to store and search embeddings at scale. It provides semantic search capabilities by indexing vector embeddings alongside metadata, enabling LLMs to retrieve relevant context efficiently. The database handles vector similarity search, sparse vector/BM25 lexical search, full-text regex matching, and metadata filtering in a single system. Built on object storage (S3/GCS) with intelligent tiering, Chroma separates hot query data in memory from cold archive storage, dramatically reducing infrastructure costs compared to traditional in-memory vector databases. It's designed for developer productivity with minimal operational overhead—auto-scaling, serverless pricing, and no manual tuning required.
AI analysis from Feb 23, 2026
Key Features
- Vector similarity search with 90-100% recall over billions of indexed vectors with intelligent data tiering
- Hybrid search combining sparse vectors (BM25/SPLADE), full-text regex, and metadata filtering in a single query
- Dataset versioning and forking for A/B testing and rollout strategies without duplicating storage
- Multi-language SDK support (TypeScript, Python, Rust) with straightforward API for collection creation and querying
- Enterprise BYOC deployment with VPC isolation, multi-region replication, and point-in-time recovery
- Automatic intelligent caching with memory (hot), SSD (warm), and object storage (cold) tiers eliminating manual tuning
- CLI tools for development and local-first testing before cloud deployment
Use Cases
- 1Building Retrieval-Augmented Generation (RAG) systems where LLMs need to fetch relevant documents before generating responses
- 2Creating chatbots and conversational AI that require semantic search over large knowledge bases
- 3Implementing recommendation systems using vector similarity to find related products, articles, or users
- 4Building semantic search features in applications where users query by meaning rather than keywords
- 5Enterprise AI applications requiring multi-tenant isolation, compliance (SOC 2), and data residency controls via BYOC deployment
Pros & Cons
Advantages
- 10x cost reduction compared to alternatives by leveraging object storage with automatic tiering—stores vectors cheaply while keeping hot queries fast (20ms p50 latency)
- Zero operational burden with auto-scaling, serverless pricing, and no manual database tuning required; SOC 2 Type II certified for enterprise trust
- Multi-search capabilities in one system: vector similarity, BM25/SPLADE sparse vectors, full-text regex, and metadata filtering eliminate the need for separate search systems
- True open-source Apache 2.0 license with 24k GitHub stars and 5M+ monthly downloads, providing strong community backing and transparency
- BYOC enterprise option with multi-region replication and point-in-time recovery for organizations requiring data sovereignty
Limitations
- Cold start latency of 650-1,500ms for initial queries is acceptable but noticeable compared to in-memory databases, limiting real-time interactive use cases requiring sub-100ms responses
- Write throughput capped at 30 MB/s (2000+ QPS) per collection and concurrent reads at 5 (100+ QPS)—limiting for high-frequency batch insert/update scenarios
- Limited to 5M records per collection maximum, which could be restrictive for organizations managing datasets exceeding this threshold without sharding complexity
- Pricing details not publicly available on the website; unclear cost structure for cloud deployments beyond vague 'serverless pricing' claims
- Relatively young technology stack compared to mature alternatives like Elasticsearch or Pinecone; fewer production case studies and proven patterns at enterprise scale
Pricing Details
Pricing details not publicly available on the website. The site mentions 'serverless pricing' and offers a free cloud tier with the option to 'get started locally' for the open-source version, but specific pricing tiers, usage limits, and per-request/storage costs are not disclosed.
Who is this for?
AI/ML engineers and startup founders building LLM applications, particularly those needing semantic search without managing database infrastructure. Enterprise organizations requiring multi-tenant isolation, compliance certifications, and data residency controls via BYOC. Development teams seeking cost-effective vector search without in-memory database overhead. Teams already in the Chroma ecosystem (Capital One, UnitedHealthcare, Weights & Biases) or deploying via integration partners.