Key Takeaways
- ClearML is an AI infrastructure platform for MLOps and GPU orchestration.
- It manages GPU clusters, experiment tracking, and model deployment end to end.
- It spans infrastructure control, an AI development center, and a GenAI app engine.
- Great for teams that need to run AI workloads and share GPUs at scale.
ClearML is an AI infrastructure platform that helps teams take machine-learning and AI workloads from development to production while making the most of their GPU resources. Originally an open-source MLOps tool, it has grown into a broader platform for orchestrating GPU clusters, running experiments, and deploying models — giving IT, researchers, and AI builders a shared, governed way to manage compute and workflows.
What is ClearML?
ClearML is an enterprise AI infrastructure platform that manages GPU resources and streamlines AI workflows from development to production. It originated as an open-source MLOps tool for experiment tracking and has expanded into comprehensive infrastructure management built around three integrated layers: an Infrastructure Control Plane that manages and optimizes GPU clusters across on-premises, cloud, and hybrid environments with multi-tenancy and resource optimization; an AI Development Center providing an IDE-based workbench for model development, training, and testing; and a GenAI App Engine for rapid LLM deployment and management on compute clusters. Key capabilities include fractional GPU allocation, priority-based job scheduling, role-based access control, usage-based billing and chargeback, and Kubernetes and cloud deployment support, with a vendor-, cloud-, and silicon-agnostic architecture. ClearML reports use by 2,100-plus organizations, including major technology companies, and combines an open-source foundation and free tier with paid plans for managed infrastructure and enterprise features.
What it does well
- GPU orchestration: manage and optimize clusters across cloud and on-prem.
- End-to-end: experiment tracking, development, and model deployment.
- Efficient sharing: fractional GPUs, scheduling, and multi-tenancy.
- Open foundation: open-source roots with a free tier to start.
Who it is for
ClearML fits IT and DevOps teams, AI researchers, and enterprises that need to manage GPU infrastructure, share compute across teams, and run machine-learning and generative-AI workloads from experiment to production with proper governance. Its GPU-as-a-service capabilities suit organizations scaling AI initiatives. Individuals or very small projects may find lighter, single-purpose tools sufficient, and adopting an infrastructure platform takes setup, but for teams operationalizing AI and orchestrating GPUs at scale, ClearML is a capable, well-established choice with an open-source foundation.
Things to keep in mind
- It is infrastructure tooling aimed at teams, with a corresponding learning curve.
- Full value comes with real GPU workloads and multiple teams sharing compute.
- Managed infrastructure and enterprise features are paid.
Our verdict
ClearML is a capable AI infrastructure platform, and its evolution from open-source MLOps into full GPU orchestration — spanning an infrastructure control plane, a development center, and a GenAI app engine — makes it a strong backbone for teams running AI at scale. Fractional GPUs, scheduling, and multi-tenancy help organizations share compute efficiently, and its open-source roots lower the barrier to entry. It carries a learning curve and targets teams rather than individuals, but for MLOps and GPU orchestration, ClearML is a well-rounded, proven platform.
Frequently asked questions
What is ClearML?
ClearML is an AI infrastructure platform for MLOps and GPU orchestration, managing GPU clusters, experiment tracking, model development, and deployment from development to production.
Is ClearML open-source?
ClearML originated as an open-source MLOps tool and offers a free tier, with paid plans adding managed infrastructure, GPU orchestration, and enterprise features.
What are ClearML’s main components?
It has three integrated layers: an infrastructure control plane for GPU clusters, an AI development center for building and training models, and a GenAI app engine for LLM deployment.
Who is ClearML for?
It is for IT/DevOps teams, AI researchers, and enterprises that need to orchestrate GPUs, share compute across teams, and run ML and generative-AI workloads at scale.
