az-scout-plugin-aoai-ptu-advisor 2026.3.31

Azure OpenAI PTU cost advisor plugin for az-scout — compare PAYGO, PTU, and hybrid deployment strategies

az-scout-plugin-aoai-ptu-advisor

Azure OpenAI PTU Cost Advisor — an az-scout plugin that compares PAYGO, PTU (Provisioned Throughput), and Hybrid deployment cost strategies for Azure OpenAI models.

What it does

Given your workload characteristics (average TPM, burst percentiles, usage hours), the plugin:

• Estimates monthly costs for PAYGO, PTU on-demand, PTU reserved, and hybrid (PTU base + PAYGO spillover)
• Sizes PTU capacity with correct minimum and increment rounding per model and deployment type
• Detects burst patterns and recommends hybrid when steady base + bursty overflow is cheaper
• Computes break-even TPM thresholds between PAYGO and PTU
• Fetches live pricing from the Azure Retail Prices API with fallback to catalog defaults
• Supports price overrides for enterprise/negotiated rates

Inspiration

This plugin is inspired by the public project azureptucalc by Ricardo Martins. The functional goals are the same — compare Azure OpenAI deployment cost strategies — but the implementation is completely native to az-scout:

• No React — vanilla JavaScript frontend
• No Vercel proxy — direct Azure Retail Prices API calls from the Python backend
• No external dependencies — no database, no KQL, no cache layer
• MCP tools — AI agents can call the calculator directly
• Plugin architecture — auto-discovered by az-scout at startup

Installation

# Install the plugin (az-scout must be installed first)
uv pip install az-scout-plugin-aoai-ptu-advisor

# Or for development
cd az-scout-plugin-aoai-ptu-advisor
uv sync --group dev
uv pip install -e .

# Start az-scout — the plugin is auto-discovered
az-scout web

API Routes

All routes are under /plugins/aoai-ptu-advisor/.

GET /models

Returns supported Azure OpenAI models with PTU specifications.

GET /pricing?region=eastus&model=gpt-4o&deployment_type=Global

Returns resolved PAYGO and PTU pricing for a model/region/deployment type.

POST /analyze

Main analysis endpoint. Request body:

{
  "region": "eastus",
  "model": "gpt-4o",
  "deployment_type": "Global",
  "average_tpm": 50000,
  "p95_tpm": 80000,
  "p99_tpm": 120000,
  "input_ratio": 0.5,
  "hours_per_day": 24,
  "days_per_month": 30,
  "reserved_ptu": false,
  "spillover_enabled": true,
  "paygo_overrides": {
    "input_per_1m": 2.50,
    "output_per_1m": 10.00
  }
}

Response includes: cost estimates for each strategy, PTU sizing, break-even analysis, recommendation with rationale, warnings, and assumptions.

POST /analyze-from-timeseries

Accepts raw TPM time-series data (JSON array of {timestamp, tpm} objects), computes percentiles automatically, and runs the analysis.

{
  "region": "eastus",
  "model": "gpt-4o",
  "datapoints": [
    {"timestamp": "2025-01-01T00:00:00Z", "tpm": 50000},
    {"timestamp": "2025-01-01T01:00:00Z", "tpm": 120000},
    {"timestamp": "2025-01-01T02:00:00Z", "tpm": 30000}
  ]
}

MCP Tools

Available in the az-scout AI chat:

Tool	Description
list_aoai_models	List supported models with PTU specs
get_aoai_pricing	Get resolved pricing for a model/region
analyze_aoai_ptu_cost	Full cost comparison analysis

Supported Models

Model catalog is fetched dynamically from the Microsoft PTU onboarding documentation (cached 24h, static fallback on failure). Currently includes GPT-5.x, GPT-4.1, GPT-4o, o-series, DeepSeek, and Llama models.

Azure Auto-fill

The plugin can populate workload inputs directly from your Azure environment:

1. Select your subscription and Azure OpenAI resource
2. Click "Auto-fill from Azure"
3. The plugin queries Azure Monitor Logs (KQL) and the ARM model capacity API
4. Form fields are populated with live metrics (avg TPM, P99 TPM, recommended PTU)

Azure routes

Route	Method	Description
/azure/openai-resources	GET	List Azure OpenAI resources in a subscription
/azure/kql-autofill	POST	Query Azure Monitor Logs for TPM/PTU metrics
/azure/model-capacity	POST	Check available PTU capacity (preview API)
/azure/autofill-all	POST	Orchestrate KQL + capacity in one call

Required Azure permissions

• Reader on the subscription (for resource discovery)
• Log Analytics Reader on the Log Analytics workspace (for KQL queries)
• Reader on the Cognitive Services account (for model capacity API)

Prerequisites for KQL autofill

Azure Monitor metrics must be exported to a Log Analytics workspace via diagnostic settings on the Azure OpenAI resource. If not configured, the plugin will return a clear warning.

Model capacity API

The capacity API (Microsoft.CognitiveServices/locations/{location}/modelCapacities) is a preview API (2024-04-01-preview). It returns location-based capacity data. The deployment type (Global/DataZone/Regional) is used as a UI filter over SKU names.

Limitations

• Pricing accuracy: Live prices from the Azure Retail Prices API may not reflect negotiated enterprise rates. Use price overrides for accurate estimates.
• No persistent storage: All calculations are stateless — no database or cache.
• Throughput-per-PTU values: Fetched dynamically from Microsoft documentation; may change as models evolve.
• KQL autofill: Requires diagnostic settings configured on the Azure OpenAI resource to export metrics to Log Analytics.
• Capacity API: Preview API — results may be incomplete or change without notice.

Quality Checks

uv run ruff check src/ tests/
uv run ruff format --check src/ tests/
uv run mypy src/
uv run pytest

License

MIT

Disclaimer

This tool is not affiliated with Microsoft. Pricing, throughput, and capacity information are indicative and not a guarantee. PTU throughput-per-unit values are based on published documentation and may change. Always validate with actual Azure pricing and your account team for enterprise rates.