DVC
Git for data and pipelines. Reproducibility rather than dashboards.
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sovereigntyDVC treats datasets, models and pipelines the way Git treats code: version them, branch them, and reproduce any past state exactly. It stores large files in your own object storage and keeps lightweight pointers in Git, so `git checkout` of an old commit brings the matching data and model with it. Its experiment tracking is a consequence of that design rather than the headline. Apache-2.0, and it addresses the failure most trackers do not — not knowing which data produced a result.
OPEN SOURCEApache-2.0SELF-HOSTLOCAL-FIRST
LicenseApache-2.0
PricingFree, Apache-2.0. You supply the object storage.
Open sourceYes
Self-hostableYes
Local-first dataYes
What it does well
- +Data and models versioned alongside code in Git
- +True reproducibility — check out a commit, get the matching data
- +Storage-agnostic: S3, GCS, Azure, SSH or a local disk
- +Apache-2.0, no server to run
Where it falls short
- −Not a metrics dashboard — different tool for a different problem
- −Git-centric workflow takes adjusting to
- −Large binary handling needs care in the repository
DVC as an alternative to
Where DVC shows up in our comparisons, and how it ranked.
DVC head-to-head
Straight comparisons against the tools people weigh it against.