---
id: post/demiliani-com/12186
type: post
title: Using Azure AI Foundry Model Router in your AI projects.
summary: Azure AI Foundry Model Router intelligently routes LLM requests to the most suitable model based on query complexity, cost, and performance requirements. It achieves up to 60% cost savings by automatically selecting smaller models for simple tasks and larger models for complex reasoning, packaged as a single deployment that simplifies integration with standard APIs.
tier: community
language: en
tags:
  - azure ai
  - model router
  - llm routing
  - cost optimization
  - ai orchestration
  - api integration
  - business central
  - copilot
system: copilot
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    url: https://demiliani.com/2025/07/29/using-azure-ai-foundry-model-router-in-your-ai-projects/
    title: Using Azure AI Foundry Model Router in your AI projects.
    date: "2025-07-29"
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  - kind: blog
    url: https://demiliani.com/2025/07/29/using-azure-ai-foundry-model-router-in-your-ai-projects/
    title: Using Azure AI Foundry Model Router in your AI projects.
    date: "2025-07-29"
    commit: null
    t: null
    quote: It seems that the AL Copilot SDK is not able to use the Model Router endpoint.
  - kind: blog
    url: https://demiliani.com/2025/07/29/using-azure-ai-foundry-model-router-in-your-ai-projects/
    title: Using Azure AI Foundry Model Router in your AI projects.
    date: "2025-07-29"
    commit: null
    t: null
    quote: The router also supports seamless scalability, adapting to varying workloads in real-time.
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post_id: "12186"
source_id: demiliani-com
source_name: Stefano Demiliani
url: https://demiliani.com/2025/07/29/using-azure-ai-foundry-model-router-in-your-ai-projects/
published_at: "2025-07-29T07:45:34.000Z"
author: Stefano Demiliani
full_text: false
words: 1219
quotes:
  - text: It seems that the AL Copilot SDK is not able to use the Model Router endpoint.
    why_it_matters: Important limitation for Business Central developers considering Model Router integration within AL code
  - text: The router also supports seamless scalability, adapting to varying workloads in real-time.
    why_it_matters: Highlights the dynamic adaptation capability that makes Model Router suitable for production workloads with variable demand
code_objects_mentioned: []
systems:
  - copilot
  - integration
  - development
versions_mentioned:
  - "2025-05-19"
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  frame_url: https://demiliani.com/2025/07/29/using-azure-ai-foundry-model-router-in-your-ai-projects/embed/
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---

# Using Azure AI Foundry Model Router in your AI projects.

[Read the post](https://demiliani.com/2025/07/29/using-azure-ai-foundry-model-router-in-your-ai-projects/) · Stefano Demiliani (Stefano Demiliani, MVP) · 2025-07-29 · 1219 words · tier community · reviewed (checked by Opus)

> Azure AI Foundry Model Router intelligently routes LLM requests to the most suitable model based on query complexity, cost, and performance requirements. It achieves up to 60% cost savings by automatically selecting smaller models for simple tasks and larger models for complex reasoning, packaged as a single deployment that simplifies integration with standard APIs.

## Key points

- Model Router acts as a meta-orchestrator that evaluates prompts in real-time and routes them to appropriate underlying models like gpt-4.1-nano, gpt-4.1-mini, gpt-4.1, or o4-mini
- Achieves significant cost reductions by routing simpler queries to smaller, cheaper models while maintaining accuracy
- Integrates via standard Chat Completions API with a single endpoint, decoupling application logic from specific AI models
- Context window is limited by the smallest underlying model, which may cause failures if prompts exceed that model's capacity
- AL Copilot SDK currently does not support Model Router endpoints, requiring alternative approaches like custom AI layers for Business Central integrations

## Quotes

- "It seems that the AL Copilot SDK is not able to use the Model Router endpoint." (Important limitation for Business Central developers considering Model Router integration within AL code)
- "The router also supports seamless scalability, adapting to varying workloads in real-time." (Highlights the dynamic adaptation capability that makes Model Router suitable for production workloads with variable demand)

## Context

- Features: Model Router deployment, Intelligent request routing, Cost optimization, Real-time model selection, Chat Completions API integration, Performance monitoring
- Versions: 2025-05-19

Source: Stefano Demiliani, community blog. Summary, key points and quotes are derived (CONTENT-NOTICE.md); read the original for the full text.