Pricing
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Overview
Chroma is open-source data infrastructure for AI applications, centered on storing and retrieving information for embedding-based and language-model workflows. Its ecosystem supports vector search and is designed to make it straightforward for developers to add retrieval and memory capabilities to AI applications.
The project exposes a deliberately small core API while its hosted Chroma Cloud service adds serverless vector, hybrid, and full-text search. This allows developers to start with a simple local or application-oriented interface and move toward managed infrastructure when scale, operational simplicity, or additional retrieval modes become important.
Chroma is widely applicable to retrieval-augmented generation, semantic search, agent memory, recommendation-style retrieval, and other systems that need to find relevant context from large collections of data. It is particularly attractive to AI developers who want a purpose-built retrieval layer without adopting a traditional database workflow first.
README
Chroma - the open-source data infrastructure for AI.
pip install chromadb # python client
# for javascript, npm install chromadb!
# for client-server mode, chroma run --path /chroma_db_path
Chroma Cloud
Our hosted service, Chroma Cloud, powers serverless vector, hybrid, and full-text search. It's extremely fast, cost-effective, scalable and painless. Create a DB and try it out in under 30 seconds with $5 of free credits.
API
The core API is only 4 functions (run our 💡 Google Colab):
import chromadb
# setup Chroma in-memory, for easy prototyping. Can add persistence easily!
client = chromadb.Client()
# Create collection. get_collection, get_or_create_collection, delete_collection also available!
collection = client.create_collection("all-my-documents")
# Add docs to the collection. Can also update and delete. Row-based API coming soon!
collection.add(
documents=["This is document1", "This is document2"], # we handle tokenization, embedding, and indexing automatically. You can skip that and add your own embeddings as well
metadatas=[{"source": "notion"}, {"source": "google-docs"}], # filter on these!
ids=["doc1", "doc2"], # unique for each doc
)
# Query/search 2 most similar results. You can also .get by id
results = collection.query(
query_texts=["This is a query document"],
n_results=2,
# where={"metadata_field": "is_equal_to_this"}, # optional filter
# where_document={"$contains":"search_string"} # optional filter
)
Learn about all features on our Docs
Get involved
Chroma is a rapidly developing project. We welcome PR contributors and ideas for how to improve the project.
- Join the conversation on Discord -
#contributingchannel - Review the 🛣️ Roadmap and contribute your ideas
- Grab an issue and open a PR -
Good first issue tag - Read our contributing guide
Release Cadence
We currently release new tagged versions of the pypi and npm packages on Mondays. Hotfixes go out at any time during the week.
License
Releases
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