ClearML
7.4#1 in gpu cluster management softwareFrom $15 a monthFree planWebAPILinuxmacOS
VisitClearML vs ZenML
What both publish
| Fact | ClearML | ZenML |
|---|---|---|
| Headquarters | Berkeley, California, USA | Munich, Germany |
| Infrastructure | The Infrastructure Control Plane manages GPU resources across on-premises, cloud, and hybrid environments and supports multi-tenant GPU-as-a-Service. | Pipelines can run on Kubernetes, Vertex AI, SageMaker, AzureML, or a laptop without rewriting the code. |
| Integrations | The integrations documentation lists PyTorch, TensorFlow, Keras, scikit-learn, XGBoost, LightGBM, Optuna, Hydra, TensorBoard, Matplotlib, and LangChain among supported integrations. | The integrations directory lists 66 tools, including LangGraph, OpenAI Agents SDK, PyTorch, MLflow, AWS, and Google Cloud Vertex AI Pipelines. |
| Support | Scale includes private Slack channel support and an SLA, while Enterprise includes white-glove support with a custom SLA and professional services. | The Open Source plan includes community support, Scale includes priority support, and Enterprise includes dedicated support with an SLA. |
ClearML plans
- CommunityFree
- Pro$15 a month
- EnterprisePriced on request
- ScalePriced on request
ZenML plans
- Open SourceFree
- Scale$999 a month
- EnterprisePriced on request
Pick ClearML if…
ClearML suits AI and machine-learning teams that need experiment management and model development tools alongside GPU resource management. Its hosting choices may also suit teams with self-hosted, VPC, on-premises, or hybrid requirements.
Pick ZenML if…
The free Open Source plan is described for individuals and small teams; Scale is aimed at teams running ML in production. Enterprise is for organizations operating at scale.