dstack vs NVIDIA Mission Control
dstack
dstack.ai
NVIDIA Mission Control
nvidia.comSide by side
| Figure | dstack | NVIDIA Mission Control |
|---|---|---|
| Price from | Free | — |
| Free plan | Yes | — |
| Free trial | — | — |
| Open source | — | — |
| Web | Yes | Yes |
| Windows | Yes | — |
| macOS | Yes | — |
| Linux | Yes | Yes |
| API | Yes | — |
| Self-hosted | Yes | Yes |
| Plans published | 2 | 1 |
| Spec score · GPU Cluster Management Software | 8.9 (#1) | 7.2 (#15) |
| Deployment | The server can run on a laptop or another environment with access to the cloud and on-prem clusters being used. | The 2.3 release adds an option for a virtualized control plane and deployment in air-gapped environments. |
| Purpose | dstack is an open-source orchestration layer for AI workloads on heterogeneous accelerators, including GPU clouds, Kubernetes, VMs, and bare-metal clusters. | NVIDIA Mission Control is an integrated AI factory management platform for enterprise AI infrastructure that combines cluster automation and operational best practices in one control plane. |
| Security | The service configuration supports authorization, which is enabled by default for services. | Mission Control offers secure configurations with validated namespace isolation and provides Kubernetes security policy files through its artifact collection. |
| Support | The dstack Sky page directs users with questions or needing help to Discord or to contact the company directly. | NVIDIA directs Mission Control customers to NVIDIA Enterprise Support Services for expert support and guidance. |
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.
NVIDIA Mission Control
NVIDIA Mission Control has no write-up on Specifiction yet; its figures are above.