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Open-source search infrastructure for AI
Fast, serverless, and scalable infrastructure supporting vector, full-text, regex, and metadata search. Built on object storage and trusted by millions of developers. Open-source Apache 2.0.
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What can I build with Chroma? Who else uses Chroma? How does Chroma scale?

Chroma
knowledge_base - 1,277,467 records

awaiting query input

15M+ monthly downloads
Apache 2.0
27k Github stars

Low latency search
Fast queries over billions of multi-tenant indexes.

Up to 10x cheaper
Built on object storage with automatic data tiering.

No engineering ops
Scales with your data and traffic. SOC 2 Type II.

Features
◇ 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

TypeScript Python Rust
Copy

// 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: {}
}
]

Performance
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
Query Latency
@384 dim at 100k vectors

Warm

Cold

p50
20ms

650ms

p90
27ms

1.2s

p99
57ms

1.5s

Contact us 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%

Zero-ops infra
┌───────────────────────────────┐
│ 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

Enterprise
Chroma brings the security, compliance, education and operational model enterprises need with our Apache 2.0 architecture.
BYOC in your VPC, multi-cloud/multi-region replication, point-in-time-recovery ensure a resilient and scalable search system with the same 0-ops story as Cloud.

Learn more Contact us
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▓░ ░▓
▓░ ┌──────────── YOUR VPC ─────────────┐ ░▓
▓░ │ │ ░▓
▓░ │ █ DATA PLANE █ │ ░▓
▓░ │ │ ░▓
▓░ │ Your data, your cloud │ ░▓
▓░ │ │ ░▓
▓░ │ │ ░▓
▓░ └───────────────────────────────────┘ ░▓
▓░ │ ░▓
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▓░ ═════════════════════════════════════ ░▓
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▓░ ░▓
▓░ ┌────────── CHROMA VPC ─────────────┐ ░▓
▓░ │ │ ░▓
▓░ │ █ CONTROL PLANE █ │ ░▓
▓░ │ │ ░▓
▓░ │ Managed by Chroma │ ░▓
▓░ │ Monitoring, backups, ops │ ░▓
▓░ │ │ ░▓
▓░ └───────────────────────────────────┘ ░▓
▓░ ░▓
▓░ ✓ BYOC in your VPC ░▓
▓░ ✓ Multi-region replication ░▓
▓░ ✓ 0-ops management ░▓
▓░ ░▓
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[▶] Videos

Deep dive: Using Reranking to improve search results
15:23

Chroma Context-1
13:20

Lexical Search in Chroma
4:41

Schema() and Search() APIs
9:02

Context Engineering Episode 3 - Lance Martin - LangChain
1:02:36

Beyond The Embedding: Vector Indexing
11:26

Long live Context Engineering
57:00

Context Rot
7:55

Context Engineering: The Outer Loop
23:43

Context Engineering for Engineers
11:16

Reliability at Scale
26:30

Context Engineering with DSPy
12:46

See more
Visit our YouTube channel →

[●] Open source community
Open-source databases give your team the control and flexibility to build exactly what you need. No licensing limits, no vendor lock-in, just reliable performance backed by a large community.
Github → Chroma has over 26k GitHub stars and is used in over 90k other open-source codebases on GitHub. It is downloaded over 11M times a month.

Discord → Join the Discord to see what people are building!

Social → Find the greater community on X and YouTube.

Run Chroma OSS → Run Chroma on your own infrastructure with our open-source deployment guides.

[◆] Support
Open-source → Join our 10K person strong Discord community to get fast and expert help from the open-source community.

All plans → Helpful support direct from engineers on the Chroma team

Pro plan → Direct Slack communication for fast support and help designing and iterating your search system.

Enterprise plan → Customized SLAs ensure your team gets 24/7 assistance.

[▲] Research
Our research spans both basic and applied research for search, retrieval, agents, and context engineering.

Context-1
Training a self-editing search agent.

Context Rot
How increasing input tokens impacts LLM performance.

Generative Benchmarking
New methods for evaluating retrieval systems.

Chunking Strategies
Evaluating chunking strategies in retrieval for AI.

Embedding Adapters
Lightweight transforms to boost embedding accuracy.

[■] Updates
Chroma's project is rapidly improving. Here are the latest updates.
Chroma Cloud Sync
Serverless data ingestion for Chroma Cloud.
Mar 2026

Metadata Arrays
Store arrays of strings, numbers, and booleans in metadata.
Feb 2026

Indexing Status
Monitor real-time indexing progress of your collections.
Jan 2026

Read Level
Control read consistency with index-only or full read modes.
Jan 2026

Private Networking
Secure connectivity with AWS PrivateLink support.
Jan 2026

GroupBy
Group and aggregate search results by metadata keys.
Jan 2026

Customer-Managed Encryption Keys
Encrypt your data with your own encryption keys.
Dec 2025

Chroma Web Sync
Automatically crawl, scrape, chunk and embed web pages.
Nov 2025

Sparse Vector Search
First class support for BM25 and SPLADE vectors.
Oct 2025

Introducing Chroma Sync
Automatically chunk, embed, and index GitHub repos.
Oct 2025

wal3: Chroma's Write-Ahead Log
A Write-Ahead Log for Chroma, Built on Object Storage
Sep 2025

Package Search MCP
Query thousands of open-source repos through MCP.
Sep 2025

Collection Forking
Fast duplication of collections with copy-on-write.
Aug 2025

Introducing Chroma Cloud
Chroma Cloud is now generally available.
Aug 2025

Designing a query execution engine
A push-based, morsel-driven execution engine in Rust.
Aug 2025

70% Data Throughput Increase
Performance boost using base64 vector encoding.
Jul 2025

Regex Search Support
Search using regular expressions with new operators.
Jun 2025

JavaScript Client V3
Complete rewrite with reduced bundle size.
Jun 2025

We’re looking for curious people who are dedicated to becoming world-class at their craft to join our team.
See open roles

Get started
Get up and running in 30 seconds or less with $5 in free credits.
Quick Start

Python Python getting started docs →
pip install chromadb

JavaScript / TypeScript JavaScript / TypeScript getting started docs →
npm install chromadb

View full documentation →

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© 2026

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