Azure OpenAI

Integrate Azure-hosted OpenAI models through the Adaline Proxy to automatically capture telemetry — requests, responses, token usage, latency, and costs — with minimal code changes.

Supported Models

Azure OpenAI supports any OpenAI model deployed in your Azure resource. Chat Models

Model	Description
`gpt-4o`	Multimodal with vision support
`gpt-4o-mini`	Fast and cost-effective multimodal
`gpt-4-turbo`	High capability with 128k context
`gpt-4`	Original GPT-4 model
`gpt-3.5-turbo`	Fast and cost-effective

Embedding Models

Model	Description
`text-embedding-3-large`	Highest quality embeddings
`text-embedding-3-small`	Balanced quality and cost
`text-embedding-ada-002`	Legacy embedding model

Models must be deployed in your Azure OpenAI resource before use. The model name corresponds to your Azure deployment name.

Proxy Base URL

https://gateway.adaline.ai/v1/azure/

Prerequisites

An Azure OpenAI resource with deployed models
Your Azure OpenAI API key, resource name, and deployment name
An Adaline API key, project ID, and prompt ID

Chat Completions

Complete Chat

from openai import AzureOpenAI

client = AzureOpenAI(
    api_key="your-azure-openai-api-key",
    api_version="2024-02-01",
    azure_endpoint="https://gateway.adaline.ai/v1/azure/",
    azure_deployment="your-deployment-name"
)

headers = {
    "adaline-api-key": "your-adaline-api-key",
    "adaline-project-id": "your-project-id",
    "adaline-prompt-id": "your-prompt-id",
    "adaline-azure-resource-name": "your-resource-name"
}

response = client.chat.completions.create(
    model="gpt-4",
    messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "What is cloud computing?"}
    ],
    extra_headers=headers
)

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

import { AzureOpenAI } from "openai";

const client = new AzureOpenAI({
  apiKey: "your-azure-openai-api-key",
  apiVersion: "2024-02-01",
  endpoint: "https://gateway.adaline.ai/v1/azure/",
  deployment: "your-deployment-name",
});

const response = await client.chat.completions.create(
  {
    model: "gpt-4",
    messages: [
      { role: "system", content: "You are a helpful assistant." },
      { role: "user", content: "What is cloud computing?" },
    ],
  },
  {
    headers: {
      "adaline-api-key": "your-adaline-api-key",
      "adaline-project-id": "your-project-id",
      "adaline-prompt-id": "your-prompt-id",
      "adaline-azure-resource-name": "your-resource-name",
    },
  }
);

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

The adaline-azure-resource-name header is required for Azure OpenAI. This is the name of your Azure OpenAI resource (not the deployment name).

Stream Chat

from openai import AzureOpenAI

client = AzureOpenAI(
    api_key="your-azure-openai-api-key",
    api_version="2024-02-01",
    azure_endpoint="https://gateway.adaline.ai/v1/azure/",
    azure_deployment="your-deployment-name"
)

headers = {
    "adaline-api-key": "your-adaline-api-key",
    "adaline-project-id": "your-project-id",
    "adaline-prompt-id": "your-prompt-id",
    "adaline-azure-resource-name": "your-resource-name"
}

stream = client.chat.completions.create(
    model="gpt-4",
    messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "Explain Azure services in detail."}
    ],
    stream=True,
    extra_headers=headers
)

for chunk in stream:
    if chunk.choices[0].delta.content is not None:
        print(chunk.choices[0].delta.content, end="")

Embeddings

from openai import AzureOpenAI

client = AzureOpenAI(
    api_key="your-azure-openai-api-key",
    api_version="2024-02-01",
    azure_endpoint="https://gateway.adaline.ai/v1/azure/",
    azure_deployment="your-embedding-deployment-name"
)

headers = {
    "adaline-api-key": "your-adaline-api-key",
    "adaline-project-id": "your-project-id",
    "adaline-prompt-id": "your-prompt-id",
    "adaline-azure-resource-name": "your-resource-name"
}

response = client.embeddings.create(
    model="text-embedding-ada-002",
    input="The quick brown fox jumps over the lazy dog",
    extra_headers=headers
)

embedding = response.data[0].embedding
print(f"Embedding dimension: {len(embedding)}")

Next Steps

Multi-Step Workflows — RAG pipelines, multi-step generation, and conversational agents
Headers Reference — Complete header documentation

Back to Integrations

Browse all integrations

Get started

AI providers

Frameworks

Azure OpenAI

Azure OpenAI

Supported Models

Proxy Base URL

Prerequisites

Chat Completions

Complete Chat

Stream Chat

Embeddings

Next Steps

Back to Integrations

​Azure OpenAI

​Supported Models

​Proxy Base URL

​Prerequisites

​Chat Completions

​Complete Chat

​Stream Chat

​Embeddings

​Next Steps

Back to Integrations

Azure OpenAI

Supported Models

Proxy Base URL

Prerequisites

Chat Completions

Complete Chat

Stream Chat

Embeddings

Next Steps