Datachain

Use Case

One-Click Reproducibility for Every Dataset

Lineage, versioning, and authorship are not features. They are structural properties of how DataChain stores results.

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Trusted by startups and Fortune 500 companies

  • DeGould
  • Kibsi
  • Hugging Face
  • Mantis
  • Motorway
  • Inlab
  • Pieces
  • Sicara
  • UBS
  • SumerSports
  • UK Hydrographic Office
  • XP Inc.
  • Cyclica
  • Aicon
  • Billie
  • Papercup

Every result comes with its history

DataChain automatically records input versions, source code, parameters, author, and time. No separate metadata system, manual logs, or forgotten experiments. Its context layer pairs with Claude Code, Cursor, and Codex so people can inspect how results were produced. Evaluated under Claude Code at DL4C @ ICML 2026, every saved result carries its schema, version, and lineage on disk—ready for oversight and audit

From Saved Results to Reproducible Work

Automatic lineage

Provenance built into every save

Dataset DB captures provenance on every .save(). It follows from running the chain, without manual declarations or a separate catalog that drifts out of date.

  • Dependencies: parent dataset versions and storage URIs for every input file.
  • Source code: the full script that produced the dataset, stored verbatim.
  • Author: the person or service account that ran the chain.
  • Creation time: the exact timestamp when the result was saved.

Immutable versions

Names organize. Versions audit.

Each dataset has a durable name and an immutable version. Keep work organized while preserving a stable record across teammates and time.

  • Re-running a chain appends a new version. Prior versions never change.
  • Compare schemas, row counts, or column values without re-running the producing chain.
  • Pin downstream chains to a specific upstream version for deterministic reproduction.

Reconstruction

Recreate the result in one click

Query the full chain—parents, code, parameters, and environment—from its lineage. Re-run it to create an identical new version for audit or debugging.

  • Explore the lineage graph in Studio, back to source storage URIs and the exact script.
  • Run against original inputs or a newer upstream version, using delta updates only for changes.
  • Compare versions to pinpoint regressions or track changes in the document corpus.

Add the missing data context layer to your object storage

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