Chroma website, open-source search infrastructure for AI, ChromaDB, Chroma Cloud, vector search, full-text search, sparse vectors, metadata filtering, RAG, SDKs, and WHOIS domain data

Chroma

Chroma is an open-source AI search and retrieval database website and platform for storing embeddings, searching vectors and text, and building retrieval layers for AI applications.

Core purpose

Chroma stores embeddings, metadata, and documents so AI applications can retrieve useful context with vector, sparse, full-text, regex, and metadata search.

Product shape

The ecosystem includes the open-source Chroma database, SDKs, documentation, Chroma Cloud, and hosted search infrastructure for AI applications.

Domain registered

March 23, 2022

The official Chroma wordmark used as the brand image for the AI search infrastructure website page.View official Chroma wordmark

What Chroma is

Chroma official site presents Chroma as open-source search infrastructure for AI. Its documentation describes Chroma as a retrieval system for storing embeddings with metadata, searching with dense and sparse vectors, filtering by metadata, and retrieving across text, images, and other data used by AI applications.

Search infrastructure for AI

Chroma sits in the data layer around models. Instead of asking a language model to answer from memory alone, an application can put documents, chunks, embeddings, and metadata into Chroma, retrieve the most relevant items, and pass that context to the model. That makes it useful for retrieval-augmented generation, semantic search, internal assistants, and product search experiences.

Vector, text, and metadata search

The Chroma website emphasizes vector, full-text, regex, and metadata search. That mix matters because retrieval is rarely just one similarity score. Some queries need semantic matching, some need exact words or patterns, and many need filters such as source, customer, project, date, document type, or permission boundary. Chroma gives developers a retrieval interface that can combine those signals.

Local development and cloud

Chroma is known for being easy to start locally through its SDKs, which helps developers prototype RAG workflows without running a separate production cluster first. Chroma Cloud extends that idea into a hosted platform with serverless search infrastructure. The practical choice depends on the project: local Chroma can be enough for notebooks and small apps, while cloud infrastructure fits shared and production workloads.

Embeddings and documents

Chroma stores embeddings along with the documents and metadata that make retrieved results useful. Documentation pages describe embedding functions, document storage, filters, and retrieval operations. This is important because applications usually need to show sources, enforce permissions, deduplicate chunks, and debug why a model received a particular piece of context.

Open-source project

The main GitHub repository describes Chroma as search infrastructure for AI and provides the open-source Chroma database. Developers commonly install the Python package as chromadb and use it through framework integrations, custom RAG pipelines, or direct client calls. The open-source project gives teams a way to inspect, self-host, and extend the retrieval layer rather than treating it only as a black-box service.

Who uses Chroma

Chroma is used by AI engineers, application developers, data scientists, startup teams, researchers, educators, and product teams building retrieval-heavy AI tools. Common users build document question answering, chat-with-data prototypes, semantic search, support assistants, internal knowledge bases, local AI experiments, multimodal retrieval, and production RAG systems that later move into hosted infrastructure.

Strengths and cautions

Chroma is useful when a team wants a developer-friendly retrieval database that can start locally and grow toward cloud search infrastructure. It still needs careful chunking, embedding choice, metadata design, source permissions, and evaluation. A retrieval database can make context available, but it cannot make weak documents, stale data, or unsafe access rules reliable by itself.

Why it matters

Chroma matters because AI applications increasingly depend on retrieval systems that are easy to prototype and realistic to operate. The model call is only one piece; teams also need to store knowledge, search it, filter it, cite it, and revise it as the data changes. Chroma gives developers a focused path for building that search layer around AI products.

WHOIS domain data

Data pulled: May 24, 2026View current WHOIS record

Domain: trychroma.com
IP address: 76.76.21.21
Registrar: Squarespace Domains II LLC
Registrar IANA ID: 895
WHOIS server: whois.squarespace.domains
Referral URL: http://domains2.squarespace.com
Created: March 23, 2022
Updated: March 8, 2026
WHOIS database updated: May 24, 2026
Expires: March 23, 2027
Nameservers: ns-cloud-d1.googledomains.com (216.239.32.109); ns-cloud-d2.googledomains.com (216.239.34.109); ns-cloud-d3.googledomains.com (216.239.36.109); ns-cloud-d4.googledomains.com (216.239.38.109)
Domain status: clientDeleteProhibited; clientTransferProhibited
DNSSEC: signedDelegation
DNSSEC DS data: 50620 8 2 AB826ACD5899B321A2735FE8FA4666D6BBBD5980938FF87F0229DBD1E79191CC
Contact privacy: Registrant name, address, phone, fax, and email are redacted for privacy; visible contact data shows CA, US and a Squarespace WHOIS contact form.

Key concepts

EmbeddingA vector representation of content used for semantic retrieval.
CollectionA grouping of records, embeddings, documents, and metadata inside Chroma.
Metadata filterA structured condition used to narrow retrieved results by fields such as source, date, or tenant.
Sparse searchRetrieval that can use keyword-like sparse vector signals alongside dense vector search.

Common workflows

Install Chroma locally and add documents, embeddings, IDs, and metadata through an SDK.
Retrieve relevant records for a RAG pipeline before sending context to a language model.
Combine vector, full-text, regex, sparse, or metadata search depending on the query.
Move from local development to Chroma Cloud when a team needs hosted, shared, or production infrastructure.

Common misconceptions

Chroma is not a foundation model; it is a database and search layer used around models.
A local vector store is not automatically production-ready without access control, monitoring, backups, and evaluation.
Semantic search does not guarantee factual answers unless retrieved sources are relevant, current, and safe to expose.

Open questions

How will local-first AI search tools balance simplicity with production governance and scale?
Which retrieval signals will matter most as vector, full-text, sparse, and regex search converge in AI databases?
How should teams evaluate retrieval quality continuously as documents, embeddings, and user behavior change?