DVC vs ClearML
Both are alternatives to Weights & Biases. Here's how they stack up — verified facts, no spin.
Also searched as ClearML vs DVC — same comparison, one verdict.
DVC
Git for data and pipelines. Reproducibility rather than dashboards.
DVC 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.
ClearML
The most complete open platform — tracking, orchestration and data together.
ClearML goes furthest in scope of the open options: experiment tracking, pipeline orchestration, remote task execution on a compute cluster, dataset versioning and model serving, in one Apache-2.0 platform. Its automatic-logging is unusually thorough, capturing arguments, environment and outputs with almost no instrumentation. If you are replacing several tools rather than one, this covers the most ground, at the cost of being the heaviest to deploy.
Side by side
| DVC | ClearML | |
|---|---|---|
| Sovereignty Score | 94 | 90 |
| Open source | Yes | Yes |
| Self-hostable | Yes | Yes |
| Local-first | Yes | Yes |
| License | Apache-2.0 | Apache-2.0 |
| Pricing | Free, Apache-2.0. You supply the object storage. | Free self-hosted, Apache-2.0; a hosted tier with paid plans is also offered. |
DVC edges it on the Sovereignty Score, but the right pick depends on the trade-offs below.
DVC
Strengths
- +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
Trade-offs
- −Not a metrics dashboard — different tool for a different problem
- −Git-centric workflow takes adjusting to
- −Large binary handling needs care in the repository
ClearML
Strengths
- +Broadest scope — tracking, orchestration, data and serving
- +Automatic logging captures almost everything without instrumentation
- +Remote execution on your own compute cluster
- +Apache-2.0 self-hosted with no seat limit
Trade-offs
- −Heaviest deployment of the group
- −Breadth means more concepts to learn before it is useful
- −Some conveniences are nudged toward the hosted tier
Related alternative guides
Facts verified 2026-08-11. Licenses and pricing change — spotted something out of date? That's a correction we want.