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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

Mediumnot started

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.