All skills
microsoft avatar

/deploy-model

@04d245b
by microsoftmicrosoft/skills3.1k stars
351

Unified Azure OpenAI model deployment skill with intelligent intent-based routing. Handles quick preset deployments, fully customized deployments (version/SKU/capacity/RAI policy), and capacity discovery across regions and projects. USE FOR: deploy model, deploy gpt, create deployment, model deployment, deploy openai model, set up model, provision model, find capacity, check model availability, where can I deploy, best region for model, capacity analysis. DO NOT USE FOR: listing existing deployments (use foundry_models_deployments_list MCP tool), deleting deployments, agent creation (use agent/create), project creation (use project/create).

Use this Skill: https://skilld.dev/gh/microsoft/skills/deploy-model

This session only. Nothing lands on disk.

customizereferencescustomize-guides.md

≈872 tokens on demand. Your agent reads this file only when SKILL.md points to it.

Customize Guides — Selection Guides & Advanced Topics

Reference for: models/deploy-model/customize/SKILL.md

Table of Contents: Selection Guides · Advanced Topics

Selection Guides

How to Choose SKU

SKU Best For Cost Availability
GlobalStandard Production, high availability Medium Multi-region
Standard Development, testing Low Single region
ProvisionedManaged High-volume, predictable workloads Fixed (PTU) Reserved capacity
DataZoneStandard Data residency requirements Medium Specific zones

Decision Tree:

Do you need guaranteed throughput?
├─ Yes → ProvisionedManaged (PTU)
└─ No → Do you need high availability?
        ├─ Yes → GlobalStandard
        └─ No → Standard

How to Choose Capacity

For TPM-based SKUs (GlobalStandard, Standard):

Workload Recommended Capacity
Development/Testing 1K - 5K TPM
Small Production 5K - 20K TPM
Medium Production 20K - 100K TPM
Large Production 100K+ TPM

For PTU-based SKUs (ProvisionedManaged):

Use the PTU calculator based on:

  • Input tokens per minute
  • Output tokens per minute
  • Requests per minute

Capacity Planning Tips:

  • Start with recommended capacity
  • Monitor usage and adjust
  • Enable dynamic quota for flexibility
  • Consider spillover for peak loads

How to Choose RAI Policy

Policy Filtering Level Use Case
Microsoft.DefaultV2 Balanced Most applications
Microsoft.Prompt-Shield Enhanced Security-sensitive apps
Custom Configurable Specific requirements

Recommendation: Start with Microsoft.DefaultV2 and adjust based on application needs.


Advanced Topics

PTU (Provisioned Throughput Units) Deployments

What is PTU?

  • Reserved capacity with guaranteed throughput
  • Measured in PTU units, not TPM
  • Fixed cost regardless of usage
  • Best for high-volume, predictable workloads

PTU Calculator:

Estimated PTU = (Input TPM × 0.001) + (Output TPM × 0.002) + (Requests/min × 0.1)

Example:
- Input: 10,000 tokens/min
- Output: 5,000 tokens/min
- Requests: 100/min

PTU = (10,000 × 0.001) + (5,000 × 0.002) + (100 × 0.1)
    = 10 + 10 + 10
    = 30 PTU

PTU Deployment:

az cognitiveservices account deployment create \
  --name <account-name> \
  --resource-group <resource-group> \
  --deployment-name <deployment-name> \
  --model-name <model-name> \
  --model-version <version> \
  --model-format "OpenAI" \
  --sku-name "ProvisionedManaged" \
  --sku-capacity 100  # PTU units

Spillover Configuration

Spillover Workflow:

  1. Primary deployment receives requests
  2. When capacity reached, requests overflow to spillover target
  3. Spillover target must be same model or compatible
  4. Configure via deployment properties

Best Practices:

  • Use spillover for peak load handling
  • Spillover target should have sufficient capacity
  • Monitor both deployments
  • Test failover behavior

Priority Processing

What is Priority Processing?

  • Prioritizes your requests during high load
  • Available for ProvisionedManaged SKU
  • Additional charges apply
  • Ensures consistent performance

When to Use:

  • Mission-critical applications
  • SLA requirements
  • High-concurrency scenarios

Source: SKILL.md on GitHub

No alerts1mo3 checks · Risk SAFE
  • Gen Agent Trust Hub1mo

    This skill provides a unified workflow for Azure OpenAI model deployments, utilizing the Azure CLI for resource management. It incorporates sensible safety measures, including mandatory user confirmation before resource creation and dynamic validation of model availability.

  • Socket1mo

    No alerts

  • Snyk1mo

    Risk: LOW · No issues

Signed by skilld at 04d245b. This ties the file your Agent reads to that commit on GitHub. It does not review the instructions.

Last checked against GitHub 20 hours ago.

Activeupdated 2 months ago
metadata
{
  "author": "Microsoft",
  "version": "1.0.0"
}

README badge

README badge for microsoft/skills/deploy-model