DVC

Git-style version control for datasets, ML models, and experiment pipelines.

Free· Free and open source; lakeFS Enterprise available for large-scale deploymentsCoding

Best for

Pick DVC if you want reproducible ML pipelines and dataset versioning that lives in your existing Git repo without adopting a heavyweight MLOps platform.

Skip if

Skip it if you want a hosted, click-through MLOps dashboard or your team is allergic to the command line and Git internals.

DVC (Data Version Control) is an open-source command-line tool that brings Git workflows to machine learning. It tracks datasets, model files, and experiment metadata by storing lightweight pointer files in Git while pushing the heavy binaries to remote storage like S3, GCS, Azure Blob, or any SSH/HTTP target. On top of versioning, it ships a pipeline runner (dvc.yaml), experiment tracker, and metric/plot comparison commands so teams can reproduce a training run from any commit.

It's aimed at ML engineers and data scientists who want reproducibility without adopting a proprietary MLOps platform. The core tool is free and open source under Apache 2.0; the commercial story now runs through lakeFS, which acquired Iterative and pitches an enterprise data-lake version-control product alongside DVC. A first-party VS Code extension surfaces experiments, plots, and dataset diffs inside the editor, and DVC integrates cleanly with CML for CI-driven model training.

Caveats: DVC is a workflow layer, not a hosted service, so you bring your own remote storage and your own compute. Large-binary pulls can be slow over weak networks, and the learning curve compounds with Git for teams new to either tool.

Editor's take

DVC remains the default open-source answer for 'how do I version a 50GB dataset alongside my code?'. Now that it sits inside the lakeFS organization, expect more enterprise polish, but the CLI core is still the right tool for individual researchers and small ML teams who value portability over a SaaS dashboard.

— The AI Tool Bible editorial team