About K8sLLM
K8sLLM is an independent Kubernetes LLM platform guide for senior engineers who need to design, scale, secure, and operate AI workloads on Kubernetes.
The site is written for practical platform decisions. It avoids beginner-only definitions and focuses on operating model, ownership, failure modes, metrics, and tradeoffs that matter in production reviews.
Editorial intent
K8sLLM exists to help platform teams answer concrete questions:
- How should GPU node pools be isolated, scheduled, and scaled?
- When should a team use vLLM directly, KServe, Ray Serve, Triton, or a custom deployment path?
- What does a production RAG platform need beyond a vector database?
- Which signals prove an LLM endpoint is healthy under real user traffic?
- Which security controls should exist before exposing model endpoints?
Source-of-truth policy
Official project documentation is the source anchor for technical claims:
| Area | Preferred source |
|---|---|
| Kubernetes APIs and behavior | Kubernetes documentation |
| GPU scheduling | Kubernetes GPU scheduling docs and NVIDIA GPU Operator docs |
| vLLM serving | vLLM documentation |
| KServe inference resources | KServe documentation |
| Ray Serve and KubeRay | Ray documentation |
| SEO and indexing practices | Google Search Central documentation |
Vendor blogs, benchmark posts, and community examples can support operational context, but they should not override official API behavior, security guidance, or lifecycle documentation.
Review cadence
| Content type | Review trigger |
|---|---|
| Kubernetes core pages | Kubernetes minor-version adoption, API deprecation, or platform baseline change. |
| Security pages | Policy exception, incident review, admission-controller change, or secrets-management change. |
| LLM serving pages | Runtime upgrade, new model family, GPU profile change, or serving abstraction change. |
| RAG pages | Retrieval pipeline change, vector database change, evaluation method change, or data governance update. |
| Labs | New manifest pattern, changed CLI behavior, changed metrics contract, or failed learner validation. |
Content quality bar
Every substantial page should include:
- A clear decision or operating problem.
- Platform ownership boundaries.
- Failure modes.
- Metrics or validation signals.
- Links to related guides, labs, or reference architectures.
- Sources that point to official documentation when available.
The working checklist is maintained in Content Review Checklist. Labs link back to that checklist so new exercises stay useful, measurable, and reviewable.
What K8sLLM is not
K8sLLM is not a managed service, a hosted sandbox, or a product benchmark leaderboard. It is a static learning and architecture guide. The labs are runbooks that readers can adapt to their own clusters.
Popularity roadmap
The first growth goal is organic Google traffic for k8s llm, Kubernetes LLM, LLM on Kubernetes, vLLM Kubernetes, KServe vs Ray Serve, GPU node pool Kubernetes, and RAG on Kubernetes.
The site will prioritize one deep article per week and one new lab every two weeks. Each article should include a decision table, internal links to at least two related pages, and source anchors to official docs.