ClearML
An open-source MLOps and AI infrastructure platform that combines experiment tracking, dataset versioning, pipelines and model serving with GPU cluster scheduling, quotas and fractional GPUs across cloud and on-premises hardware.
About ClearML
ML teams juggle separate tools for tracking experiments, versioning data and running jobs, while IT teams struggle to share scarce GPUs fairly across projects and clouds.
Who it’s for
- ML teams that want experiment management, datasets, pipelines and a model repository in one self-hostable tool
- Platform and IT teams offering shared GPU clusters to many users with scheduling, quotas and chargeback
When to consider it
Consider ClearML when you want one Apache-2.0 platform for both the ML development workflow and GPU resource management, and you need it to run on your own cloud or on-premises clusters.
Tradeoffs & limitations
- The hosted Community plan is free for up to 3 users with 100 GB artifact storage, 1 GB of metric events and 1M API calls a month; Pro is $15 per user per month plus usage for teams of up to 10 ($0.1 per GB storage, $1 per 100K API calls, $0.04 per application hour beyond included amounts).
- Scale and Enterprise plans, needed for SSO, Kubernetes integration, fractional GPUs, role-based access control and on-premises or air-gapped clusters, are quoted individually; Scale is VPC-only and aimed at 8–48 GPUs.
SOTA overview · Documentation-based assessment · Sources & review method
Updates
No updates shared yet.
Discussion
Newest firstAsk a question or share how you use ClearML.
Keep it helpful. Community rules
Loading discussion…
Sources & review method
Documentation-based assessment · Nov 1, 2026 · Prepared with AI assistance; not a hands-on benchmark.
Checked by SOTA · AI-assisted documentation review. Selection advice is our assessment; verify current requirements for your deployment.
Import history & original evidence
ClearML official website
An open-source MLOps and AI infrastructure platform that combines experiment tracking, dataset versioning, pipelines and model serving with GPU cluster scheduling, quotas and fractional GPUs across cloud and on-premises hardware.
Based on official pages and announcements checked on 2026-11-01. No hands-on test, performance benchmark or popularity ranking is claimed.