airunway-aks-setup

1# AI Runway AKS Setup
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3This skill walks users from a bare Kubernetes cluster to a running AI model deployment. Follow each step in sequence unless the user provides `skip-to-step N` to resume from a specific phase.
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5> **Cost awareness:** GPU node pools incur significant compute charges (A100-80GB can cost $3–5+/hr). Confirm the user understands cost implications before provisioning GPU resources.
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7## Prerequisites
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9This skill assumes an AKS cluster already exists. If the user does not have a cluster, hand off to the `azure-kubernetes` skill first to provision one (with a GPU node pool unless CPU-only inference is acceptable), then return here.
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11## Quick Reference
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13| Property | Value |
14|----------|-------|
15| Best for | End-to-end AI Runway onboarding on AKS |
16| CLI tools | `kubectl`, `make`, `curl` |
17| MCP tools | None |
18| Related skills | `azure-kubernetes` (cluster setup), `azure-diagnostics` (baseline troubleshooting), optional `aks-gpu-inference` (existing GPU/inference Day-2 incidents only) |
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20## When to Use This Skill
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22Use this skill when the user wants to:
23- Set up AI Runway on an existing AKS cluster from scratch
24- Install the AI Runway controller and CRDs
25- Assess GPU hardware compatibility for model deployment
26- Choose and install an inference provider (KAITO, Dynamo, KubeRay)
27- Deploy their first AI model to AKS via AI Runway
28- Resume a partially-complete AI Runway setup from a specific step
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30## GPU setup versus Day-2 incidents
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