aks-cost-optimization

Reduce Azure Kubernetes Service (AKS) spending: pod rightsizing, VPA-driven recommendations, cluster-autoscaler tuning, spot node pools, namespace-level cost visibility, and cost-anomaly detection. WHEN: rightsize pods, VPA recommendations, idle nodes, scale-down, autoscaler profile, spot nodes, cheaper compute, cost add-on, namespace cost breakdown, spending anomaly, 'my AKS bill is too high', 'is my app consuming what it requests', resource requests vs actual usage, over-provisioned workloads, spot vs on-demand, per-namespace or per-team cost allocation, 'who's spending what and why'. DO NOT USE FOR: GPU / inference cost and idle GPU pools (use aks-gpu-inference); autoscaler that is failing to scale up during an incident (use aks-troubleshooting); choosing SKUs at cluster-creation time (use aks-cluster-setup).

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Repositorio: Azure/AKS-Skills
Última actividad en el origen: 17 de agosto de 2026 a las 22:31
Idioma detectado de SKILL.md: inglés
Estrellas: 4
Forks: 1

Opciones de instalación

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npx skills add https://github.com/Azure/AKS-Skills --skill aks-cost-optimization

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SKILL.md

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aks-automatic-readiness

Assess Kubernetes workloads and cluster configuration for AKS Automatic compatibility. Identifies incompatibilities, generates fixes, and guides migration from AKS Standard to AKS Automatic. WHEN: migrate to AKS Automatic, check AKS Automatic readiness, validate manifests for Automatic, assess cluster for Automatic compatibility, fix deployment for Automatic compatibility, identify AKS Automatic migration blockers, is my cluster ready for AKS Automatic. DO NOT USE FOR: creating a brand-new cluster (use aks-cluster-setup); debugging a running cluster (use aks-troubleshooting).

2026-08-26

aks-troubleshooting

Debug and root-cause live Azure Kubernetes Service (AKS) incidents with a read-only, evidence-first investigation and structured report. WHEN: pod crashes or Pending, CrashLoopBackOff, OOMKilled, ImagePullBackOff, node NotReady, DNS failure, ingress 502/503, connectivity timeout, network policy, SNAT exhaustion, upgrade stuck, cordon/drain failure, spot or zone disruption, expired certificate, or 'investigate my AKS cluster'. DO NOT USE FOR: packet capture (use aks-network-capture); GPU or model-serving issues (use aks-gpu-inference); cluster creation or provisioning (use aks-cluster-setup); cost or rightsizing (use aks-cost-optimization); an exact documented AKS error code, nested VM-extension/CSE signature, or message-qualified allocation failure (use aks-known-issues). Bare wrappers or codes without the nested cataloged signature, generic failures without a stable signature, and open-ended incidents stay here.

2026-08-26

aks-gpu-inference

Day-2 operations for GPU and model-inference workloads on Azure Kubernetes Service (AKS): diagnose GPU pods stuck Pending, missing nvidia.com/gpu, CUDA/driver mismatches, model OOM on weight load, GPU vCPU-quota failures, KAITO (AI toolchain operator) Workspaces stuck not-ready, and GPU cost / scale-to-zero / spot eviction. WHEN: GPU pod Pending 'Insufficient nvidia.com/gpu', no nvidia.com/gpu on node, CUDA driver version insufficient, model OOMKilled loading weights, GPU node pool quota exceeded, KAITO Workspace never becomes ready, idle GPU cost, autoscale GPU on DCGM, spot GPU eviction, right-size a GPU SKU for a model. DO NOT USE FOR: initial GPU/AI setup or enablement (use azure-skills airunway-aks-setup); non-GPU pod/node/network incidents (use aks-troubleshooting); non-GPU cost (use aks-cost-optimization).

2026-08-24

Ver todos los de este repositorio

name	aks-cost-optimization
license	MIT
metadata	{"author":"Microsoft","version":"1.0.0"}
description	Reduce Azure Kubernetes Service (AKS) spending: pod rightsizing, VPA-driven recommendations, cluster-autoscaler tuning, spot node pools, namespace-level cost visibility, and cost-anomaly detection. WHEN: rightsize pods, VPA recommendations, idle nodes, scale-down, autoscaler profile, spot nodes, cheaper compute, cost add-on, namespace cost breakdown, spending anomaly, 'my AKS bill is too high', 'is my app consuming what it requests', resource requests vs actual usage, over-provisioned workloads, spot vs on-demand, per-namespace or per-team cost allocation, 'who's spending what and why'. DO NOT USE FOR: GPU / inference cost and idle GPU pools (use aks-gpu-inference); autoscaler that is failing to scale up during an incident (use aks-troubleshooting); choosing SKUs at cluster-creation time (use aks-cluster-setup).