dstack vs Volcano
dstack
dstack.ai
Volcano
volcano.shSide by side
| Figure | dstack | Volcano |
|---|---|---|
| Price from | Free | — |
| Free plan | Yes | — |
| Free trial | — | — |
| Open source | — | — |
| Web | Yes | — |
| Windows | Yes | — |
| macOS | Yes | — |
| Linux | Yes | — |
| API | Yes | Yes |
| Self-hosted | Yes | Yes |
| Plans published | 2 | 0 |
| Spec score · GPU Cluster Management Software | 8.9 (#1) | 6.5 (#19) |
| Deployment | The server can run on a laptop or another environment with access to the cloud and on-prem clusters being used. | Installation options include YAML files, source code, and Helm on an existing Kubernetes cluster. |
| Frameworks | The tasks guide names accelerate, torchrun, Ray, and Spark as distributed frameworks that work with dstack. | It supports frameworks including Spark, TensorFlow, PyTorch, Flink, Argo, MindSpore, PaddlePaddle, and Ray. |
| Purpose | dstack is an open-source orchestration layer for AI workloads on heterogeneous accelerators, including GPU clouds, Kubernetes, VMs, and bare-metal clusters. | Volcano is a cloud native system for high-performance workloads and a container batch scheduling project. |
| What it does | dstack is an open-source orchestration layer for AI workloads across GPU clouds, Kubernetes, VMs, and bare-metal clusters. | Volcano is a cloud-native system for scheduling high-performance workloads on Kubernetes. |
| Workloads | It supports fleets, dev environments, tasks, services, experimental presets, and volumes through YAML configurations. | It schedules Kubernetes workloads and batch jobs for frameworks including Spark, TensorFlow, PyTorch, Flink, Argo, MindSpore, PaddlePaddle, and Ray. |
Plans
In their own words
dstack
dstack is an open-source orchestration layer for AI workloads across GPU clouds, Kubernetes, virtual machines, and bare-metal clusters. YAML configurations define fleets, development environments, tasks, services, presets, and volumes, while dstack handles provisioning and job scheduling, including auto-scaling, port forwarding, and ingress. Supported accelerators include NVIDIA, AMD, TPU, and Tenstorrent. Documented backends include AWS, Azure, GCP, Kubernetes, GPU cloud providers, remote SSH hosts, and experimental Slurm. Tasks can use frameworks such as accelerate, torchrun, Ray, and Spark. Users manage resources through the CLI or HTTP API, and the server can run on a laptop or another environment that can access the clusters in use. Services can publish inference endpoints through gateways with HTTPS, custom domains, auto-scaling, and rate limits. The self-hosted open-source stack is free. Server data and project secrets are stored in plaintext by default unless administrators configure AES-256-GCM encryption. TPU support is limited to single-host instances with up to eight cores. GPU Marketplace usage is prepaid, with prices that vary by GPU and provider and appear in the console before provisioning.