MLOps & experiment tracking · ClearML (formerly Allegro AI)

ClearML

Open-source experiment tracker that also bundles pipeline orchestration, dataset versioning, and model deployment.

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ClearML is an open-source MLOps platform whose core function is experiment tracking: it automatically captures metrics, hyperparameters, code, and artifacts from training runs with minimal code changes, placing it in the same bucket as MLflow, W&B, Comet, and neptune.ai. Beyond tracking, it bundles orchestration (queued and scheduled pipelines), dataset versioning, and model serving/deployment in one platform, so it also overlaps with orchestration tools like Kubeflow and Metaflow. The open-source core (ClearML Server) is self-hostable for free, and ClearML also offers a hosted community server plus paid Pro and Enterprise tiers that add larger usage allowances, GPU-hour billing for managed compute, Kubernetes integration, SSO, and on-premises or VPC deployment for larger teams.

At a glance

Vendor ClearML (formerly Allegro AI)
Pricing model Free tier + paid plans
Free tier Yes
Deployment Cloud, Self-hosted
Open source Yes (Apache-2.0)
Best for Teams wanting a single open-source platform that combines experiment tracking with pipeline orchestration and deployment.

Pricing

A free hosted Community tier and a fully self-hosted open-source server are both available; Pro adds per-user fees plus usage-based storage/compute, and Scale/Enterprise are custom-quoted for larger or VPC/on-prem deployments.

Plan Price Notes
Community Free Up to 3 team members; 100GB artifact storage, 1GB metric events, 1M API calls/month
Pro $15/user/month + usage Up to 10 team members; 120GB storage, 1.2GB metric events, 1.2M API calls included; extra usage billed per GB/MB/call and $0.04/hr per application
Scale / Enterprise Custom (VPC or on-prem) For 8-48+ GPU organizations; adds Hyper-Datasets, fine-tuning, Kubernetes integration, SSO, RBAC, and white-glove support

Prices read from the vendor's own page on September 21, 2026. Vendors change prices; check the source before you budget.

Features

  • Automatic experiment tracking with minimal code changes
  • Pipeline orchestration and job scheduling
  • Dataset versioning (Hyper-Datasets)
  • Model registry and one-click deployment/serving
  • GPU/compute orchestration across on-prem and cloud
  • Self-hosted open-source server
  • Kubernetes integration for enterprise scale

Integrations

Profile last reviewed September 21, 2026

Alternatives

MLflow Open-source experiment tracker and model lifecycle toolkit, governed by the Linux Foundation. Weights & Biases Hosted experiment tracker and AI evaluation platform for logging training runs, metrics, and model lineage. Kubeflow Open-source, Kubernetes-native toolkit for orchestrating ML pipelines, notebooks, and training jobs. Comet Hosted experiment-tracking platform that also offers a separate LLM observability product (Opik).

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Terms to know

Machine learning Building models that learn patterns from data to make predictions or decisions, rather than following hand-coded rules. Data pipeline The automated sequence of steps that moves data from source systems to a destination, transforming it along the way. Data lineage A traceable record of where data came from, how it was transformed, and where it is used downstream. MLOps (machine learning operations) The set of practices for deploying, monitoring and maintaining machine learning models reliably in production, extending DevOps to ML systems. Workflow orchestration Scheduling, sequencing and monitoring interdependent data tasks so each step runs only after the ones it depends on succeed.

Related guides

How to choose an MLOps & experiment tracking tool "MLOps" covers five different jobs — tracking, monitoring, orchestration, serving, versioning — and most teams need more than one, not the biggest single tool.