AI Runway AKS Setup

End-to-end AI Runway setup on AKS from bare cluster to running model deployment.

- Walks through six sequential steps: cluster verification, controller installation, GPU assessment, inference provider setup, first model deployment, and summary

- Includes cost awareness warnings for GPU node pools and error handling for common deployment failures

- Supports resuming from any step via skip-to-step N argument if setup is partially complete

- Uses only kubectl and make CLI tools; no MCP tools required

AI Runway AKS Setup

This 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.

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.

Prerequisites

This 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.

Quick Reference

Property
Value

Best for
End-to-end AI Runway onboarding on AKS

CLI tools
kubectl, make, curl

MCP tools
None

Related skills
azure-kubernetes (cluster setup), azure-diagnostics (troubleshooting)