Comparison
LLMKube vs ome
Verdict
Pick LLMKube if lLMKube is a Kubernetes operator for deploying and managing self-hosted large language model inference services on various GPU architectures, including NVIDIA CUDA, AMD Vulkan, and Apple Silicon Metal. It supports runtim; 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.
Markdown twin · LLMKube alternatives · ome alternatives
GraphCanon updated Sep 20, 2026
7views this month
Trust & integrity
| Signal | LLMKube | ome |
|---|---|---|
| Maintenance | Very active (0d since push) As of Sep 18, 2026 · github_public_v1 | Very active (0d since push) As of Sep 20, 2026 · github_public_v1 |
| Provenance | Not a fork · Organization account As of Sep 18, 2026 · github_public_v1 | Not a fork · Organization account As of Sep 20, 2026 · github_public_v1 |
| OSV dependency advisories | Published findings As of Sep 18, 2026 · osv@v1 | No lockfile (source not queried) As of Jul 11, 2026 · osv@v1 |
| deps.dev advisories | Not queried deps.dev@v1 | Not queried deps.dev@v1 |
| OpenSSF Scorecard | Not queried openssf-scorecard@v1 | Not queried openssf-scorecard@v1 |
Tagline
- LLMKube
- Kubernetes operator for self-hosted LLM inference across heterogeneous GPU fleets
- ome
- Kubernetes operator for LLM serving and management
Stars
- LLMKube
- 210
- ome
- 509
Forks
- LLMKube
- 33
- ome
- 97
Open issues
- LLMKube
- 98
- ome
- 141
Language
- LLMKube
- Go
- ome
- Go
Adopt for
- LLMKube
- LLMKube is a Kubernetes operator for deploying and managing self-hosted large language model inference services on various GPU architectures, including NVIDIA CUDA, AMD Vulkan, and Apple Silicon Metal. It supports runtim
- ome
- 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
- LLMKube
- -
- ome
- -
Runtime
- LLMKube
- -
- ome
- -
License
- LLMKube
- Apache-2.0
- ome
- Apache-2.0
Last pushed
- LLMKube
- Sep 17, 2026
- ome
- Sep 20, 2026
Categories
- LLMKube
- Inference & Serving
- ome
- Inference & Serving
Trust and health
Open issues (now)
- LLMKube
- 98
- ome
- 141
Open issues delta
- LLMKube
- +21 (30d)
- ome
- +20 (30d)
OSV dependency advisories
- LLMKube
- Published findings
- ome
- No lockfile (source not queried)
Full report
- LLMKube
- Trust report
- ome
- Trust report
Choose LLMKube if…
- Pricing: Free and open-source under the Apache-2.0 license..
- Requirements: Requires a Kubernetes cluster for installation and operation.; Supports deployment on a variety of GPU architectures, including NVIDIA CUDA, AMD Vulkan, and Apple Silicon Metal..
- Tags unique to LLMKube: ai, apple-silicon, autoscaling, edge-computing.
- LLMKube ships Docker support for self-hosted deployment.
- When you need to deploy and manage self-hosted large language model inference services across a variety of GPU architectures, including NVIDIA CUDA, AMD Vulkan, and Apple Silicon Metal.
When NOT to use LLMKube
- If your infrastructure does not include a Kubernetes cluster, as LLMKube is designed to operate within a Kubernetes environment.
- If you are looking for a solution that does not support a heterogeneous mix of GPU architectures, LLMKube is specifically designed to work with NVIDIA CUDA, AMD Vulkan, and Apple Silicon Metal.
- If you do not require or prefer not to use the specific runtimes supported by LLMKube, such as llama.cpp, vLLM, TGI, and mlx-server.
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 210) - visibility, not fit.
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
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (defilantech/LLMKube) · observed Sep 20, 2026
- GitHub forks (defilantech/LLMKube) · observed Sep 20, 2026
- Last push (defilantech/LLMKube) · observed Sep 17, 2026
- License file (Apache-2.0) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Sep 18, 2026
- Trust scan (lockfile / OSV) · observed Sep 18, 2026
- GitHub stars (ome-projects/ome) · observed Sep 20, 2026
- GitHub forks (ome-projects/ome) · observed Sep 20, 2026
- Last push (ome-projects/ome) · observed Sep 20, 2026
- License file (Apache-2.0) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Jul 16, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: LLMKube 210 · ome 509 (synced Sep 20, 2026).
Common questions
- What is the difference between LLMKube and ome?
- LLMKube: Kubernetes operator for self-hosted LLM inference across heterogeneous GPU fleets. ome: Kubernetes operator for LLM serving and management. See the comparison table for live GitHub stats and shared categories.
- When should I choose LLMKube over ome?
- Choose LLMKube over ome when Pricing: Free and open-source under the Apache-2.0 license.; Requirements: Requires a Kubernetes cluster for installation and operation.; Supports deployment on a variety of GPU architectures, including NVIDIA CUDA, AMD Vulkan, and Apple Silicon Metal.; Tags unique to LLMKube: ai, apple-silicon, autoscaling, edge-computing; LLMKube ships Docker support for self-hosted deployment; When you need to deploy and manage self-hosted large language model inference services across a variety of GPU architectures, including NVIDIA CUDA, AMD Vulkan, and Apple Silicon Metal.
- When should I choose ome over LLMKube?
- Choose ome over LLMKube 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 210) - visibility, not fit.
- When should I avoid LLMKube?
- If your infrastructure does not include a Kubernetes cluster, as LLMKube is designed to operate within a Kubernetes environment. If you are looking for a solution that does not support a heterogeneous mix of GPU architectures, LLMKube is specifically designed to work with NVIDIA CUDA, AMD Vulkan, and Apple Silicon Metal. If you do not require or prefer not to use the specific runtimes supported by LLMKube, such as llama.cpp, vLLM, TGI, and mlx-server.
- 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 LLMKube or ome more popular on GitHub?
- ome has more GitHub stars (509 vs 210). Stars measure visibility, not whether either tool fits your constraints.
- Are LLMKube and ome open source?
- Yes - both are open-source projects on GitHub (LLMKube: Apache-2.0, ome: Apache-2.0).
- Where can I find alternatives to LLMKube or ome?
- GraphCanon lists graph-backed alternatives at LLMKube alternatives and ome alternatives (LLMKube markdown twin, ome markdown twin), 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 mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers.
- Which is better maintained, LLMKube or ome?
- LLMKube: 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 LLMKube and ome?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: LLMKube trust report; ome trust report.