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
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
| ClearML | Determined AI | |
|---|---|---|
| Sovereignty Score | 90 | 89 |
| Open source | Yes | Yes |
| Self-hostable | Yes | Yes |
| Local-first | 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. |
ClearML edges it on the Sovereignty Score, but the right pick depends on the trade-offs below.
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
Related alternative guides
Facts verified 2026-08-11. Licenses and pricing change — spotted something out of date? That's a correction we want.