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Where DataChain fits

AI coding agents are stateless on data. Each session starts from raw bytes; embeddings recompute, classifications recompute, joins recompute, and the work fails to compound. The two-orders-of-magnitude gap between recomputing a dataset (~$100), querying a materialised one (~$1), and reading its summary (~$0.01) is what makes Data Memory worth building. DataChain fits where that gap is real for your team.

When DataChain fits

  • Your data is files in object storage (images, video, documents, sensor data) or rows in a database.
  • An agent is doing real work over that data.
  • You want what the agent produces to outlive the session that produced it.

When it does not fit

  • BI on a curated warehouse with a stable schema: dbt plus a semantic layer.
  • Conversation memory for one user across chat sessions: Letta or Mem0.
  • File-blob versioning for a small ML repo: DVC.

DataChain is the Python-and-files layer; when the work is not Python over files, the tools above sit closer to the shape of the problem.

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