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 and ClearML are closely matched on ownership (94 vs 90) — this one comes down to pricing and to which trade-offs below you can live with.
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
10 points of comparison, every one read from a verified field. Green marks the side that wins a row outright. A dash means we do not hold that fact — never that it is zero.
| DVC | ClearML | |
|---|---|---|
| Sovereignty ScoreOur transparent 0–100 composite for data ownership and exit cost. | 94 | 90 |
| Open source | Yes | Yes |
| Self-hostable | Yes | Yes |
| Local-first data | 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. |
| RAM to run it wellThe figure that actually matters, not the vendor's minimum. | — | 8 GB |
| Realistic running costWhat the box costs each month if you run it yourself. | — | $50–80/mo self-hosted, against per-seat MLOps platform pricing |
| Setup timeHonest first-install estimate, not the marketing quickstart. | — | Half a day |
| Ongoing maintenanceThe part nobody budgets for. | — | Moderate to high. Elasticsearch is the component that will need attention. |
DVC edges it on the Sovereignty Score, but the right pick depends on the trade-offs below.
Weighing both against staying on Weights & Biases? Is Weights & Biases free? What it actually costs →
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
Which one fits you
The trade-offs above, turned into a decision. Find the line that describes your team.
Choose DVC
if a lower exit cost matters more to you than any single feature, and data and models versioned alongside code in Git.
Choose ClearML
if broadest scope — tracking, orchestration, data and serving.
Neither, yet
if both carry a real cost you should weigh first — not a metrics dashboard — different tool for a different problem, and heaviest deployment of the group. If either of those is a dealbreaker for your team, the shortlist is wrong rather than the choice.
What it takes to run these yourself
Real requirements and honest running costs, not the vendor quickstart.
DVC vs ClearML — common questions
Is DVC a better fit than ClearML for experiment tracking & ml ops?
It depends on what you are optimising for, and the honest split is this: DVC scores 94 to ClearML's 90 on data ownership and exit cost, so it is the safer choice if you care about being able to leave. ClearML earns its place on a different axis — broadest scope — tracking, orchestration, data and serving. Neither is a wrong answer for every team; the table above is the actual comparison.
What happens if we want to switch later?
DVC keeps its data local or in open formats, so leaving is an export rather than a negotiation. ClearML is still self-hostable, so the files stay on your server either way — but it is not local-first by design, so check what its export produces before you rely on it.
Can I self-host DVC or ClearML?
Both can be self-hosted. The difference is what it costs you in time rather than whether it is possible — see the setup and maintenance rows above.
Are DVC and ClearML both alternatives to Weights & Biases?
Yes — both appear in our Weights & Biases comparison, which is why they are worth putting side by side. People usually arrive here already having decided to move off Weights & Biases and now choosing between the two replacements, which is a narrower and much easier question.
Related alternative guides
Facts verified 2026-08-11. Licenses and pricing change — spotted something out of date? That's a correction we want.