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Kubernetes LLM Platform Roadmap

A guided path from Kubernetes primitives to production LLM serving, RAG, observability, cost, and readiness.

Module 01

Foundations

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KServe vs Ray Serve Decision LabChoose the serving abstraction by ownership model, CRDs, graph complexity, autoscaling, and rollout needs.

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GPU Node Pool Scheduling LabProve accelerator placement with labels, taints, tolerations, quotas, and unschedulable-pod debugging.

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Production Readiness ChallengeRun a launch review across security, quota, rollout, observability, cost, and ownership before live traffic.

Module 02

Model Serving

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vLLM Kubernetes Deployment LabDesign the deployment contract for vLLM with model cache, readiness, runtime flags, and service exposure.

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vLLM Inference ChallengeDeploy a GPU-backed OpenAI-compatible endpoint and prove scheduling, health, TTFT, queueing, and rollback readiness.

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LLM Rollout and Rollback LabDesign traffic shifting, readiness gates, rollback triggers, and model-version ownership for inference services.

Module 03

RAG Platform

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RAG Retrieval ChallengeOperate ingestion, metadata filters, vector retrieval, answer evaluation, and failure drills for production RAG.

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RAG Retrieval Quality LabMeasure retrieval recall, citation accuracy, tenant filtering, and reranking latency before generation.

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Multi-Tenant LLM Security LabReview tenant routing, namespace boundaries, secrets, NetworkPolicy, prompt logging, and retrieval authorization.

Module 04

Operate and Optimize

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LLM Observability ChallengeBuild the signal model needed to debug user latency, runtime saturation, GPU pressure, traces, logs, and alerts.

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LLM Observability and Cost Dashboard LabCreate a dashboard model that joins user latency, queue wait, GPU pressure, token throughput, and cost signals.

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Inference Cost Model LabCalculate cost per request from input tokens, output tokens, GPU profile, utilization, and cache behavior.