Chroma
by Chroma · Open-source search infrastructure for AI
BenchRank score
Screenshots of Chroma
Homepage
Overview
Chroma is an open-source (Apache 2.0) search engine for AI applications, covering vector, sparse vector (BM25, SPLADE), full-text, regex and metadata search. Indexes are built on object storage such as S3 or GCS, with memory and SSD caching and automatic tiering. It can be self-hosted or run serverlessly on Chroma Cloud, using Python, TypeScript or Rust clients.
- Best for
- Developers building AI search and retrieval who want an open-source engine with a hosted option
- Pricing
- Starter is $0/month with $5 in free credits then usage-based pricing ($2.50 per GiB written, $0.33 per GiB stored per month, $0.0075 per TiB queried, $0.09 per GiB returned); Team is $250/month plus usage, and Enterprise is custom-priced.
- Runs on
- Self-hostedCLI
Strengths and trade-offs
Strengths
- Apache 2.0 open source, runnable on your own infrastructure
- Vector, sparse, full-text, regex and metadata search in one system
- Warm query latency p50 20ms at 100k vectors (384 dim)
- Free Starter plan with $5 credits; Python and JS/TS clients
Trade-offs
- Cold queries are far slower than warm: p99 1.5s versus 57ms
- Stated caps of 5M records and 10 concurrent reads per collection
- Team plan costs $250/month before any usage charges
- Four separate usage meters make monthly cost hard to predict
How Chroma markets itself
A structured read of the promise, proof and page design on Chroma’s captured homepage.
Homepage capture
“Open-source search infrastructure for AI”
- Angle: Open source / ownership
- Hero: Product screenshot
Pricing
Published plans and prices from Chroma’s own pricing page.
How this score is made up
Each dimension is scored out of 100 and combined into the headline score using fixed weights.
MCP support
Whether an agent can drive the product through the Model Context Protocol, and how much setup that takes.
API quality
Public API surface: machine-readable spec, official SDKs, documented auth, errors, rate limits and versioning.
Documentation
Publicly reachable docs — coverage, freshness, code samples and machine readability.
Agent friendliness
How readable the site is to an automated client: llms.txt, structured data, server-rendered content, crawler access.
Pricing transparency
Whether real prices are published, self-serve signup exists, and usage costs are knowable without a sales call.
Changelog
A public, dated record of what shipped and when — the clearest signal that a product is still alive.
Marketing site structure
Whether the site answers a buyer's questions: clear positioning, the pages that matter, and accessibility.
Page speed
How fast the site loads for real visitors: Chrome UX Report 75th-percentile LCP, INP and CLS, with a Lighthouse mobile run standing in where a site has too little traffic for field data.
Operational trust
Status page and incident history, security disclosure, compliance and data-processing documentation.
Measured, but not part of the score
Useful to know, but not a mark for or against the product — so these do not affect the ranking.
Openness
Source availability, self-hosting, data export and open standards. Scored and shown, but not part of the composite — paid SaaS is not worse for being paid SaaS.
Maintenance
Release cadence and repository activity. Scored and shown, but not part of the composite — it is only measurable for open repositories.
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Where this comes from
The Chroma pages BenchRank reads when it scores the product — its documentation, release notes, status and security pages, and its repository where there is one.
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