ClearML
ClearML / clear.ml
Open source MLOps platform for experiment tracking, data management, pipeline orchestration, and model serving — providing a free alternative to Weights & Biases and MLflow for ML teams.
Pricing
Free
Free plan
Yes
Category
Developer Tools
Platforms
3
Free plan
Yes
API access
Yes
Open source
Yes
Platforms
3
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What is ClearML?
ClearML is an open source MLOps platform providing comprehensive machine learning operations tooling — experiment tracking, dataset versioning, ML pipeline orchestration, model registry, and model serving — as a single integrated platform that teams can self-host for free.
Experiment tracking records hyperparameters, metrics, model weights, and artifacts from ML training runs automatically (with minimal code changes or without any code changes using auto-magic integration with popular frameworks). Comparing runs across experiments, reproducing results, and understanding what changed between versions is the core MLOps problem ClearML solves.
Data management with ClearML Data versioned dataset tracking connects datasets to the experiments that used them, enabling reproducibility and lineage tracking of ML training data alongside model tracking.
ML pipeline orchestration (ClearML Pipelines) executes multi-step ML workflows — data preparation, feature engineering, training, evaluation, deployment — automatically across distributed compute resources. Pipeline steps are defined as Python code and orchestrated through the ClearML server.
Agent-based compute provides dynamic scaling of ML compute — ClearML Agents on computing nodes pull tasks from queues automatically, enabling auto-scaling of training capacity without managed Kubernetes complexity.
At completely open source with self-hosting, ClearML is attractive for ML teams with privacy requirements, budget constraints, or a preference for open source tooling over commercial alternatives.
How ClearML works
ClearML runs as ml platform software built around code and data workflows. Users typically start with a prompt, upload, or connected data source, and the underlying model handles the heavy lifting before returning a result you can refine or export. It's available on python, cli, and web, with API access for teams that want to embed it into their own products.
Watch ClearML in action
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What makes it worth shortlisting
The capabilities that matter most for teams evaluating ClearML.
Experiment tracking
Records hyperparameters, metrics, model weights, and artifacts from ML training runs with minimal or no code changes using auto-magic framework integration.
ML pipeline orchestration
Executes multi-step ML workflows across distributed compute resources with ClearML Agents pulling tasks from queues, enabling auto-scaling without managed Kubernetes complexity.
Open source self-hosted
Free deployment on own infrastructure keeping all ML data private, with Docker-based setup for self-hosting without cloud dependency.
Best use cases
Who should use it
Pros
- Completely free self-hosted deployment for teams with privacy requirements or budget constraints
- Open source transparency — inspect and modify the platform code for custom needs
- Auto-magic experiment tracking integrates without code changes for common ML frameworks
- Comprehensive platform covering experiment to deployment rather than requiring multiple tools
Cons
- Self-hosted requires DevOps setup and maintenance overhead
- Community support for open source tier — enterprise support requires paid plan
- Less polished UI/UX than commercial alternatives like Weights & Biases
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Is it worth the price?
Open source self-hosted is free. ClearML Cloud managed from $13/month. Enterprise custom.
Model
Open Source
Starting price
Free
Free trial
No
Tools like ClearML
Weights & Biases (covered) is the commercial standard with stronger experiment visualisation. MLflow (Apache) is an open source alternative focused on experiment tracking. Kubeflow provides ML pipelines on Kubernetes. DVC provides data versioning alongside MLflow.
ClearML vs Comet ML
A side-by-side look at the closest alternative in this category.
ClearML
ClearML
Comet ML
Comet ML
Open source self-hosted is free. ClearML Cloud managed from $13/month. Enterprise custom.
Free plan (1 user, limited storage). Team $179/month. Enterprise custom.
Open source self-hosted keeps all data on customer infrastructure. ClearML Cloud subject to ClearML's data handling policy. Enterprise includes data handling agreements.
SOC 2 Type II. ISO 27001. GDPR compliant. On-premise deployment option for data privacy. Enterprise data handling agreements.
Self-hosted deployment keeps all ML experiment data on customer infrastructure. ClearML Cloud transmits experiment data to ClearML's servers — review privacy policy.
Review Comet ML's data handling policy. Experiment data, metrics, and artefacts processed on Comet's infrastructure. On-premise deployment keeps data on customer infrastructure.
- Completely free self-hosted deployment for teams with privacy requirements or budget constraints
- Open source transparency — inspect and modify the platform code for custom needs
- Auto-magic experiment tracking integrates without code changes for common ML frameworks
- Comprehensive platform covering experiment to deployment rather than requiring multiple tools
- Free plan provides meaningful single-user experiment tracking for individual researchers
- Comet Optics LLM evaluation expands scope beyond classical ML tracking to the current LLM era
- Auto-logging with popular ML frameworks reduces instrumentation overhead significantly
- On-premise deployment option addresses data privacy requirements not available on all competitors
- Self-hosted requires DevOps setup and maintenance overhead
- Community support for open source tier — enterprise support requires paid plan
- Less polished UI/UX than commercial alternatives like Weights & Biases
- Weights & Biases has stronger mindshare and ecosystem integrations in the current research community
- $179/month Team tier has a significant jump from the free plan
- LLM evaluation features still maturing compared to dedicated LLMOps tools like Langfuse
Technical & deployment info
Key facts about model providers, platforms, and team support.
Model Provider
ClearML, Community
Platforms
Python, CLI, Web
Deployment
Open Source, SaaS, Self-hosted
Integrations
PyTorch, TensorFlow, Keras, scikit-learn, Hugging Face, AWS, GCP, Azure, Kubernetes, API
Team Collaboration
No
Launch Year
2022
Security & privacy
Compliance signals and data-handling notes as reported by the vendor.
Open source self-hosted keeps all data on customer infrastructure. ClearML Cloud subject to ClearML's data handling policy. Enterprise includes data handling agreements.
Self-hosted deployment keeps all ML experiment data on customer infrastructure. ClearML Cloud transmits experiment data to ClearML's servers — review privacy policy.
What users are saying
Verified reviews from signed-in users, stored in the backend and averaged into this tool's rating.
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Common questions about ClearML
Editorial Verdict
Should you use ClearML?
ClearML is the best open source MLOps platform for ML teams who need private self-hosted experiment tracking and pipeline orchestration without the subscription cost of Weights & Biases or the complexity of building custom MLOps infrastructure.
Last verified July 24, 2026.