Azure AI Foundry - Portkey Docs

Azure AI Foundry provides a unified platform for enterprise AI operations, model building, and application development. With Portkey, you can seamlessly integrate with various models available on Azure AI Foundry and take advantage of features like observability, prompt management, fallbacks, and more.

Understanding Azure AI Foundry Deployments

Azure AI Foundry offers three different ways to deploy models, each with unique endpoints and configurations:

AI Services: Azure-managed models accessed through Azure AI Services endpoints
Managed: User-managed deployments running on dedicated Azure compute resources
Serverless: Seamless, scalable deployment without managing infrastructure

You can learn more about the Azure AI Foundry deployment here.

OpenAI modes on Azure

If you’re specifically looking to use OpenAI models on Azure, you should use Azure OpenAI instead, which is optimized for OpenAI models.

Anthropic Models on Azure

If you are looking to use Anthropic Models on Azure. Check this.

Integrate

To integrate Azure AI Foundry with Portkey, you’ll need to create a virtual key. Integrations securely store your Azure AI Foundry credentials in Portkey’s vault, allowing you to use a simple identifier in your code instead of handling sensitive authentication details directly. Navigate to the Inteagrations section in Portkey and select “Azure AI Foundry” as your provider.

Creating Your Azure AI Foundry Integration

Integrate Azure AI Foundry with Portkey to centrally manage your AI models and deployments. This guide walks you through setting up the integration using API key authentication.

Prerequisites

Before creating your integration, you’ll need:

An active Azure AI Foundry account
Access to your Azure AI Foundry portal
A deployed model on Azure Foundry

Step 1: Start Creating Your Integration

Navigate to the Integrations page in your Portkey dashboard and Select Azure AI Foundry as your provider.

Step 2: Configure Integration Details

Fill in the basic information for your integration:

Name: A descriptive name for this integration (e.g., “Azure AI Production”)
Short Description: Optional context about this integration’s purpose
Slug: A unique identifier used in API calls (e.g., “azure-ai-prod”)

Step 3: Set Up Authentication

Portkey supports three authentication methods for Azure AI Foundry. For most use cases, we recommend using the Default (API Key) method.

Default (API Key)
Azure Managed Entity
Azure Entra ID

Gather Your Azure Credentials

From your Azure AI Foundry portal, you’ll need to collect:

Navigate to your model deployment in Azure AI Foundry
Click on the deployment to view details
Copy the API Key from the authentication section
Copy the Target URI - this is your endpoint URL
Note the API Version from your deployment URL
Azure Deployment Name (Optional): Only required for Managed Services deployments

Enter Credentials in Portkey

For managed Azure deployments:Required parameters:

Azure Managed ClientID: Your managed client ID
Azure Foundry URL: The base endpoint URL for your deployment, formatted according to your deployment type:
- For AI Services: https://your-resource-name.services.ai.azure.com/models
- For Managed: https://your-model-name.region.inference.ml.azure.com/score
- For Serverless: https://your-model-name.region.models.ai.azure.com
Azure API Version: The API version to use (e.g., “2024-05-01-preview”). This is required if you have api version in your deployment url. Examples:
- If your URL is https://mycompany-ai.westus2.services.ai.azure.com/models?api-version=2024-05-01-preview, the API version is 2024-05-01-preview
Azure Deployment Name: (Optional) Required only when a single resource contains multiple deployments.

To use this authentication your azure application need to have the role of: conginitive services user. Enterprise-level authentication with Azure Entra ID:Required parameters:

Azure Entra ClientID: Your Azure Entra client ID
Azure Entra Secret: Your client secret
Azure Entra Tenant ID: Your tenant ID
Azure Foundry URL: The base endpoint URL for your deployment, formatted according to your deployment type:
- For AI Services: https://your-resource-name.services.ai.azure.com/models
- For Managed: https://your-model-name.region.inference.ml.azure.com/score
- For Serverless: https://your-model-name.region.models.ai.azure.com
Azure API Version: The API version to use (e.g., “2024-05-01-preview”). This is required if you have api version in your deployment url. Examples:
- If your URL is https://mycompany-ai.westus2.services.ai.azure.com/models?api-version=2024-05-01-preview, the API version is 2024-05-01-preview
Azure Deployment Name: (Optional) Required only when a single resource contains multiple deployments. Common in Managed deployments.

You can Learn more about these Azure Entra Resources here

Adding Multiple Models to Your Azure AI Foundry Integration

You can deploy multiple models through a single Azure AI Foundry integration by using Portkey’s custom models feature.

Steps to Add Additional Models

Navigate to your Azure AI Foundry integration in Portkey
Select the Model Provisioning step
Click Add Model in the top-right corner

Configure Your Model

Enter the following details for your Azure deployment: Model Slug: Use your Azure Model Deployment name exactly as it appears in Azure AI Foundry

Short Description: Optional description for team reference Model Type: Select “Custom model” Base Model: Choose the model that matches your deployment’s API structure (e.g., select gpt-4 for GPT-4 deployments)

This is just for reference. If you can’t find the particular model, you can just choose a similar model.

Custom Pricing: Enable to track costs with your negotiated rates Once configured, this model will be available alongside others in your integration, allowing you to manage multiple Azure deployments through a single set of credentials.

Using Anthropic Models on Azure AI Foundry

Azure AI Foundry supports Anthropic models (Claude) through a slightly different configuration process. Follow these steps to integrate Anthropic models with Portkey.

Step 1: Create an Azure Foundry Integration

When creating the integration for Anthropic models, you’ll need to configure the following:

Azure API Key: Copy this from your Azure Foundry console

Azure Target URI: From your Foundry console, you’ll get a URL like:

https://resource-name-swedencentral.services.ai.azure.com/anthropic/v1/messages

You need to strip your URL till /anthropic:

https://resource-name-swedencentral.services.ai.azure.com/anthropic

For Anthropic models on Azure Foundry, you don’t need to provide the Azure API Version or Deployment Name fields.

Step 2: Configure Workspace Provisioning

After setting up the integration credentials, proceed with the workspace provisioning step as usual.

Step 3: Add Your Anthropic Model

In the Model Provisioning step:

Click the + Add Model button at the top
Configure the model with these details:
- Model Slug: Enter your deployment name from the Azure Foundry console
- Base Model: Search for and select your Anthropic model (e.g., claude-opus-4-1-20250805, claude-sonnet-4-20250514, etc.)
Save the configuration

Making Requests to Anthropic Models

Once configured, you can call your Anthropic model using the Model Slug you saved:

NodeJS
Python
cURL

import Portkey from 'portkey-ai';

const client = new Portkey({
  apiKey: 'PORTKEY_API_KEY',
  provider: '@AZURE_FOUNDRY_ANTHROPIC_PROVIDER'
});

const response = await client.chat.completions.create({
  messages: [{ role: "user", content: "Hello, Claude!" }],
  model: "your-azure-deployment-name", // Use the Model Slug you configured
});

console.log(response.choices[0].message.content);

from portkey_ai import Portkey

client = Portkey(
  api_key="PORTKEY_API_KEY",
  virtual_key="AZURE_FOUNDRY_ANTHROPIC_PROVIDER"
)

response = client.chat.completions.create(
  model="your-azure-deployment-name", # Use the Model Slug you configured
  messages=[
    {"role": "user", "content": "Hello, Claude!"}
  ]
)

print(response.choices[0].message.content)

curl https://api.portkey.ai/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "x-portkey-api-key: $PORTKEY_API_KEY" \
  -H "x-portkey-provider: $AZURE_FOUNDRY_ANTHROPIC_PROVIDER" \
  -d '{
    "model": "your-azure-deployment-name",
    "messages": [
      { "role": "user", "content": "Hello, Claude!" }
    ]
  }'

Sample Request

Once you’ve created your Integration key, you can start making requests to Azure AI Foundry models through Portkey.

NodeJS
Python
cURL

Install the Portkey SDK with npm

npm install portkey-ai

import Portkey from 'portkey-ai';

const client = new Portkey({
  apiKey: 'PORTKEY_API_KEY',
  provider:'@AZURE_FOUNDRY_PROVIDER'
});

async function main() {
  const response = await client.chat.completions.create({
    messages: [{ role: "user", content: "Tell me about cloud computing" }],
    model: "DeepSeek-V3-0324", // Replace with your deployed model name
  });

  console.log(response.choices[0].message.content);
}

main();

Install the Portkey SDK with pip

pip install portkey-ai

from portkey_ai import Portkey

client = Portkey(
  api_key = "PORTKEY_API_KEY",
  virtual_key = "AZURE_FOUNDRY_PROVIDER"
)

response = client.chat.completions.create(
  model="DeepSeek-V3-0324", # Replace with your deployed model name
  messages=[
    {"role": "system", "content": "You are a helpful assistant."},
    {"role": "user", "content": "Tell me about cloud computing"}
  ]
)

print(response.choices[0].message.content)

curl https://api.portkey.ai/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "x-portkey-api-key: $PORTKEY_API_KEY" \
  -H "x-portkey-provider: $AZURE_FOUNDRY_PROVIDER" \
  -d '{
    "model": "DeepSeek-V3-0324",
    "messages": [
      { "role": "user", "content": "Tell me about cloud computing" }
    ]
  }'

Advanced Features

Function Calling

Azure AI Foundry supports function calling (tool calling) for compatible models. Here’s how to implement it with Portkey:

Node.js
Python

let tools = [{
    type: "function",
    function: {
        name: "getWeather",
        description: "Get the current weather",
        parameters: {
            type: "object",
            properties: {
                location: { type: "string", description: "City and state" },
                unit: { type: "string", enum: ["celsius", "fahrenheit"] }
            },
            required: ["location"]
        }
    }
}];

let response = await portkey.chat.completions.create({
    model: "DeepSeek-V3-0324", // Use a model that supports function calling
    messages: [
        { role: "system", content: "You are a helpful assistant." },
        { role: "user", content: "What's the weather like in Delhi?" }
    ],
    tools,
    tool_choice: "auto",
});

console.log(response.choices[0]);

tools = [{
    "type": "function",
    "function": {
        "name": "getWeather",
        "description": "Get the current weather",
        "parameters": {
            "type": "object",
            "properties": {
                "location": {"type": "string", "description": "City and state"},
                "unit": {"type": "string", "enum": ["celsius", "fahrenheit"]}
            },
            "required": ["location"]
        }
    }
}]

response = portkey.chat.completions.create(
    model="DeepSeek-V3-0324", # Use a model that supports function calling
    messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "What's the weather like in Delhi?"}
    ],
    tools=tools,
    tool_choice="auto"
)

print(response.choices[0])

Vision Capabilities

Process images alongside text using Azure AI Foundry’s vision capabilities:

Node.js
Python

const response = await portkey.chat.completions.create({
  model: "Llama-4-Scout-17B-16E", // Use a model that supports vision
  messages: [
    {
      role: "user",
      content: [
        { type: "text", text: "What's in this image?" },
        {
          type: "image_url",
          image_url: "https://upload.wikimedia.org/wikipedia/commons/thumb/d/dd/Gfp-wisconsin-madison-the-nature-boardwalk.jpg/2560px-Gfp-wisconsin-madison-the-nature-boardwalk.jpg",
        },
      ],
    },
  ],
  max_tokens: 500,
});

console.log(response.choices[0].message.content);

response = portkey.chat.completions.create(
    model="Llama-4-Scout-17B-16E", # Use a model that supports vision
    messages=[
        {
            "role": "user",
            "content": [
                {"type": "text", "text": "What's in this image?"},
                {
                    "type": "image_url",
                    "image_url": "https://upload.wikimedia.org/wikipedia/commons/thumb/d/dd/Gfp-wisconsin-madison-the-nature-boardwalk.jpg/2560px-Gfp-wisconsin-madison-the-nature-boardwalk.jpg",
                },
            ],
        }
    ],
    max_tokens=500,
)

print(response.choices[0].message.content)

Structured Outputs

Get consistent, parseable responses in specific formats:

Node.js
Python

const response = await portkey.chat.completions.create({
  model: "cohere-command-a", // Use a model that supports response formats
  messages: [
    { role: "system", content: "You are a helpful assistant." },
    { role: "user", content: "List the top 3 cloud providers with their main services" }
  ],
  response_format: { type: "json_object" },
  temperature: 0
});

console.log(JSON.parse(response.choices[0].message.content));

response = portkey.chat.completions.create(
    model="cohere-command-a", # Use a model that supports response formats
    messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "List the top 3 cloud providers with their main services"}
    ],
    response_format={"type": "json_object"},
    temperature=0
)

import json
print(json.loads(response.choices[0].message.content))

Relationship with Azure OpenAI

For Azure OpenAI specific models and deployments, we recommend using the existing Azure OpenAI provider in Portkey:

Azure OpenAI Integration

Learn how to integrate Azure OpenAI with Portkey for access to OpenAI models hosted on Azure.

Portkey Features with Azure AI Foundry

Setting Up Fallbacks

Create fallback configurations to ensure reliability when working with Azure AI Foundry models:

{
  "strategy": {
    "mode": "fallback"
  },
  "targets": [
    {
      "provider":"@azure-foundry-virtual-key",
      "override_params": {
        "model": "DeepSeek-V3-0324"
      }
    },
    {
      "provider":"@openai-virtual-key",
      "override_params": {
        "model": "gpt-4o"
      }
    }
  ]
}

Load Balancing Between Models

Distribute requests across multiple models for optimal performance:

{
  "strategy": {
    "mode": "loadbalance"
  },
  "targets": [
    {
      "provider":"@azure-foundry-virtual-key-1",
      "override_params": {
        "model": "DeepSeek-V3-0324"
      },
      "weight": 0.7
    },
    {
      "provider":"@azure-foundry-virtual-key-2",
      "override_params": {
        "model": "cohere-command-a"
      },
      "weight": 0.3
    }
  ]
}

Conditional Routing

Route requests based on specific conditions like user type or content requirements:

{
  "strategy": {
    "mode": "conditional",
    "conditions": [
      {
        "query": { "metadata.user_type": { "$eq": "premium" } },
        "then": "high-performance-model"
      },
      {
        "query": { "metadata.content_type": { "$eq": "code" } },
        "then": "code-specialized-model"
      }
    ],
    "default": "standard-model"
  },
  "targets": [
    {
      "name": "high-performance-model",
      "provider":"@azure-foundry-virtual-key-1",
      "override_params": {
        "model": "Llama-4-Scout-17B-16E"
      }
    },
    {
      "name": "code-specialized-model",
      "provider":"@azure-foundry-virtual-key-2",
      "override_params": {
        "model": "DeepSeek-V3-0324"
      }
    },
    {
      "name": "standard-model",
      "provider":"@azure-foundry-virtual-key-3",
      "override_params": {
        "model": "cohere-command-a"
      }
    }
  ]
}

Managing Prompts with Azure AI Foundry

You can manage all prompts to Azure AI Foundry in the Prompt Library. Once you’ve created and tested a prompt in the library, use the portkey.prompts.completions.create interface to use the prompt in your application.

NodeJS
Python

const promptCompletion = await portkey.prompts.completions.create({
    promptID: "Your Prompt ID",
    variables: {
       // The variables specified in the prompt
    }
})

prompt_completion = portkey.prompts.completions.create(
    prompt_id="Your Prompt ID",
    variables={
       # The variables specified in the prompt
    }
)

Next Steps

Explore these additional resources to make the most of your Azure AI Foundry integration with Portkey:

Add Metadata

Learn how to add custom metadata to your Azure AI Foundry requests.

Gateway Configs

Configure advanced gateway features for your Azure AI Foundry requests.

Request Tracing

Trace your Azure AI Foundry requests for better observability.

Setup Fallbacks

Create fallback configurations between different providers.

Ecosystem

LLM Integrations

Cloud Platforms

Guardrails

Plugins

Vector Databases

Agents

AI Apps

Libraries

Tracing Providers

MCP Clients

MCP Servers

​Understanding Azure AI Foundry Deployments

OpenAI modes on Azure

Anthropic Models on Azure

​Integrate

​Creating Your Azure AI Foundry Integration

​Prerequisites

​Step 1: Start Creating Your Integration

​Step 2: Configure Integration Details

​Step 3: Set Up Authentication

​Gather Your Azure Credentials

​Enter Credentials in Portkey

​Adding Multiple Models to Your Azure AI Foundry Integration

​Steps to Add Additional Models

​Configure Your Model

​Using Anthropic Models on Azure AI Foundry

​Step 1: Create an Azure Foundry Integration

​Step 2: Configure Workspace Provisioning

​Step 3: Add Your Anthropic Model

​Making Requests to Anthropic Models

​Sample Request

​Advanced Features

​Function Calling

​Vision Capabilities

​Structured Outputs

​Relationship with Azure OpenAI

Azure OpenAI Integration

​Portkey Features with Azure AI Foundry

​Setting Up Fallbacks

​Load Balancing Between Models

​Conditional Routing

​Managing Prompts with Azure AI Foundry

​Next Steps

Add Metadata

Gateway Configs

Request Tracing

Setup Fallbacks

Understanding Azure AI Foundry Deployments

Integrate

Creating Your Azure AI Foundry Integration

Prerequisites

Step 1: Start Creating Your Integration

Step 2: Configure Integration Details

Step 3: Set Up Authentication

Gather Your Azure Credentials

Enter Credentials in Portkey

Adding Multiple Models to Your Azure AI Foundry Integration

Steps to Add Additional Models

Configure Your Model

Using Anthropic Models on Azure AI Foundry

Step 1: Create an Azure Foundry Integration

Step 2: Configure Workspace Provisioning

Step 3: Add Your Anthropic Model

Making Requests to Anthropic Models

Sample Request

Advanced Features

Function Calling

Vision Capabilities

Structured Outputs

Relationship with Azure OpenAI

Portkey Features with Azure AI Foundry

Setting Up Fallbacks

Load Balancing Between Models

Conditional Routing

Managing Prompts with Azure AI Foundry

Next Steps