--- title: "ome vs openmodelz" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/ome-projects-ome-vs-tensorchord-openmodelz" tools: ["ome-projects-ome", "tensorchord-openmodelz"] --- # ome vs openmodelz *GraphCanon updated Sep 29, 2026* ## Verdict 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; pick openmodelz if openModelZ automates and scales large language model inferences on Kubernetes. [ome](http://ome-projects.github.io/ome/) reports 509 GitHub stars, 97 forks, and 141 open issues, last pushed Sep 20, 2026. [openmodelz](https://docs.open.modelz.ai) has 284 stars, 26 forks, and 23 open issues, last pushed Nov 3, 2023. Figures are from public GitHub metadata via [ome's repository](https://github.com/ome-projects/ome) and [openmodelz's repository](https://github.com/tensorchord/openmodelz). | | [ome](/tools/ome-projects-ome.md) | [openmodelz](/tools/tensorchord-openmodelz.md) | | --- | --- | --- | | Tagline | Kubernetes operator for LLM serving and management | Automate and scale inference of large language models on Kubernetes. | | Stars | 509 | 284 | | Forks | 97 | 26 | | Open issues | 141 | 23 | | Language | Go | Go | | 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. | OpenModelZ automates and scales large language model inferences on Kubernetes. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | Apache-2.0 | | Categories | Inference & Serving | Inference & Serving | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [ome](/tools/ome-projects-ome.md) | [openmodelz](/tools/tensorchord-openmodelz.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Dormant (18%) | | Days since push | 0d | 1060d | | Open issues (now) | 141 | 23 | | Stars delta | +27 (30d) | +2 (30d) | | Open issues delta | +20 (30d) | 0 (30d) | | Full report | [trust report](/tools/ome-projects-ome/trust.md) | [trust report](/tools/tensorchord-openmodelz/trust.md) | ## 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. ## Decision facts: openmodelz - **Adopt for:** OpenModelZ automates and scales large language model inferences on Kubernetes. ## Choose when ### Choose ome if… - Tags unique to ome: gpu-scheduling, kubernetes-operator, llm-inference, model-serving. - If you need robust GPU scheduling alongside LLM serving - More GitHub stars (509 vs 284) - visibility, not fit. ### Choose openmodelz if… - Tags unique to openmodelz: cluster-manager, hacktoberfest, inference, llm. - When you need automatic scaling of large language models based on current load on Kubernetes clusters. - Leaner open-issue backlog (23). ## 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 ## When NOT to use openmodelz - Avoid using if your deployment setup does not include Kubernetes or another cluster management system that OpenModelZ supports. - Do not use this tool if you do not need automatic scaling features, as manual setup might be more straightforward for simpler deployments. ## Common questions ### What is the difference between ome and openmodelz? ome: Kubernetes operator for LLM serving and management. openmodelz: Automate and scale inference of large language models on Kubernetes.. See the comparison table for live GitHub stats and shared categories. ### When should I choose ome over openmodelz? Choose ome over openmodelz when Tags unique to ome: gpu-scheduling, kubernetes-operator, llm-inference, model-serving; If you need robust GPU scheduling alongside LLM serving; More GitHub stars (509 vs 284) - visibility, not fit. ### When should I choose openmodelz over ome? Choose openmodelz over ome when Tags unique to openmodelz: cluster-manager, hacktoberfest, inference, llm; When you need automatic scaling of large language models based on current load on Kubernetes clusters; Leaner open-issue backlog (23). ### 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 ### When should I avoid openmodelz? Avoid using if your deployment setup does not include Kubernetes or another cluster management system that OpenModelZ supports. Do not use this tool if you do not need automatic scaling features, as manual setup might be more straightforward for simpler deployments. ### Is ome or openmodelz more popular on GitHub? ome has more GitHub stars (509 vs 284). Stars measure visibility, not whether either tool fits your constraints. ### Are ome and openmodelz open source? Yes - both are open-source projects on GitHub (ome: Apache-2.0, openmodelz: Apache-2.0). ### Where can I find alternatives to ome or openmodelz? GraphCanon lists graph-backed alternatives at [ome alternatives](/tools/ome-projects-ome/alternatives) and [openmodelz alternatives](/tools/tensorchord-openmodelz/alternatives) ([ome markdown twin](/tools/ome-projects-ome/alternatives.md), [openmodelz markdown twin](/tools/tensorchord-openmodelz/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/ome-projects-ome-vs-tensorchord-openmodelz.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, ome or openmodelz? ome: Very active. openmodelz: Dormant. 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 ome and openmodelz? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [ome trust report](/tools/ome-projects-ome/trust); [openmodelz trust report](/tools/tensorchord-openmodelz/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=ome-projects-ome`](/api/graphcanon/graph?tool=ome-projects-ome) - 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/_