ClearML vs Determined AI
Both are alternatives to Weights & Biases. Here's how they stack up — verified facts, no spin.
Also searched as Determined AI vs ClearML — same comparison, one verdict.
ClearML and Determined AI are closely matched on ownership (90 vs 89) — this one comes down to pricing and to which trade-offs below you can live with.
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.
Determined AI
Distributed training and hyperparameter search, self-hosted.
Determined is a training platform rather than a tracker: it schedules jobs on a GPU cluster, handles distributed training and fault tolerance, runs state-of-the-art hyperparameter search, and tracks the results as a by-product. That is the shape of W&B Sweeps plus the compute orchestration underneath, self-hosted and Apache-2.0. It is the right pick when hyperparameter search across a cluster is the actual requirement rather than logging.
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.
| ClearML | Determined AI | |
|---|---|---|
| Sovereignty ScoreOur transparent 0–100 composite for data ownership and exit cost. | 90 | 89 |
| Open source | Yes | Yes |
| Self-hostable | Yes | Yes |
| Local-first data | Yes | Yes |
| License | Apache-2.0 | Apache-2.0 |
| Pricing | Free self-hosted, Apache-2.0; a hosted tier with paid plans is also offered. | Free and Apache-2.0, self-hosted on your own cluster. |
| 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. | — |
ClearML 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 →
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
Determined AI
Strengths
- +Distributed training and cluster scheduling built in
- +Advanced hyperparameter search including early stopping
- +Fault tolerance and checkpoint management handled for you
- +Apache-2.0
Trade-offs
- −Assumes you have a GPU cluster to schedule onto
- −Overkill if you only need to log runs
- −Smaller community than MLflow's
Which one fits you
The trade-offs above, turned into a decision. Find the line that describes your team.
Choose ClearML
if a lower exit cost matters more to you than any single feature, and broadest scope — tracking, orchestration, data and serving.
Choose Determined AI
if distributed training and cluster scheduling built in.
Neither, yet
if both carry a real cost you should weigh first — heaviest deployment of the group, and assumes you have a GPU cluster to schedule onto. 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.
ClearML vs Determined AI — common questions
Is ClearML a better fit than Determined AI for experiment tracking & ml ops?
It depends on what you are optimising for, and the honest split is this: ClearML scores 90 to Determined AI's 89 on data ownership and exit cost, so it is the safer choice if you care about being able to leave. Determined AI earns its place on a different axis — distributed training and cluster scheduling built in. Neither is a wrong answer for every team; the table above is the actual comparison.
What happens if we want to switch later?
ClearML keeps its data local or in open formats, so leaving is an export rather than a negotiation. Determined AI 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 ClearML or Determined AI?
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 ClearML and Determined AI 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.
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Facts verified 2026-08-11. Licenses and pricing change — spotted something out of date? That's a correction we want.