Collection Forking

Instant copy-on-write collection forking in Chroma Cloud.

Forking lets you create a new collection from an existing one instantly, using copy-on-write under the hood. The forked collection initially shares its data with the source and only incurs additional storage for incremental changes you make afterward.

How it works#

• Copy-on-write: Forks share data blocks with the source collection. New writes to either branch allocate new blocks; unchanged data remains shared.
• Instant: Forking a collection of any size completes quickly.
• Isolation: Changes to a fork do not affect the source, and vice versa.

Try it#

• Cloud UI: Open any collection and click the "Fork" button.
• SDKs: Use the fork API from Python or JavaScript.

Examples

source_collection = client.get_collection(name="main-repo-index")

# Create a forked collection. Name must be unique within the database.
forked_collection = source_collection.fork(name="main-repo-index-pr-1234")

# Forked collection is immediately queryable; changes are isolated
forked_collection.add(documents=["new content"], ids=["doc-pr-1"])  # billed as incremental storage

In this notebook you can find a comprehensive demo, where we index a codebase in a Chroma collection, and use forking to efficiently create collections for new branches.

Pricing#

• $0.03 per fork call
• Storage: You only pay for incremental blocks written after the fork (copy-on-write). Unchanged data remains shared across branches.

Quotas and errors#

Chroma limits the number of fork edges in your fork tree. Every time you call "fork", a new edge is created from the parent to the child. The count includes edges created by forks on the root collection and on any of its descendants; see the diagram below. The current default limit is 4,096 edges per tree. If you delete a collection, its edge remains in the tree and still counts.

If you exceed the limit, the request returns a quota error for the NUM_FORKS rule. In that case, create a new collection with a full copy to start a fresh root.

When to use forking#

• Data versioning/checkpointing: Maintain consistent snapshots as your data evolves.
• Git-like workflows: For example, index a branch by forking from its divergence point, then apply the diff to the fork. This saves both write and storage costs compared to re-ingesting the entire dataset.

Notes#

• Your forked collections will belong to the same database as the source collection.