dstack vs GPUStack
dstack
dstack.aiGPUStack
gpustack.aiSide by side
| Figure | dstack | GPUStack |
|---|---|---|
| Price from | Free | Free |
| Free plan | Yes | Yes |
| Free trial | — | — |
| Open source | — | — |
| Web | Yes | Yes |
| Windows | Yes | Yes |
| macOS | Yes | Yes |
| Linux | Yes | Yes |
| API | Yes | Yes |
| Self-hosted | Yes | Yes |
| Plans published | 2 | 1 |
| Spec score · GPU Cluster Management Software | 8.9 (#1) | 8.3 (#4) |
| Deployment | The server can run on a laptop or another environment with access to the cloud and on-prem clusters being used. | It supports on-premise, Kubernetes, and multi-cloud GPU deployments, and the maker states that air-gapped deployments are supported. |
| Integrations | Listed backend types include AWS, Azure, GCP, Kubernetes, Slurm, Runpod, Nebius, Lambda, and other cloud providers. | Listed integrations include OpenWebUI, LangChain, n8n, Dify, RAGFlow, Claude Code, OpenClaw, Docker, Podman, Kubernetes, Helm, Prometheus, and Grafana. |
| Security | The service configuration supports authorization, which is enabled by default for services. | The site lists RBAC, multi-tenancy, OIDC/SAML/AD-LDAP SSO, scoped API keys, IP allowlisting, token quotas, and usage analytics. |
| Support | The dstack Sky page directs users with questions or needing help to Discord or to contact the company directly. | The contact page offers personalized demos and architecture, deployment, and scaling guidance, and says replies typically arrive within one business day. |
| What it does | dstack is an open-source orchestration layer for AI workloads across GPU clouds, Kubernetes, VMs, and bare-metal clusters. | GPUStack is an open-source GPU cluster manager for deploying, governing, and scaling AI models across on-premise, cloud, or hybrid infrastructure. |
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.
GPUStack
GPUStack is an open-source GPU cluster manager for deploying, governing, and scaling AI models across on-premise, cloud, or hybrid infrastructure. It provides OpenAI-compatible and Anthropic-compatible inference endpoints, with support for engines such as vLLM, SGLang, llama.cpp, TensorRT-LLM, and MindIE. Users can browse models from Hugging Face and ModelScope or supply local files, with automated compatibility checks. GPUStack can provision GPU instances with lifecycle controls, partitioning, SSH key injection, Jupyter access, and persistent S3 or NFS storage. Deployment options include on-premise, Kubernetes, and multi-cloud, including air-gapped deployments. The maker lists support for accelerators from NVIDIA, AMD, Ascend, T-head, Hygon, MetaX, Moore Threads, Cambricon, and Iluvatar. Security controls include RBAC, multi-tenancy, SSO, scoped API keys, IP allowlisting, token quotas, and usage analytics. GPUStack Community Edition is free. Worker nodes support Linux only; Windows users are directed to consider WSL2, and macOS worker nodes are not supported.