ClearML
7.4#1 in gpu cluster management softwareFrom $15 a monthFree planWebAPILinuxmacOS
VisitClearML vs HAMi
What both publish
| Fact | ClearML | HAMi |
|---|---|---|
| Integrations | The integrations documentation lists PyTorch, TensorFlow, Keras, scikit-learn, XGBoost, LightGBM, Optuna, Hydra, TensorBoard, Matplotlib, and LangChain among supported integrations. | The site lists Kubernetes, Volcano, Kueue, Koordinator and KAI Scheduler in its Kubernetes scheduling ecosystem. |
| Product | ClearML describes its AI infrastructure platform as three layers: Infrastructure Control Plane, AI Development Center, and GenAI App Engine. | HAMi is open-source GPU virtualization middleware that enables sharing, isolation and scheduling of heterogeneous accelerators for AI workloads on Kubernetes. |
| Support | Scale includes private Slack channel support and an SLA, while Enterprise includes white-glove support with a custom SLA and professional services. | The site links to documentation, tutorials, Discord and the `#hami-dev` Slack channel for community resources. |
ClearML plans
- CommunityFree
- Pro$15 a month
- EnterprisePriced on request
- ScalePriced on request
HAMi plans
- HAMiFree
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 HAMi if…
HAMi suits teams running AI, machine-learning or HPC workloads on Kubernetes that need to share accelerators and manage GPU quotas. It is also relevant to operators who want device utilization metrics and scheduling integrations.