MLOps & experiment tracking · Treeverse (lakeFS); formerly stewarded by Iterative

DVC

Open-source, Git-like version control for datasets, models, and ML pipelines rather than a tracker or monitoring tool.

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DVC (Data Version Control) is a versioning and pipeline-definition tool, not an experiment tracker or a monitoring product: it extends Git's commit/branch/diff model to large data files and trained models by storing lightweight pointer files in Git while the actual data lives in a configured remote (S3, GCS, Azure Blob, or a local/network path). It also lets teams define multi-step ML pipelines as code, with DVC tracking which inputs, code version, and parameters produced a given output so runs stay reproducible. DVC runs entirely self-hosted, as a Git extension and CLI installed alongside a project's code — there is no DVC-branded hosted SaaS. Originally released by Iterative in 2017, stewardship of the open-source DVC project passed to lakeFS (Treeverse) in late 2025, while lakeFS itself continues as a separate, larger-scale enterprise data-versioning product.

At a glance

Vendor Treeverse (lakeFS); formerly stewarded by Iterative
Pricing model Open source + paid options
Free tier Yes
Deployment Self-hosted
Open source Yes (Apache-2.0)
Best for Teams wanting Git-style version control and reproducibility for datasets, models, and pipelines.

Pricing

DVC is free, open-source software; any cost comes from the storage remote (cloud or on-prem) you point it at.

Pricing has not been verified yet — see the vendor's site.

Features

  • Git-like versioning for datasets and models
  • Pipeline definition and dependency tracking (DAGs)
  • Reproducible experiment runs tied to code/data versions
  • Storage-agnostic remotes (S3, GCS, Azure, local)
  • Metric and parameter diffing across commits
  • Data registries for sharing datasets across projects

Integrations

Profile last reviewed September 21, 2026

Alternatives

MLflow Open-source experiment tracker and model lifecycle toolkit, governed by the Linux Foundation. Metaflow Open-source Python framework for orchestrating data-science and ML workflows from laptop to production.

DVC in the index now

Terms to know

Data pipeline The automated sequence of steps that moves data from source systems to a destination, transforming it along the way. Data lineage A traceable record of where data came from, how it was transformed, and where it is used downstream. Machine learning Building models that learn patterns from data to make predictions or decisions, rather than following hand-coded rules. MLOps (machine learning operations) The set of practices for deploying, monitoring and maintaining machine learning models reliably in production, extending DevOps to ML systems. Reproducibility The ability of an independent researcher to obtain the same results using the original data, code and methods.

Related guides

How to choose an MLOps & experiment tracking tool "MLOps" covers five different jobs — tracking, monitoring, orchestration, serving, versioning — and most teams need more than one, not the biggest single tool.