ClearML
What it is: End-to-end MLOps platform for experiment tracking, orchestration, deployment, and model management.
What It Does Best
Complete MLOps suite. Unlike MLflow (tracking only), ClearML handles the full ML lifecycle: experiments, data versioning, pipeline orchestration, model serving, and compute management.
Auto-magical tracking. Add two lines of code and ClearML automatically logs everything: hyperparameters, metrics, models, code, environment, console output, and git info.
Remote execution. Run experiments locally, then queue them to remote workers without code changes. Auto-scales compute resources.
Key Features
Experiment tracking: Auto-logs metrics, hyperparameters, artifacts
Pipeline orchestration: Build and run ML pipelines visually
Remote execution: Queue experiments to worker pools
Data versioning: Track datasets and their lineage
Model serving: Deploy models to production endpoints
Pricing
Free: Self-hosted, unlimited users and experiments
Cloud Free: Up to 100GB storage, 3 users
Pro: $50/user/month (managed cloud, premium support)
Enterprise: Custom pricing (on-prem, SSO, advanced features)
When to Use It
✅ Need end-to-end MLOps, not just tracking
✅ Want to run experiments on remote compute
✅ Team needs to collaborate on ML workflows
✅ Building ML pipelines that run automatically
✅ Need both experiment tracking and model serving
When NOT to Use It
❌ Only need simple experiment logging (MLflow simpler)
❌ Working solo on small projects (overkill)
❌ Already locked into cloud vendor tools (SageMaker, Vertex AI)
❌ Need lightweight tracking with minimal setup
❌ Budget is tight (free tier limited on cloud)
Common Use Cases
Hyperparameter optimization: Track hundreds of experiment runs
Pipeline automation: Schedule and orchestrate ML workflows
Model registry: Version and manage production models
Remote training: Queue GPU jobs to worker pools
Team collaboration: Share experiments and reproduce results
ClearML vs Alternatives
vs MLflow: ClearML more features, MLflow simpler for tracking only
vs Weights & Biases: ClearML self-hostable, W&B better UI/UX
vs Kubeflow: ClearML easier setup, Kubeflow more Kubernetes-native
Unique Strengths
Full MLOps platform: Tracking, orchestration, serving in one tool
Auto-tracking: Minimal code changes to log everything
Self-hostable: Free unlimited use on your infrastructure
Remote execution: Run local code on remote compute seamlessly
Bottom line: Best choice when you need a complete MLOps platform beyond just experiment tracking. Perfect for teams that want to self-host and need orchestration, not just logging. More complex than MLflow but far more capable for production ML workflows.