ClearML

ClearML is an open-source MLOps platform that automates and manages ML workflows end to end. Its experiment tracking automatically logs code, parameters, scalars, artifacts, plots, and visualizations with a two-line integration (Task.init), and it adds hyperparameter optimization, dataset versioning, pipeline orchestration via PipelineController, and remote execution across local machines, cloud VMs, or Kubernetes through the clearml-agent.

The self-hosted option is genuinely free with no asterisks, which makes it the dark-horse pick for teams that want an open-source full stack without the operational burden of Kubeflow. A managed hosted tier exists with a Pro plan starting around $15/user/month, and the company is valued around $80 million. It is frequently ranked alongside MLflow and Weights & Biases as a leading experiment-tracking option for 2026.

Limitation: the UI is less polished than Weights & Biases and the community is smaller, so teams wanting premium visualizations or a large plugin ecosystem may prefer alternatives. Self-hosting at scale still needs a dedicated platform engineer.

pip install clearml, then Task.init() in your training script; optionally deploy the open-source ClearML Server via Docker.