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
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.
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.
The capabilities that matter most for teams evaluating ClearML.
Records hyperparameters, metrics, model weights, and artifacts from ML training runs with minimal or no code changes using auto-magic framework integration.
Executes multi-step ML workflows across distributed compute resources with ClearML Agents pulling tasks from queues, enabling auto-scaling without managed Kubernetes complexity.
Free deployment on own infrastructure keeping all ML data private, with Docker-based setup for self-hosting without cloud dependency.
Open source self-hosted is free. ClearML Cloud managed from $13/month. Enterprise custom.
Model
Open Source
Starting price
Free
Free trial
No
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.
A side-by-side look at the closest alternative in this category.
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
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.
Editorial Verdict
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.
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.
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