Teams App Developer

foundry-cloud-ts

purpose

Using Azure AI Foundry (cloud) and the Azure AI model catalog for serverless model inference. Covers Model-as-a-Service (MaaS) deployments, the Azure AI Inference SDK, GitHub Models, and connecting to Foundry-hosted models from TypeScript.

rules

1. Azure AI Foundry provides serverless model endpoints (Model-as-a-Service). No GPU provisioning needed — deploy a model from the catalog and get an HTTPS endpoint with pay-per-token billing. Available models include Phi-4, Llama, Mistral, Cohere, and more. learn.microsoft.com/azure/ai-studio/how-to/deploy-models-serverless

2. MaaS endpoints are OpenAI-compatible. The deployed endpoint exposes /v1/chat/completions with the standard OpenAI request/response format. Use the openai npm package with a custom baseURL and the endpoint's API key. learn.microsoft.com/azure/ai-studio/reference/reference-model-inference-chat-completions

3. Alternatively, use @azure-rest/ai-inference for the Azure AI Inference SDK. This TypeScript SDK provides a typed client for Azure AI model endpoints. npm install @azure-rest/ai-inference. It supports chat completions, embeddings, and image generation. learn.microsoft.com/azure/ai-studio/reference/reference-model-inference-api

4. Authentication uses either API key or Entra ID token. MaaS endpoints accept an API key in the Authorization: Bearer <key> header. For managed identity, use @azure/identity to get a token. learn.microsoft.com/azure/ai-studio/how-to/deploy-models-serverless

5. GitHub Models provides free-tier access to the same model catalog. Use https://models.inference.ai.azure.com as the base URL with a GitHub personal access token as the API key. Great for prototyping before deploying to your own Azure subscription. docs.github.com/en/github-models

6. Deploy models via the Azure AI Foundry portal or CLI. In the portal: AI Foundry → Model catalog → Deploy. Via CLI: az cognitiveservices account deployment create for Azure OpenAI models, or use the AI Foundry portal for MaaS models. learn.microsoft.com/azure/ai-studio/how-to/deploy-models-serverless

7. Each deployment gets a unique endpoint URL and API key. The endpoint URL format is https://<deployment-name>.<region>.models.ai.azure.com. Copy the endpoint and key from the deployment details page.

8. Streaming works via standard SSE. Pass stream: true in the request body. The response is text/event-stream with data: {...} chunks, identical to OpenAI streaming format.

9. Some catalog models support tool use. Check the model card in the catalog for "Function calling" or "Tool use" support. The API format matches OpenAI's tools / tool_choice parameters.

10. For Azure OpenAI models (GPT-4o, etc.), use the Azure OpenAI Service instead. Foundry MaaS is for non-OpenAI models (Phi, Llama, Mistral, etc.). GPT-4o goes through the Azure OpenAI resource, not MaaS. See openai-azure-openai-ts.md for that.

patterns

Connect via OpenAI SDK (simplest)

import OpenAI from 'openai';

// MaaS endpoint from Azure AI Foundry deployment
const client = new OpenAI({
  baseURL: process.env.AZURE_AI_ENDPOINT, // e.g., https://my-phi4.eastus.models.ai.azure.com/v1
  apiKey: process.env.AZURE_AI_API_KEY,
  timeout: 30000,
});

const response = await client.chat.completions.create({
  model: 'phi-4', // model name from deployment
  messages: [
    { role: 'system', content: 'You are a helpful assistant.' },
    { role: 'user', content: userMessage },
  ],
  temperature: 0.7,
  max_tokens: 1000,
});

const reply = response.choices[0].message.content;

Connect via Azure AI Inference SDK

import ModelClient, { isUnexpected } from '@azure-rest/ai-inference';
import { AzureKeyCredential } from '@azure/core-auth';

const client = ModelClient(
  process.env.AZURE_AI_ENDPOINT!,
  new AzureKeyCredential(process.env.AZURE_AI_API_KEY!),
);

const response = await client.path('/chat/completions').post({
  body: {
    messages: [
      { role: 'system', content: 'You are a helpful assistant.' },
      { role: 'user', content: userMessage },
    ],
    temperature: 0.7,
    max_tokens: 1000,
  },
});

if (isUnexpected(response)) {
  throw new Error(`API error: ${response.status} ${response.body.error?.message}`);
}

const reply = response.body.choices[0].message.content;

GitHub Models (free prototyping)

import OpenAI from 'openai';

const client = new OpenAI({
  baseURL: 'https://models.inference.ai.azure.com',
  apiKey: process.env.GITHUB_TOKEN, // GitHub personal access token
});

const response = await client.chat.completions.create({
  model: 'Phi-4', // model name from GitHub Models catalog
  messages: [
    { role: 'system', content: 'You are a helpful assistant.' },
    { role: 'user', content: userMessage },
  ],
});

Dev/prod switching (GitHub Models → Azure AI Foundry)

import OpenAI from 'openai';

function createClient(): OpenAI {
  if (process.env.NODE_ENV === 'production') {
    return new OpenAI({
      baseURL: process.env.AZURE_AI_ENDPOINT,
      apiKey: process.env.AZURE_AI_API_KEY,
    });
  }
  // Free tier for development
  return new OpenAI({
    baseURL: 'https://models.inference.ai.azure.com',
    apiKey: process.env.GITHUB_TOKEN,
  });
}

pitfalls

• Confusing Foundry MaaS with Azure OpenAI. MaaS is for non-OpenAI models (Phi, Llama, Mistral). GPT-4o uses Azure OpenAI Service with a different SDK path. Don't mix them up.

• Wrong base URL format. MaaS endpoints already include the path. When using the openai SDK, set baseURL to the endpoint URL plus /v1. Check the deployment details page for the exact URL.

• GitHub Models rate limits. The free tier has aggressive rate limits. For production, deploy your own model in Azure AI Foundry.

• Model-specific quirks. Different models have different context windows, token limits, and feature support. Phi-4 supports function calling; some models don't. Check the model card.

• Endpoint key rotation. MaaS API keys can be regenerated in the portal. After rotation, update all services using the old key.

• Region availability. Not all models are available in all Azure regions. Check the model catalog for your region before deploying.

references

• Azure AI Foundry — Model Catalog

• Deploy Serverless Models (MaaS)

• Azure AI Inference SDK — npm

• Azure AI Model Inference API

• GitHub Models

instructions

This expert covers Azure AI Foundry cloud (Model-as-a-Service) and GitHub Models. Use it when the developer wants to call non-OpenAI models (Phi, Llama, Mistral) hosted on Azure's serverless infrastructure. For OpenAI/GPT models on Azure, see openai-azure-openai-ts.md. For running models locally, see foundry-local-ts.md.

Pair with: openai-azure-openai-ts.md (Azure OpenAI for GPT models), foundry-local-ts.md (local development), ../deploy/azure-cli-reference-ts.md (provisioning), ../security/secrets-ts.md (key management).

research

Deep Research prompt:

"Write a micro expert on Azure AI Foundry Model-as-a-Service (MaaS) for TypeScript developers. Cover: model catalog overview, serverless deployment, OpenAI-compatible endpoints, connecting with the openai npm package, @azure-rest/ai-inference SDK, GitHub Models as a free-tier option, API key vs Entra ID auth, streaming, function calling support by model, dev-to-prod patterns, and deployment via portal and CLI."