deploy-model

Deploy Model

Scope — read this first. This skill creates model deployments out-of-band via Azure CLI / MCP / portal. For azd-managed Foundry projects (those scaffolded from azd-ai-starter-basic or via azd ai agent init), declare deployments in azure.yaml services.ai-project.deployments[] instead — azd ai agent init writes the entry from the sample manifest and azd provision creates the deployment through Bicep. See foundry-agent/create/create-hosted.md for the Golden Path. Use this skill only for: (a) Foundry projects not managed by an azd project, (b) ad-hoc deployments outside the azd lifecycle.

Unified entry point for all Azure OpenAI model deployment workflows. Analyzes user intent and routes to the appropriate deployment mode.

Quick Reference

Intent Detection

Analyze the user's prompt and route to the correct mode:

User Prompt
    │
    ├─ Simple deployment (no modifiers)
    │  "deploy gpt-4o", "set up a model"
    │  └─> PRESET mode
    │
    ├─ Customization keywords present
    │  "custom settings", "choose version", "select SKU",
    │  "set capacity to X", "configure content filter",
    │  "PTU deployment", "with specific quota"
    │  └─> CUSTOMIZE mode
    │
    ├─ Capacity/availability query
    │  "find where I can deploy", "check capacity",
    │  "which region has X capacity", "best region for 10K TPM",
    │  "where is this model available"
    │  └─> CAPACITY DISCOVERY mode
    │
    └─ Ambiguous (has capacity target + deploy intent)
       "deploy gpt-4o with 10K capacity to best region"
       └─> CAPACITY DISCOVERY first → then PRESET or CUSTOMIZE

Routing Rules

Multi-Mode Chaining

Some prompts require two modes in sequence:

Pattern: Capacity → Deploy When a user specifies a capacity requirement AND wants deployment:

1. Run Capacity Discovery to find regions/projects with sufficient quota
2. Present findings to user
3. Ask: "Would you like to deploy with quick defaults or customize settings?"
4. Route to Preset or Customize based on answer

💡 Tip: If unsure which mode the user wants, default to Preset (quick deployment). Users who want customization will typically use explicit keywords like "custom", "configure", or "with specific settings".

Project Selection (All Modes)

Before any deployment, resolve which project to deploy to. This applies to all modes (preset, customize, and after capacity discovery).

Resolution Order

1. Check PROJECT_RESOURCE_ID env var — if set, use it as the default
2. Check user prompt — if user named a specific project or region, use that
3. If neither — query the user's projects and suggest the current one

Confirmation Step (Required)

Always confirm the target before deploying. Show the user what will be used and give them a chance to change it:

If user picks option 2, show top 5 projects in that region:

⚠️ Never deploy without showing the user which project will be used. This prevents accidental deployments to the wrong resource.

Pre-Deployment Validation (All Modes)

Before presenting any deployment options (SKU, capacity), always validate both of these:

1. Model supports the SKU — query the model catalog to confirm the selected model+version supports the target SKU:

   az cognitiveservices model list --location <region> --subscription <sub-id> -o json

   Filter for the model, extract .model.skus[].name to get supported SKUs.

2. Subscription has available quota — check that the user's subscription has unallocated quota for the SKU+model combination:

   az cognitiveservices usage list --location <region> --subscription <sub-id> -o json

   Match by usage name pattern OpenAI.<SKU>.<model-name> (e.g., OpenAI.GlobalStandard.gpt-4o). Compute available = limit - currentValue.

⚠️ Warning: Only present options that pass both checks. Do NOT show hardcoded SKU lists — always query dynamically. SKUs with 0 available quota should be shown as ❌ informational items, not selectable options.

💡 Quota management: For quota increase requests, usage monitoring, and troubleshooting quota errors, defer to the quota skill instead of duplicating that guidance inline.

Prerequisites

All deployment modes require:

• Azure CLI installed and authenticated (az login)
• Active Azure subscription with deployment permissions
• Azure AI Foundry project resource ID (or agent will help discover it via PROJECT_RESOURCE_ID env var)

Sub-Skills

• preset/SKILL.md — Quick deployment to optimal region with sensible defaults
• customize/SKILL.md — Interactive guided flow with full configuration control
• capacity/SKILL.md — Discover available capacity across regions and projects