ClearML / clear.ml
Open source MLOps platform providing experiment tracking, data versioning, model registry, and pipeline orchestration for ML teams wanting comprehensive MLOps without vendor lock-in.
Pricing
Free
Free plan
Yes
Category
Developer Tools
Platforms
4
Free plan
Yes
API access
Yes
Open source
Yes
Platforms
4
ClearML is an open source MLOps platform providing the full ML lifecycle management stack — experiment tracking, data versioning, model registry, pipeline orchestration, and GPU task scheduling. As a comprehensive open source MLOps suite, ClearML competes with both Weights & Biases (commercial) and MLflow (open source) by providing more complete operational capabilities.
Experiment Tracking automatically captures training run metadata — metrics, hyperparameters, model snapshots, console output, and system metrics — with a few lines of Python code. All training runs are searchable, comparable, and reproducible from the ClearML interface.
Data Versioning provides dataset lineage tracking — storing dataset versions, documenting how datasets were processed, and linking training runs to the specific data versions used. Complete data provenance is essential for regulated industries and ML reproducibility.
Pipelines enables building and orchestrating multi-step ML workflows — data preprocessing, training, evaluation, and deployment steps connected in reproducible pipelines with dependency management and automatic execution.
Task Scheduler and Agent enable distributed ML task execution — queuing training jobs, allocating GPU resources, and monitoring execution across on-premise GPU clusters and cloud GPU instances. For teams with significant GPU infrastructure, ClearML provides orchestration without vendor lock-in.
ClearML AI runs as ml platform software built around 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 AI.
Captures training metrics, hyperparameters, and artifacts automatically — making every run searchable, comparable, and reproducible from the ClearML interface.
Builds multi-step ML workflows connecting data preprocessing, training, evaluation, and deployment steps in reproducible pipelines with dependency management.
Queues and executes ML training jobs across on-premise and cloud GPU resources — distributed training coordination without cloud vendor lock-in for teams with significant GPU infrastructure.
Open source free (self-hosted). ClearML Pro $299/month. Enterprise custom. SaaS cloud option available.
Model
Open Source
Starting price
Free
Free trial
No
Weights & Biases (rank 653) is the most adopted ML tracking platform. MLflow (covered) provides open source tracking. Neptune.ai (rank 668) provides tracking with generous free tier. Kubeflow provides Kubernetes-native ML pipeline orchestration.
A side-by-side look at the closest alternative in this category.
Key facts about model providers, platforms, and team support.
Model Provider
ClearML
Platforms
Python, CLI, Web, API
Deployment
Open Source, SaaS, Self-hosted
Integrations
PyTorch, TensorFlow, Keras, scikit-learn, 50+ frameworks, Kubernetes, API
Team Collaboration
Yes
Launch Year
2022
Compliance signals and data-handling notes as reported by the vendor.
Apache 2.0 open source licence. ClearML Pro SOC 2 compliant. GDPR compliant. Self-hosted keeps all data on customer infrastructure.
Self-hosted ClearML keeps all ML metadata, models, and data on customer infrastructure — strongest privacy for sensitive ML work. ClearML Pro processes data on managed infrastructure.
Editorial Verdict
ClearML is the best open source MLOps platform for ML teams wanting comprehensive lifecycle management — experiment tracking, data versioning, pipeline orchestration, and GPU scheduling — without vendor lock-in.
Last verified July 24, 2026.
Open source architecture means teams can self-host ClearML on their own infrastructure — keeping all ML metadata, models, and data on customer-controlled infrastructure without cloud service dependency.
Open source free (self-hosted). ClearML Pro $299/month. Enterprise custom. SaaS cloud option available.
Open source self-hosted is free. ClearML Cloud managed from $13/month. Enterprise custom.
Apache 2.0 open source licence. ClearML Pro SOC 2 compliant. GDPR compliant. Self-hosted keeps all data on customer infrastructure.
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 ClearML keeps all ML metadata, models, and data on customer infrastructure — strongest privacy for sensitive ML work. ClearML Pro processes data on managed infrastructure.
Self-hosted deployment keeps all ML experiment data on customer infrastructure. ClearML Cloud transmits experiment data to ClearML's servers — review privacy policy.
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