MLflow vs ClearML
Both are alternatives to Weights & Biases. Here's how they stack up — verified facts, no spin.
Also searched as ClearML vs MLflow — same comparison, one verdict.
MLflow and ClearML are closely matched on ownership (93 vs 90) — this one comes down to pricing and to which trade-offs below you can live with.
MLflow
TOP PICKThe open standard. Tracking, registry, projects and deployment in one.
MLflow is the most widely adopted open ML platform, and its scope is why: experiment tracking, a model registry with stage transitions, reproducible project packaging and deployment tooling, all Apache-2.0. It runs as a local file store for one person or as a server with a database and object store for a team, integrates with essentially every framework, and is supported natively by every major cloud — so it is the option least likely to become a dead end.
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.
| MLflow | ClearML | |
|---|---|---|
| Sovereignty ScoreOur transparent 0–100 composite for data ownership and exit cost. | 93 | 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. Managed versions are sold by the clouds if you want one. | 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. | 4 GB server | 8 GB |
| Realistic running costWhat the box costs each month if you run it yourself. | $24/mo server plus object storage, against Weights & Biases at roughly $50 per user per month | $50–80/mo self-hosted, against per-seat MLOps platform pricing |
| Setup timeHonest first-install estimate, not the marketing quickstart. | 2 hours for a team-grade install | Half a day |
| Ongoing maintenanceThe part nobody budgets for. | Moderate. Artifact storage growth is the recurring cost. | Moderate to high. Elasticsearch is the component that will need attention. |
MLflow is Macrostack's recommended Weights & Biases alternative, so it's our pick here.
Weighing both against staying on Weights & Biases? Is Weights & Biases free? What it actually costs →
MLflow
Strengths
- +Covers tracking, registry, packaging and deployment — not just tracking
- +The de facto standard; integrated with every major framework and cloud
- +Scales from a local directory to a full server deployment
- +Apache-2.0 with unlimited seats
Trade-offs
- −UI is functional rather than pleasant, and slows on very large run counts
- −Team deployment means running a server, a database and object storage
- −No built-in sweep orchestration comparable to W&B's
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 MLflow
if a lower exit cost matters more to you than any single feature, and covers tracking, registry, packaging and deployment — not just tracking.
Choose ClearML
if broadest scope — tracking, orchestration, data and serving.
Neither, yet
if both carry a real cost you should weigh first — uI is functional rather than pleasant, and slows on very large run counts, 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.
MLflow vs ClearML — common questions
Is MLflow a better fit than ClearML for experiment tracking & ml ops?
It depends on what you are optimising for, and the honest split is this: MLflow scores 93 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?
MLflow 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 MLflow 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 MLflow 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.