ClearML is an open-source MLOps platform for experiment tracking, data versioning, pipelines, and model deployment. Compare features, pricing, and use cases.
ClearML was founded in 2016 in Tel Aviv, Israel, and built its reputation as one of the more complete open-source MLOps toolkits available, covering experiment tracking, data versioning, pipeline orchestration, and compute-resource management in one project rather than requiring teams to combine several separate tools. The core codebase is open source on GitHub, and the company also sells hosted and enterprise versions for teams that want a managed platform or additional controls.
ClearML is aimed at machine learning engineers and MLOps teams running training and inference workloads, whether on a handful of GPUs or across large multi-cloud clusters. Its open-source, self-hostable nature makes it a common choice for teams with strict data-residency or air-gapped infrastructure requirements.
The platform automatically logs experiments, hyperparameters, metrics, and artifacts with minimal code changes, and maintains a versioned model and dataset repository so results can be reproduced later. Pipelines can be defined, orchestrated, and scheduled directly in ClearML, and an agent-based system queues and executes jobs across whatever compute is available, including auto-scaling across AWS, GCP, and Azure on paid tiers.
Higher tiers add hyperparameter optimization, pipeline triggers, and dashboards for team-wide visibility, while enterprise deployments add Kubernetes and Slurm/PBS integration, fractional GPU allocation, vector database integration for LLM workflows, SSO, and role-based access control for larger or regulated organizations.
ClearML's Community plan is free for up to three users and includes core experiment tracking, data management, and pipeline features with 100GB of artifact storage and 1 million API calls per month. The Pro plan costs $15 per user per month for up to ten users and adds cloud auto-scaling and hyperparameter optimization, with extra storage, metrics, and API usage billed pay-as-you-go beyond the included allotment.
Scale and Enterprise plans are custom-quoted and intended for organizations running dedicated GPU infrastructure, whether in their own VPC, on-premises, or in a hybrid setup, with pricing based on compute scale and the specific enterprise features required rather than a flat per-user rate.
Yes. ClearML's core platform is open source and available on GitHub, and can be self-hosted for free with production-grade experiment tracking, data versioning, pipeline, and orchestration features.
The self-hosted open-source edition is free. The hosted Pro plan costs $15 per user per month for up to 10 users, while Scale and Enterprise plans for larger GPU deployments use custom quoted pricing.
Community is free for up to 3 users with core experiment tracking and pipeline features. Pro adds cloud auto-scaling across AWS, GCP, and Azure, hyperparameter optimization, pipeline triggers, and dashboards for up to 10 users.
Yes. In addition to the free open-source self-hosted edition, ClearML offers Enterprise deployment options including VPC, on-premises, hybrid, and air-gapped installations.
ClearML is used by machine learning engineers, data scientists, and MLOps teams to track experiments, version datasets and models, and orchestrate training and inference pipelines across cloud or on-premises compute.
Common alternatives include Weights & Biases, MLflow, Neptune.ai, and Comet, though ClearML differentiates itself by also including pipeline and compute orchestration in its open-source offering.