ClearML is an open-source MLOps and LLMOps platform that bundles experiment management, data versioning, pipelines, orchestration, scheduling and model serving into one system. It aims to automate the repetitive CI/CD plumbing around training and deploying models. ClearML has earned about 6,900 stars on GitHub, is Apache 2.0 licensed, and is actively developed in 2026.
Most ML teams lose time to scattered experiment logs, untracked datasets and hand-run training jobs. ClearML attacks the whole lifecycle: add two lines of code and every experiment’s hyperparameters, metrics, artifacts and environment are logged automatically; datasets are versioned and cached; pipelines chain tasks into reproducible workflows; and the ClearML Agent executes queued tasks on your own machines or Kubernetes, scaling training and serving without manual babysitting. The same platform covers newer LLMOps needs such as tracking LLM fine-tunes and evaluations. A web UI ties it together for team-wide visibility, and everything can be deployed on-premise, which matters for organizations with strict data-residency rules. The core is open source and self-hostable, while the vendor also sells hosted and enterprise editions with advanced governance, SSO and support for teams that would rather not run the server themselves.
Freemium. The ClearML open-source stack is free to self-host under Apache 2.0; the company also offers paid hosted and Pro/Enterprise plans with managed infrastructure, advanced security and support.
ML and platform teams who want one system for experiments, data, pipelines and deployment โ from individual researchers tracking training runs to enterprises standardizing MLOps across many teams.