--- title: "ormb vs ome" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/kleveross-ormb-vs-ome-projects-ome" tools: ["kleveross-ormb", "ome-projects-ome"] --- # ormb vs ome *GraphCanon updated Sep 30, 2026* ## Verdict Pick ormb if oRMB encapsulates machine learning and deep-learning models via OCI artifacts within Docker containers for streamlined model management; 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. [ormb](https://github.com/kleveross/ormb) reports 474 GitHub stars, 61 forks, and 32 open issues, last pushed Jan 26, 2024. [ome](http://ome-projects.github.io/ome/) has 509 stars, 97 forks, and 141 open issues, last pushed Sep 20, 2026. Figures are from public GitHub metadata via [ormb's repository](https://github.com/kleveross/ormb) and [ome's repository](https://github.com/ome-projects/ome). | | [ormb](/tools/kleveross-ormb.md) | [ome](/tools/ome-projects-ome.md) | | --- | --- | --- | | Tagline | Docker for ML/DL Models Based on OCI Artifacts | Kubernetes operator for LLM serving and management | | Stars | 474 | 509 | | Forks | 61 | 97 | | Open issues | 32 | 141 | | Language | Go | Go | | Adopt for | ORMB encapsulates machine learning and deep-learning models via OCI artifacts within Docker containers for streamlined model management. | 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 | Apache-2.0 | Apache-2.0 | | Categories | Inference & Serving, Model Training | Inference & Serving | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [ormb](/tools/kleveross-ormb.md) | [ome](/tools/ome-projects-ome.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Very active (96%) | | Days since push | 978d | 0d | | Open issues (now) | 32 | 141 | | Stars delta | +1 (30d) | +27 (30d) | | Open issues delta | 0 (30d) | +20 (30d) | | Full report | [trust report](/tools/kleveross-ormb/trust.md) | [trust report](/tools/ome-projects-ome/trust.md) | ## Decision facts: ormb - **Adopt for:** ORMB encapsulates machine learning and deep-learning models via OCI artifacts within Docker containers for streamlined model management. ## 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 ormb if… - Tags unique to ormb: docker, machine-learning, model-management, model-versioning. - Also covers Model Training. - If you need sophisticated version control for your ML/DL models through an image registry, ORMB provides this functionality. ### 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 474) - visibility, not fit. ## When NOT to use ormb - Should you seek simple models deployment without extensive version management features, ORMB may introduce unnecessary complexity. - If your project strictly avoids using Docker and OCI artifacts for model handling, then this tool would not be suitable. ## 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 ormb and ome? ormb: Docker for ML/DL Models Based on OCI Artifacts. ome: Kubernetes operator for LLM serving and management. See the comparison table for live GitHub stats and shared categories. ### When should I choose ormb over ome? Choose ormb over ome when Tags unique to ormb: docker, machine-learning, model-management, model-versioning; Also covers Model Training; If you need sophisticated version control for your ML/DL models through an image registry, ORMB provides this functionality. ### When should I choose ome over ormb? Choose ome over ormb 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 474) - visibility, not fit. ### When should I avoid ormb? Should you seek simple models deployment without extensive version management features, ORMB may introduce unnecessary complexity. If your project strictly avoids using Docker and OCI artifacts for model handling, then this tool would not be suitable. ### 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 ormb or ome more popular on GitHub? ome has more GitHub stars (509 vs 474). Stars measure visibility, not whether either tool fits your constraints. ### Are ormb and ome open source? Yes - both are open-source projects on GitHub (ormb: Apache-2.0, ome: Apache-2.0). ### Where can I find alternatives to ormb or ome? GraphCanon lists graph-backed alternatives at [ormb alternatives](/tools/kleveross-ormb/alternatives) and [ome alternatives](/tools/ome-projects-ome/alternatives) ([ormb markdown twin](/tools/kleveross-ormb/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/kleveross-ormb-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, ormb or ome? ormb: Dormant. 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 ormb and ome? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [ormb trust report](/tools/kleveross-ormb/trust); [ome trust report](/tools/ome-projects-ome/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=kleveross-ormb`](/api/graphcanon/graph?tool=kleveross-ormb) - 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/_