ClearML
Track AI experiments, run repeatable pipelines, and manage GPU infrastructure across cloud or on-premises environments.
About ClearML
ClearML is an end-to-end platform for teams that need experiment tracking and compute orchestration in the same system. It connects runs, metrics, models, datasets, artifacts, pipelines, and GPU resources, so an engineering team can inspect how work was produced and then schedule the next job without stitching together several unrelated consoles. The platform spans an Infrastructure Control Plane, an AI Development Center, and a GenAI App Engine. Deployment choices range from an open-source self-hosted edition to managed, VPC, on-premises, air-gapped, multi-cloud, and hybrid setups, depending on the plan. That range makes sense once an ML workflow has outgrown individual notebooks and isolated tracking tools. There is a tradeoff: advanced scheduling, identity controls, enterprise support, and several deployment modes sit in quote-based tiers, while Pro adds usage charges beyond its monthly seat price. Teams should evaluate ClearML around the full operating workflow, including infrastructure ownership, integrations, access controls, and expected artifact or API volume, rather than treating it as a lightweight experiment dashboard.
Best for
Best use cases
A growing ML team needs to compare training runs and retain the metrics, artifacts, models, and source context behind each result.
Competing jobs arrive on a shared GPU cluster, requiring scheduling, utilization monitoring, autoscaling, and allocation controls.
A regulated deployment must keep AI workloads in a VPC, on premises, or inside an air-gapped environment.
Compare before choosing
Compared with a tracking-only tool, ClearML covers compute scheduling and infrastructure control as well as experiment records.
Compared with assembling separate open-source components, ClearML offers a connected workflow but introduces a broader platform to deploy and administer.
Compared with a fixed SaaS service, ClearML offers more deployment choices, while advanced VPC and enterprise configurations require sales-led pricing.
Features
Experiment management
Track runs, compare experiments, inspect metrics, and keep models, datasets, and artifacts connected to the work that produced them.
Infrastructure control
Schedule jobs across cloud or on-premises compute, monitor GPU use, and apply autoscaling or fractional GPU allocation where the selected plan supports it.
Pipeline automation
Turn training code into repeatable pipelines with triggers, task scheduling, hyperparameter optimization, dashboards, and alerts.
Deployment flexibility
Use the open-source self-hosted edition or choose managed, VPC, on-premises, air-gapped, multi-cloud, and hybrid options according to plan.
Pros
- Combines experiment records with GPU scheduling, which reduces handoffs between model developers and infrastructure operators.
- Open-source self-hosting and multiple enterprise deployment modes support teams with strict infrastructure ownership requirements.
- Pro publishes a clear seat price and included usage allowances, making entry-level team costs easier to estimate.
Cons
- Pro costs $15 per user each month and adds metered charges for storage, metric events, API calls, and applications.
- Scale and Enterprise require custom quotes, so larger teams cannot estimate full costs without a sales conversation.
- The broad platform scope creates more setup and administration work than a focused experiment-tracking service.
Pricing Plans
Community
- Open-source self-hosting option
- Experiment tracking, dashboards, and comparisons
- Artifacts, search, metrics, and plots
Pro
- Community features plus cloud autoscaling
- Hyperparameter optimization and pipeline automation
- 120GB artifact storage, 1.2GB metric events, and 1.2M API calls per month included
Scale
- Pro features plus Hyper-Datasets, fine-tuning, and IDE Launcher
- Kubernetes and vector database integration
- Multi-cluster scheduling and fractional GPUs
Enterprise
- Scale features plus custom apps and configuration vault
- Slurm/PBS, LDAP, and role-based access control
- Air-gapped, multi-cloud, or hybrid deployment with custom SLA
* Pricing information is for reference only. Please refer to the latest announcement on ClearML's official website.