--- title: "dstack vs ome" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/dstackai-dstack-vs-ome-projects-ome" tools: ["dstackai-dstack", "ome-projects-ome"] --- # dstack vs ome *GraphCanon updated Aug 25, 2026* ## Verdict Pick dstack if vendor-agnostic AI workload orchestration tool supports GPU providers like NVIDIA and AMD across cloud, Kubernetes, and bare metal; pick ome if oME is a Kubernetes operator tailored for LLM serving and management, focusing on tasks such as GPU scheduling and model lifecycle control, compatible with SGLang, vLLM, TensorRT-LLM, and Triton. [dstack](https://dstack.ai/docs) reports 2.2k GitHub stars, 250 forks, and 66 open issues, last pushed Aug 23, 2026. [ome](http://ome-projects.github.io/ome/) has 495 stars, 92 forks, and 127 open issues, last pushed Aug 25, 2026. Figures are from public GitHub metadata via [dstack's repository](https://github.com/dstackai/dstack) and [ome's repository](https://github.com/ome-projects/ome). | | [dstack](/tools/dstackai-dstack.md) | [ome](/tools/ome-projects-ome.md) | | --- | --- | --- | | Tagline | Vendor-agnostic orchestration for AI workloads | Kubernetes operator for LLM serving and management | | Stars | 2,219 | 495 | | Forks | 250 | 92 | | Open issues | 66 | 127 | | Language | Python | Go | | Adopt for | Vendor-agnostic AI workload orchestration tool supports GPU providers like NVIDIA and AMD across cloud, Kubernetes, and bare metal. | OME is a Kubernetes operator tailored for LLM serving and management, focusing on tasks such as GPU scheduling and model lifecycle control, compatible with SGLang, vLLM, TensorRT-LLM, and Triton. | | Persona | - | - | | Runtime | - | - | | License | MPL-2.0 | Apache-2.0 | | Categories | AI Agents, Inference & Serving, Model Training | Inference & Serving | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [dstack](/tools/dstackai-dstack.md) | [ome](/tools/ome-projects-ome.md) | | --- | --- | --- | | Open issues (now) | 66 | 127 | | Stars delta | +27 (30d) | +13 (30d) | | Open issues delta | +5 (30d) | +6 (30d) | | Full report | [trust report](/tools/dstackai-dstack/trust.md) | [trust report](/tools/ome-projects-ome/trust.md) | ## Decision facts: dstack - **Adopt for:** Vendor-agnostic AI workload orchestration tool supports GPU providers like NVIDIA and AMD across cloud, Kubernetes, and bare metal. ## Decision facts: ome - **Adopt for:** OME is a Kubernetes operator tailored for LLM serving and management, focusing on tasks such as GPU scheduling and model lifecycle control, compatible with SGLang, vLLM, TensorRT-LLM, and Triton. ## Choose when ### Choose dstack if… - dstack is primarily Python; ome is Go. - License: dstack is MPL-2.0, ome is Apache-2.0. - Tags unique to dstack: agent-skills, agentic-orchestration, amd, cloud. - Also covers AI Agents, Model Training. - If your project requires support for multiple hardware vendors such as NVIDIA, AMD, TPU, or Tenstorrent ### Choose ome if… - ome is primarily Go; dstack is Python. - License: ome is Apache-2.0, dstack is MPL-2.0. - Tags unique to ome: gpu-scheduling, kubernetes-operator, llm-inference, model-serving. - If you need robust GPU scheduling alongside LLM serving ## When NOT to use dstack - When sticking to single-vendor solutions where tightly integrated proprietary tools are preferred - If the project strictly avoids open-source components with Mozilla Public License (MPL-2.0) ## When NOT to use ome - In environments where a language other than Go for the operator's implementation is preferred - When your infrastructure does not support or utilize Kubernetes for orchestration purposes ## Common questions ### What is the difference between dstack and ome? dstack: Vendor-agnostic orchestration for AI workloads. ome: Kubernetes operator for LLM serving and management. See the comparison table for live GitHub stats and shared categories. ### When should I choose dstack over ome? Choose dstack over ome when dstack is primarily Python; ome is Go; License: dstack is MPL-2.0, ome is Apache-2.0; Tags unique to dstack: agent-skills, agentic-orchestration, amd, cloud; Also covers AI Agents, Model Training; If your project requires support for multiple hardware vendors such as NVIDIA, AMD, TPU, or Tenstorrent. ### When should I choose ome over dstack? Choose ome over dstack when ome is primarily Go; dstack is Python; License: ome is Apache-2.0, dstack is MPL-2.0; Tags unique to ome: gpu-scheduling, kubernetes-operator, llm-inference, model-serving; If you need robust GPU scheduling alongside LLM serving. ### When should I avoid dstack? When sticking to single-vendor solutions where tightly integrated proprietary tools are preferred If the project strictly avoids open-source components with Mozilla Public License (MPL-2.0) ### When should I avoid ome? In environments where a language other than Go for the operator's implementation is preferred When your infrastructure does not support or utilize Kubernetes for orchestration purposes ### Is dstack or ome more popular on GitHub? dstack has more GitHub stars (2,219 vs 495). Stars measure visibility, not whether either tool fits your constraints. ### Are dstack and ome open source? Yes - both are open-source projects on GitHub (dstack: MPL-2.0, ome: Apache-2.0). ### Where can I find alternatives to dstack or ome? GraphCanon lists graph-backed alternatives at [dstack alternatives](/tools/dstackai-dstack/alternatives) and [ome alternatives](/tools/ome-projects-ome/alternatives) ([dstack markdown twin](/tools/dstackai-dstack/alternatives.md), [ome markdown twin](/tools/ome-projects-ome/alternatives.md)), ranked by typed relationship edges rather than popularity votes. ### Is there a machine-readable version of this comparison? Yes. The markdown twin at [this comparison](/compare/dstackai-dstack-vs-ome-projects-ome.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, dstack or ome? dstack: Very active. ome: Very active. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance. ### Where are the full trust reports for dstack and ome? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [dstack trust report](/tools/dstackai-dstack/trust); [ome trust report](/tools/ome-projects-ome/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=dstackai-dstack`](/api/graphcanon/graph?tool=dstackai-dstack) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_