Customize

by microsoft354361d83247MITListed Oct 8, 2026Updated Oct 8, 2026

Interactive guided deployment flow for Azure OpenAI models with full customization control. Step-by-step selection of model version, SKU (GlobalStandard/Standard/ProvisionedManaged), capacity, RAI policy (content filter), and advanced options (dynamic quota, priority processing, spillover). USE FOR: custom deployment, customize model deployment, choose version, select SKU, set capacity, configure content filter, RAI policy, deployment options, detailed deployment, advanced deployment, PTU deployment, provisioned throughput. DO NOT USE FOR: quick deployment to optimal region (use preset).

FeaturedInstructions onlyDevOps & Cloud

AI-generated overview

Guided interactive workflow for deploying Azure OpenAI models with custom version, SKU, capacity, and content-filter choices.

What it does: This skill walks an agent and user through a step-by-step Azure OpenAI model deployment, covering authentication checks, project verification, model and version selection, SKU choice, capacity configuration, RAI policy selection, advanced options, upgrade policy, naming, review, and deployment monitoring. It produces a configured Azure OpenAI deployment and reports its status and endpoint. It also documents error handling and cross-region fallback when capacity is unavailable.
When to use it: Use it when precise control over an Azure OpenAI deployment is needed, such as choosing a specific model version, SKU, capacity, content filter, or provisioned throughput. It is not intended for quick deployment to an optimal region, which the skill says should use the preset skill instead.
Requirements: An Azure subscription with Cognitive Services Contributor or Owner role, a Microsoft Foundry project resource ID, and the Azure CLI installed and authenticated via az login. Azure CLI commands are run; optional MCP tools are mentioned. The skill ships no scripts, only instructions and reference documents.

Customize Model Deployment

Interactive guided workflow for deploying Azure OpenAI models with full customization control over version, SKU, capacity, content filtering, and advanced options.

Quick Reference

Property	Description
Flow	Interactive step-by-step guided deployment
Customization	Version, SKU, Capacity, RAI Policy, Advanced Options
SKU Support	GlobalStandard, Standard, ProvisionedManaged, DataZoneStandard
Best For	Precise control over deployment configuration
Authentication	Azure CLI (`az login`)
Tools	Azure CLI, MCP tools (optional)

When to Use This Skill

Use this skill when you need precise control over deployment configuration:

✅ Choose specific model version (not just latest)
✅ Select deployment SKU (GlobalStandard vs Standard vs PTU)
✅ Set exact capacity within available range
✅ Configure content filtering (RAI policy selection)
✅ Enable advanced features (dynamic quota, priority processing, spillover)
✅ PTU deployments (Provisioned Throughput Units)

Alternative: Use preset for quick deployment to the best available region with automatic configuration.

Comparison: customize vs preset

Feature	customize	preset
Focus	Full customization control	Optimal region selection
Version Selection	User chooses from available	Uses latest automatically
SKU Selection	User chooses (GlobalStandard/Standard/PTU)	GlobalStandard only
Capacity	User specifies exact value	Auto-calculated (50% of available)
RAI Policy	User selects from options	Default policy only
Region	Current region first, falls back to all regions if no capacity	Checks capacity across all regions upfront
Use Case	Precise deployment requirements	Quick deployment to best region

Prerequisites

Azure subscription with Cognitive Services Contributor or Owner role
Microsoft Foundry project resource ID (format: /subscriptions/{sub}/resourceGroups/{rg}/providers/Microsoft.CognitiveServices/accounts/{account}/projects/{project})
Azure CLI installed and authenticated (az login)
Optional: Set PROJECT_RESOURCE_ID environment variable

Workflow Overview

Complete Flow (14 Phases)

1. Verify Authentication2. Get Project Resource ID3. Verify Project Exists4. Get Model Name (if not provided)5. List Model Versions → User Selects6. List SKUs for Version → User Selects7. Get Capacity Range → User Configures   7b. If no capacity: Cross-Region Fallback → Query all regions → User selects region/project8. List RAI Policies → User Selects9. Configure Advanced Options (if applicable)10. Configure Version Upgrade Policy11. Generate Deployment Name12. Review Configuration13. Execute Deployment & Monitor

1. Verify Authentication2. Get Project Resource ID3. Verify Project Exists4. Get Model Name (if not provided)5. List Model Versions → User Selects6. List SKUs for Version → User Selects7. Get Capacity Range → User Configures   7b. If no capacity: Cross-Region Fallback → Query all regions → User selects region/project8. List RAI Policies → User Selects9. Configure Advanced Options (if applicable)10. Configure Version Upgrade Policy11. Generate Deployment Name12. Review Configuration13. Execute Deployment & Monitor

Fast Path (Defaults)

If user accepts all defaults (latest version, GlobalStandard SKU, recommended capacity, default RAI policy, standard upgrade policy), deployment completes in ~5 interactions.

Phase Summaries

⚠️ MUST READ: Before executing any phase, load references/customize-workflow.md [blocked] for the full scripts and implementation details. The summaries below describe what each phase does — the reference file contains the how (CLI commands, quota patterns, capacity formulas, cross-region fallback logic).

Phase	Action	Key Details
1. Verify Auth	Check `az account show`; prompt `az login` if needed	Verify correct subscription is active
2. Get Project ID	Read `PROJECT_RESOURCE_ID` env var or prompt user	ARM resource ID format required
3. Verify Project	Parse resource ID, call `az cognitiveservices account show`	Extracts subscription, RG, account, project, region
4. Get Model	List models via `az cognitiveservices account list-models`	User selects from available or enters custom name
5. Select Version	Query versions for chosen model	Recommend latest; user picks from list
6. Select SKU	Query model catalog + subscription quota, show only deployable SKUs	⚠️ Never hardcode SKU lists — always query live data
7. Configure Capacity	Query capacity API, validate min/max/step, user enters value	Cross-region fallback if no capacity in current region
8. Select RAI Policy	Present content filter options	Default: `Microsoft.DefaultV2`
9. Advanced Options	Dynamic quota (GlobalStandard), priority processing (PTU), spillover	SKU-dependent availability
10. Upgrade Policy	Choose: OnceNewDefaultVersionAvailable / OnceCurrentVersionExpired / NoAutoUpgrade	Default: auto-upgrade on new default
11. Deployment Name	Auto-generate unique name, allow custom override	Validates format: `^[\w.-]{2,64}$`
12. Review	Display full config summary, confirm before proceeding	User approves or cancels
13. Deploy & Monitor	`az cognitiveservices account deployment create`, poll status	Timeout after 5 min; show endpoint + portal link

Error Handling

Common Issues and Resolutions

Error	Cause	Resolution
Model not found	Invalid model name	List available models with `az cognitiveservices account list-models`
Version not available	Version not supported for SKU	Select different version or SKU
Insufficient quota	Capacity > available quota	Skill auto-searches all regions; fails only if no region has quota
SKU not supported	SKU not available in region	Cross-region fallback searches other regions automatically
Capacity out of range	Invalid capacity value	PREVENTED: Skill validates min/max/step at input (Phase 7)
Deployment name exists	Name conflict	Auto-incremented name generation
Authentication failed	Not logged in	Run `az login`
Permission denied	Insufficient permissions	Assign Cognitive Services Contributor role
Capacity query fails	API/permissions/network error	DEPLOYMENT BLOCKED: Will not proceed without valid quota data

Troubleshooting Commands

bash

# Check deployment statusaz cognitiveservices account deployment show --name <account> --resource-group <rg> --deployment-name <name>
# List all deploymentsaz cognitiveservices account deployment list --name <account> --resource-group <rg> -o table
# Check quota usageaz cognitiveservices usage list --name <account> --resource-group <rg>
# Delete failed deploymentaz cognitiveservices account deployment delete --name <account> --resource-group <rg> --deployment-name <name>

# Check deployment statusaz cognitiveservices account deployment show --name <account> --resource-group <rg> --deployment-name <name>
# List all deploymentsaz cognitiveservices account deployment list --name <account> --resource-group <rg> -o table
# Check quota usageaz cognitiveservices usage list --name <account> --resource-group <rg>
# Delete failed deploymentaz cognitiveservices account deployment delete --name <account> --resource-group <rg> --deployment-name <name>

Selection Guides & Advanced Topics

For SKU comparison tables, PTU sizing formulas, and advanced option details, load references/customize-guides.md [blocked].

SKU selection: GlobalStandard (production/HA) → Standard (dev/test) → ProvisionedManaged (high-volume/guaranteed throughput) → DataZoneStandard (data residency).

Capacity: TPM-based SKUs range from 1K (dev) to 100K+ (large production). PTU-based use formula: (Input TPM × 0.001) + (Output TPM × 0.002) + (Requests/min × 0.1).

Advanced options: Dynamic quota (GlobalStandard only), priority processing (PTU only, extra cost), spillover (overflow to backup deployment).

Related Skills

preset - Quick deployment to best region with automatic configuration
microsoft-foundry - Parent skill for all Microsoft Foundry operations
quota — For quota viewing, increase requests, and troubleshooting quota errors, defer to this skill instead of duplicating guidance
rbac - Manage permissions and access control

Notes

Set PROJECT_RESOURCE_ID environment variable to skip prompt
Not all SKUs available in all regions; capacity varies by subscription/region/model
Custom RAI policies can be configured in Azure Portal
Automatic version upgrades occur during maintenance windows
Use Azure Monitor and Application Insights for production deployments

Source and attribution

Source:microsoft/skillsin.github/plugins/azure-skills/skills/microsoft-foundry/models/deploy-model/customizeat commit354361d

License: MIT

Content belongs to its original authors. SourceWeft indexes it from a public repository.

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Customize

Customize Model Deployment

Quick Reference

When to Use This Skill

Comparison: customize vs preset

Prerequisites

Workflow Overview

Complete Flow (14 Phases)

Fast Path (Defaults)

Phase Summaries

Error Handling

Common Issues and Resolutions

Troubleshooting Commands

Selection Guides & Advanced Topics

Related Skills

Notes

Source and attribution

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