dstack
6.9#2 in gpu cluster management softwareFree planFree planWebAPILinuxmacOS
Visitdstack vs HAMi
What both publish
| Fact | dstack | HAMi |
|---|---|---|
| Accelerators | dstack supports NVIDIA, AMD, TPU, and Tenstorrent accelerators out of the box. | The project lists support for NVIDIA, AWS Neuron, Huawei Ascend, Cambricon, Enflame, Hygon, Iluvatar, Kunlunxin, MetaX, Moore Threads, Vastai, AMD and Biren accelerators. |
| Deployment | The server can run on a laptop or another environment with access to the cloud and on-prem clusters being used. | The quick start installs HAMi with Helm and requires Kubernetes, Helm, kubectl, and installation permissions. |
| Integrations | Listed backend types include AWS, Azure, GCP, Kubernetes, Slurm, Runpod, Nebius, Lambda, and other cloud providers. | The site lists Kubernetes, Volcano, Kueue, Koordinator and KAI Scheduler in its Kubernetes scheduling ecosystem. |
| Purpose | dstack is an open-source orchestration layer for AI workloads on heterogeneous accelerators, including GPU clouds, Kubernetes, VMs, and bare-metal clusters. | HAMi is open-source, cloud-native GPU virtualization middleware for sharing, isolating, and scheduling heterogeneous accelerators on Kubernetes. |
| Support | The documentation directs users to report issues on GitHub and ask questions in the dstack Discord server. | The site links to documentation, tutorials, Discord and the `#hami-dev` Slack channel for community resources. |
| Workloads | It supports fleets, dev environments, tasks, services, experimental presets, and volumes through YAML configurations. | The site identifies LLM, machine-learning, and HPC workloads as use cases. |
dstack plans
- dstack OSSFree
- dstack Sky GPU MarketplacePriced on request
HAMi plans
- HAMiFree
Pick dstack if…
dstack suits teams orchestrating AI workloads across different accelerator and infrastructure environments. It is also suited to users who want to configure workloads with YAML and manage resources through a CLI or HTTP API.
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.