Chroma

What is Chroma?

Chroma is an Apache 2.0 licensed search database that developers use to give AI applications memory and retrieval. It stores embeddings, documents and metadata, and answers queries with vector similarity, sparse search such as BM25 and SPLADE, full-text and regex matching, and metadata filtering, often combined in one hybrid search call. You can run the open-source version anywhere, from a laptop to your own servers, with clients for Python, TypeScript and Rust. Chroma Cloud is the managed, serverless version built on object storage, so there is no cluster to size or tune, and it adds collection forking, a dashboard, a CLI and sync from S3, GitHub and web pages. Chroma Cloud pricing is usage based: $2.50 per GiB written, $0.33 per GiB stored each month and $0.0075 per TiB queried. The Starter plan costs $0 plus usage and the Team plan costs $250 per month plus usage.

Use cases

• Store embeddings for retrieval-augmented generation
• Run hybrid vector and full-text search
• Add long-term memory to AI agents
• Search code and documents semantically
• Version and A/B test datasets with forks
• Sync S3 files and web pages into search

Who it's for

• AI developers
• Machine learning engineers
• Startups
• Enterprises

Why use Chroma

• Open-source Apache 2.0 code means you can self-host and avoid lock-in, and data export is simple.
• One database handles vector, keyword, regex and metadata queries, so you need fewer systems.
• Serverless pricing means you pay for writes, storage and queries without provisioning capacity.
• Object storage keeps costs low, with Chroma saying it can be up to 10x cheaper than alternatives.
• Enterprise options include BYOC, single-tenant clusters, HIPAA and SSO for regulated teams.

Chroma features

• Vector search for semantic similarity
• Sparse vector search with BM25 and SPLADE
• Full-text trigram and regex search
• Metadata filtering and faceted search
• Collection forking for dataset versioning and roll-outs
• Search API with hybrid ranking and batch operations
• Python, TypeScript and Rust clients plus a CLI
• Serverless cloud on object storage with automatic data tiering
• Chroma Sync for S3, GitHub, web pages and file uploads
• SOC 2 Type II, with BYOC and on-prem for enterprise

Chroma integrations

• AWS
• Azure
• S3
• GitHub
• LangChain
• DSPy

How to get started with Chroma

1. Install the open-source package or create a database in Chroma Cloud.
2. Create a collection and add documents, embeddings and metadata with the Python, TypeScript or Rust client.
3. Query with vector similarity, sparse, full-text, regex or metadata filters, or combine them in the Search API.
4. Fork collections to test changes, and use Sync to keep S3, GitHub or web content indexed.
5. Monitor usage in the dashboard and move to Team or Enterprise as production traffic grows.

Chroma limitations

• Chroma Cloud multi-tenant regions are AWS us-east-1 and GCP europe-west1
• Sync, the CLI and Search Agent are not available in the europe-west1 region
• Each database stays in the single region you pick, so moving data needs a new database and reindexing
• Exceeding customer-set or Chroma-set usage limits pauses the service
• The Team plan's $100 of included usage does not roll over

Chroma pricing

Chroma is open source, so you can use and self-host it for free. Paid plans start at $250 per month, according to the official pricing page on 24 Oct 2026.

Chroma plans

• Starter

  $0/month + usage

  includes $5 in free credits

  • 10 databases
  • 10 team members
  • Usage-based pricing after credits

• Team

  $250/month + usage

  includes $100 credits

  • 100 databases
  • 30 team members
  • Slack support
  • SOC II
  • Volume-based discounts

• Enterprise

  Custom

  • Unlimited databases and team members
  • Dedicated support
  • Single tenant and BYOC clusters
  • SLAs

Plans as listed on the official pricing page on 24 Oct 2026. Prices can change and may vary by country or exclude tax. Check the official pricing page.

Chroma alternatives

The main Chroma alternatives listed on Debuly are Pinecone, Weaviate, Qdrant, pgvector, LanceDB and ReMe. The table compares pricing and platforms; order is A–Z.

1. 

   LanceDB

   Open-source vector database and multimodal lakehouse for AI search, retrieval, feature engineering and training data.

   Developer platformsResearch and search
   Open sourceVisit LanceDB (official site)
2. 

   pgvector

   Open-source PostgreSQL extension that adds vector columns, similarity search and HNSW and IVFFlat indexes to your existing database.

   Developer platformsCoding
   Open sourceVisit pgvector (official site)
3. 

   Pinecone

   Managed vector database for AI search and RAG, with dense, sparse and full-text indexes, inference and an Assistant API.

   Developer platformsResearch and search
   FreemiumVisit Pinecone (official site)
4. 

   Qdrant

   An open-source vector database and search engine for AI workloads, available self-hosted or as Qdrant Cloud with a free tier.

   Developer platformsResearch and searchAutomation and agents
   FreemiumVisit Qdrant (official site)
5. 

   ReMe

   Open-source memory layer for AI agents that stores knowledge as local Markdown files with hybrid search and a linked graph.

   Developer platformsAutomation and agentsCoding
   Open sourceVisit ReMe (official site)
6. 

   Weaviate

   Open-source vector database with hybrid search and a managed Weaviate Cloud for semantic search, RAG and recommendations.

   Developer platformsResearch and search
   FreemiumVisit Weaviate (official site)

Chroma FAQ

Sources & verification

Facts on this page come from the official sources below, each with the date we read it. Written by Debuly; the vendor did not review this page.

Image credits

• Logo: from trychroma.com · fetched 23 Oct 2026 · Vendor trademark, used only to identify the product; removed on request
• Cover: none collected yet, so a generated tile is shown.

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