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.

Quick Start

from portkey_ai import Portkey

# 1. Install: pip install portkey-ai
# 2. Add @azure-foundry provider in Model Catalog
# 3. Use it:

portkey = Portkey(
    api_key="PORTKEY_API_KEY",
    provider="@azure-foundry"
)

response = portkey.chat.completions.create(
    model="DeepSeek-V3-0324",  # Your deployed model name
    messages=[{"role": "user", "content": "Tell me about cloud computing"}]
)

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

Add Provider in Model Catalog

To integrate Azure AI Foundry with Portkey, you’ll create a provider in the Model Catalog. This securely stores your Azure AI Foundry credentials, allowing you to use a simple identifier in your code instead of handling sensitive authentication details directly.

OpenAI models 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.

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.

Creating Your Azure AI Foundry Provider

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

Prerequisites

Before creating your provider, you’ll need:

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

Step 1: Navigate to Model Catalog

Go to Model Catalog → Add Provider and select Azure AI Foundry as your provider.

Step 2: Configure Provider Details

Fill in the basic information for your provider:

Name: A descriptive name for this provider (e.g., “Azure AI Production”)
Short Description: Optional context about this provider’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 Provider

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

Steps to Add Additional Models

Navigate to your Azure AI Foundry provider in Model Catalog
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 provider, 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 Provider

When creating the provider 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 provider 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-4-5-20251101, claude-sonnet-4-5-20250929, claude-haiku-4-5-20251001, claude-opus-4-1-20250805)
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",
  provider="@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!" }
    ]
  }'

Using the /messages Route with Azure Foundry Anthropic Models

Access Anthropic models on Azure AI Foundry through Anthropic’s native /messages endpoint using Portkey’s SDK or Anthropic’s SDK.

The /messages route provides access to Anthropic-native features like extended thinking, prompt caching, and native streaming formats when using Claude models on Azure AI Foundry.

cURL
Anthropic Python SDK
Anthropic TS SDK

curl --location 'https://api.portkey.ai/v1/messages' \
--header 'Content-Type: application/json' \
--header 'x-portkey-api-key: YOUR_PORTKEY_API_KEY' \
--data-raw '{
    "model": "@your-azure-foundry-anthropic-provider/your-azure-deployment-name",
    "max_tokens": 1024,
    "messages": [
      {
        "role": "user",
        "content": "Hello, Claude!"
      }
    ]
  }'

import anthropic

client = anthropic.Anthropic(
    api_key="dummy", # we will use portkey's provider slug
    default_headers={"x-portkey-api-key": "YOUR_PORTKEY_API_KEY"},
    base_url="https://api.portkey.ai"
)

message = client.messages.create(
    model="@your-azure-foundry-anthropic-provider/your-azure-deployment-name",
    max_tokens=1024,
    messages=[
        {"role": "user", "content": "Hello, Claude!"}
    ],
)
print(message.content)

import Anthropic from '@anthropic-ai/sdk';

const anthropic = new Anthropic({
  apiKey: 'PORTKEY_API_KEY', // we will use portkey's provider slug
  baseURL: "https://api.portkey.ai",
});

const msg = await anthropic.messages.create({
  model: "@your-azure-foundry-anthropic-provider/your-azure-deployment-name",
  max_tokens: 1024,
  messages: [{ role: "user", content: "Hello, Claude!" }],
});
console.log(msg);

Using the Responses API with Azure AI Foundry

Portkey supports the Responses API on Azure AI Foundry in two modes. Pick the one that matches your Azure deployment.

Mode	Default?	How it works	When to use
Gateway Transformation	Yes	Portkey converts Responses → Chat Completions → Responses around your Azure deployment	Any model deployed on Azure AI Foundry (DeepSeek, Cohere, Llama, Mistral, etc.)
Direct (Proxy)	Opt-in via header	Portkey forwards your Responses payload as-is to Azure’s native `/responses` endpoint	Azure deployments that natively support `/responses` (e.g. Azure AI Agents)

In both modes, Portkey logging, tracing, guardrails, caching, retries, and fallbacks continue to work.

Mode 1 - Gateway Transformation (Default)

This is the default behaviour and requires no extra configuration. Portkey accepts the request at /v1/responses, adapts the payload to Chat Completions, calls Azure AI Foundry’s /chat/completions endpoint, and translates the response back to the Responses API format before returning it to you. Because the call is made over Chat Completions on Azure’s side, every model on your Azure AI Foundry provider works — even ones that don’t natively expose a Responses endpoint.

from portkey_ai import Portkey

portkey = Portkey(
    api_key="PORTKEY_API_KEY",
    provider="@AZURE_FOUNDRY_PROVIDER"
)

response = portkey.responses.create(
    model="DeepSeek-V3-0324",  # Any model on your Azure AI Foundry provider
    input="Explain the difference between AKS and ACI in one paragraph."
)

print(response.output_text)

Features that rely on Azure storing response state — previous_response_id, store, retrieve/delete on /v1/responses/:id, and built-in tools like web_search — are not available in this mode. Use multi-turn input arrays to carry conversation history. See Open Responses → Native-Only Features.

Mode 2 - Direct Responses (Proxy)

When your Azure AI Foundry deployment exposes a native /responses endpoint (for example, Azure AI Agents), set the x-portkey-provider-responses-proxy header to true. Portkey will skip the Chat Completions transformation and forward the request body exactly as-is to Azure’s native Responses endpoint. This is required to use Azure-specific parameters such as agent_reference, which targets a deployed Azure AI Agent.

from portkey_ai import Portkey

portkey = Portkey(
    api_key="PORTKEY_API_KEY",
    provider="@AZURE_FOUNDRY_PROVIDER",
    # Forwards the request directly to Azure's native /responses endpoint
    provider_responses_proxy=True,
)

response = portkey.responses.create(
    model="gpt-4.1",  # Your Azure deployment name
    input="Summarise the latest Q3 sales numbers for the EMEA region.",
    agent_reference="asst_abc123",  # Azure AI Agent reference
)

print(response.output_text)

Direct mode forwards the payload verbatim. Your Azure deployment must accept the Responses API schema — if it doesn’t, requests will fail. Use Gateway Transformation (Mode 1) for everything else.

Choosing the right mode

Use Gateway Transformation for general-purpose Responses API calls against any Azure-deployed model.
Use Direct (Proxy) when calling Azure AI Agents or any Azure deployment that exposes the native Responses endpoint and you need Azure-specific parameters (agent_reference, server-side response storage, etc.).

For a deep dive into the Responses API itself — streaming, tool calling, reasoning, vision, structured output — see the Open Responses guide.

Sample Request

Once you’ve created your provider, 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",
  provider = "@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:

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:

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:

import json

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
)

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

Portkey Gateway Features

Portkey provides advanced gateway features for Azure AI Foundry deployments:

Fallbacks

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

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

Load Balancing

Distribute requests across multiple models for optimal performance:

{
  "strategy": {
    "mode": "loadbalance"
  },
  "targets": [
    {
      "provider": "@azure-foundry-prod-1",
      "override_params": {
        "model": "DeepSeek-V3-0324"
      },
      "weight": 0.7
    },
    {
      "provider": "@azure-foundry-prod-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-prod-1",
      "override_params": {
        "model": "Llama-4-Scout-17B-16E"
      }
    },
    {
      "name": "code-specialized-model",
      "provider": "@azure-foundry-prod-2",
      "override_params": {
        "model": "DeepSeek-V3-0324"
      }
    },
    {
      "name": "standard-model",
      "provider": "@azure-foundry-prod-3",
      "override_params": {
        "model": "cohere-command-a"
      }
    }
  ]
}

Managing Prompts

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.

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

Rerank

Azure AI Foundry supports reranking through Cohere models deployed on the platform. Use the Portkey unified /rerank endpoint with the cohere. model prefix:

from portkey_ai import Portkey

client = Portkey(
api_key="PORTKEY_API_KEY",
provider="@azure-foundry"
)

result = client.post(
"/v1/rerank",
body={
"model": "cohere.Cohere-rerank-v4.0-pro",
"query": "What is deep learning?",
"documents": [
"Deep learning is a subset of machine learning",
"The weather is sunny today",
"Neural networks have multiple layers"
]
}
)

print(result)

The cohere. prefix in the model name is automatically stripped before forwarding to the provider.

Next Steps

SDK Reference

Complete SDK documentation and API reference

Azure OpenAI

Use Azure OpenAI for OpenAI-specific models

Add Metadata

Add custom metadata to your Azure AI Foundry requests

Gateway Configs

Configure advanced gateway features

Request Tracing

Trace your Azure AI Foundry requests

Setup Fallbacks

Create fallback configurations between providers

​Quick Start

​Add Provider in Model Catalog

OpenAI models on Azure

​Understanding Azure AI Foundry Deployments

​Creating Your Azure AI Foundry Provider

​Prerequisites

​Step 1: Navigate to Model Catalog

​Step 2: Configure Provider Details

​Step 3: Set Up Authentication

​Gather Your Azure Credentials

​Enter Credentials in Portkey

​Adding Multiple Models to Your Azure AI Foundry Provider

​Steps to Add Additional Models

​Configure Your Model

​Using Anthropic Models on Azure AI Foundry

​Step 1: Create an Azure Foundry Provider

​Step 2: Configure Workspace Provisioning

​Step 3: Add Your Anthropic Model

​Making Requests to Anthropic Models

​Using the /messages Route with Azure Foundry Anthropic Models

​Using the Responses API with Azure AI Foundry

​Mode 1 - Gateway Transformation (Default)

​Mode 2 - Direct Responses (Proxy)

​Choosing the right mode

​Sample Request

​Advanced Features

​Function Calling

​Vision Capabilities

​Structured Outputs

​Portkey Gateway Features

​Fallbacks

​Load Balancing

​Conditional Routing

​Managing Prompts

​Rerank

​Next Steps

SDK Reference

Azure OpenAI

Add Metadata

Gateway Configs

Request Tracing

Setup Fallbacks

Quick Start

Add Provider in Model Catalog

Understanding Azure AI Foundry Deployments

Creating Your Azure AI Foundry Provider

Prerequisites

Step 1: Navigate to Model Catalog

Step 2: Configure Provider Details

Step 3: Set Up Authentication

Gather Your Azure Credentials

Enter Credentials in Portkey

Adding Multiple Models to Your Azure AI Foundry Provider

Steps to Add Additional Models

Configure Your Model

Using Anthropic Models on Azure AI Foundry

Step 1: Create an Azure Foundry Provider

Step 2: Configure Workspace Provisioning

Step 3: Add Your Anthropic Model

Making Requests to Anthropic Models

Using the /messages Route with Azure Foundry Anthropic Models

Using the Responses API with Azure AI Foundry

Mode 1 - Gateway Transformation (Default)

Mode 2 - Direct Responses (Proxy)

Choosing the right mode

Sample Request

Advanced Features

Function Calling

Vision Capabilities

Structured Outputs

Portkey Gateway Features

Fallbacks

Load Balancing

Conditional Routing

Managing Prompts

Rerank

Next Steps