ClearML

ClearML is an MLOps and AI infrastructure platform organised into three layers: an Infrastructure Control Plane, an AI Development Center and a GenAI App Engine. The infrastructure layer is what distinguishes it from experiment tracking tools, because it addresses the resource problem rather than only the record-keeping one.

GPU-as-a-Service is published as a primary use case, in two forms: for enterprises managing their own GPU estate and for cloud service providers reselling capacity. That focus reflects where the actual constraint sits in machine learning work now. Experiment tracking solves a documentation problem, but the thing limiting most teams is access to accelerators, and a platform that schedules a scarce GPU pool fairly across competing teams is solving the expensive problem.

Industry coverage names research labs, financial services, defence, telecommunications, semiconductors and the public sector. Defence and public sector appearing on that list implies support for restricted and air-gapped deployments, which is worth confirming if that applies to you, since most MLOps platforms assume cloud connectivity.

Pricing is published and starts free. The free tier covers teams of up to three and is aimed at individuals, researchers, academia and small teams, including 120GB of free artifact storage, 1.2GB of metric events and 1.2 million API calls per month. The paid tier is $15 per user per month for teams up to ten, aimed at growing AI teams needing enhanced features, with additional usage charged from $0.10.

Publishing the storage, metric and API allowances rather than only the seat price is genuinely useful, because experiment tracking generates far more metric events than teams expect and overage is where the cost of these platforms usually appears unannounced.