# ChromaDB The open-source search infrastructure for AI. Fast, serverless, and scalable. Supporting vector, full-text, regex, and metadata search. Built on object storage and trusted by millions of developers. Start free on Cloud [Read the docs](https://docs.trychroma.com/docs/overview/introduction) ### Open source at the core Chroma is licensed under Apache 2.0. The same codebase powers both the open-source database and Chroma Cloud, so there is no vendor lock-in. ### Single-node Run locally with pip, npm, or Docker. In-memory or persistent storage. ### Chroma Cloud Fully managed, serverless, and scalable. No provisioning, no tuning. Get started in under 30 seconds. ### Bring your own cloud Deploy in your own VPC with multi-region replication and point-in-time recovery. Full control over your infrastructure. Many search methods, one platform Chroma unifies dense vector search, sparse vector search, full-text search, regex matching, and metadata filtering in a single query interface. Combine them with hybrid search for the best retrieval quality. ◇ Sparse vector search - Lexical search (BM25, SPLADE) ◆ Vector search - Semantic similarity search ● Full-text search - Trigram and regex search ◐ Metadata search - Filtering and faceted search ◊ Forking - Dataset versioning, A/B testing, and roll-outs ▣ CLI - Command-line tools for development ```javascript // configure client and collection for sparse embeddings (BM25, SPLADE) // Add documents with sparse embeddings (BM25) await collection.add({ ids: ["id1", "id2"], documents: ["Document about databases", "ML tutorial"] }) // Query with sparse vector const sparseRank = Knn({ query: "ML", key: "sparse_embedding" }); // Build and execute search const search = new Search() .rank(sparseRank) .limit(10) .select(K.DOCUMENT, K.SCORE); const results = await collection.search(search); ``` Terminal Output ``` $ node sparse-search.js Connecting to Chroma... ✓ Connected successfully Creating collection 'my_collection'... ✓ Collection created Adding documents with sparse embeddings (BM25)... ✓ Added 2 documents Querying with sparse vector... ✓ Query completed in 18ms Results (ranked by BM25 score): [\ {\ id: "id1",\ document: "Document about databases",\ score: 0.87,\ metadata: {}\ },\ {\ id: "id2",\ document: "ML tutorial",\ score: 0.45,\ metadata: {}\ }\ ] ``` Fast search over billions of multi-tenant indexes Chroma's indexes are built and optimized for object storage, offering unparalleled cost and performance. State-of-the-art vector, full-text, and regex search. Latency | Metric | Warm Latency | Cold Latency | |-----------------------------|--------------|--------------| | p50 | 20ms | 650ms | | p90 | 27ms | 1.2s | | p99 | 57ms | 1.5s | [Contact us](/content/talk-with-us/index.html) to run a POC for your specific workload. Dedicated clusters can be scaled to your specific requirements. ### Technical specs - Write throughput (per collection): 30 MB/s (2000+ QPS) - Concurrent reads (per collection): 10 (200+ QPS) - Collections per database: 1M - Records per collection: 5M - Recall: 90-100% ``` ┌───────────────────────────────┐ │ Query Layer │ │ Fast memory cache (hot) │ │ SSD cache (warm) │ └───────────────────────────────┘ ↕ Intelligent tiering ┌───────────────────────────────┐ │ Storage Layer │ │ S3 / GCS (cold) │ │ • All vectors │ │ • All metadata │ │ • All indexes │ └───────────────────────────────┘ ``` Unlike legacy search systems, Chroma is a database you'll want to be on-call for. ✓ Auto-scales with usage ✓ No manual tuning ✓ Serverless pricing Chroma takes full advantage of object storage with automatic query-aware data tiering and caching. ✓ Vectors are large: 1GB text → 15GB of vectors ✓ Memory is expensive: $5/GB/mo ✓ Object storage is not: $0.02/GB/mo ## Start searching with Chroma Start free on Cloud.