Azure OpenAI Service

Overview

Azure OpenAI Service provides OpenAI's models (GPT-4o, GPT-4o mini, DALL-E 3, Whisper) hosted on Microsoft Azure infrastructure. It offers enterprise features: Managed Identity authentication (no API keys), VNET integration, Azure Policy compliance, content filtering, abuse monitoring, and regional data residency. Uses the same

openai

Python/TS SDK — just point it at your Azure endpoint.

Azure vs OpenAI Direct

Feature	OpenAI (direct)	Azure OpenAI
Auth	API Key	API Key or Managed Identity
Data residency	US primarily	Any Azure region
Enterprise compliance	Limited	SOC2, HIPAA, ISO 27001
Content filtering	❌	✅ Configurable
VNET isolation	❌	✅ Private endpoints
Deployment control	Shared	Your own deployments
Fine-tuning	✅	✅
Latency to Azure services	Higher	Lower (co-located)

Setup

pip install openai azure-identity  # azure-identity for Managed Identity

# API Key auth (dev/test)
export AZURE_OPENAI_API_KEY=...
export AZURE_OPENAI_ENDPOINT=https://my-resource.openai.azure.com/

# Deployment names (you set these when deploying models in Azure Portal)
export AZURE_OPENAI_DEPLOYMENT=gpt-4o  # Your deployment name

Instructions

Basic Chat with API Key

from openai import AzureOpenAI

client = AzureOpenAI(
    api_key="your_azure_openai_api_key",
    azure_endpoint="https://my-resource.openai.azure.com/",
    api_version="2024-10-21",  # Check docs for latest stable version
)

response = client.chat.completions.create(
    model="gpt-4o",  # This is your DEPLOYMENT NAME, not the model name
    messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "What is Azure OpenAI Service?"},
    ],
    max_tokens=1024,
    temperature=0.7,
)

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

Managed Identity Auth (No API Keys)

from openai import AzureOpenAI
from azure.identity import DefaultAzureCredential, get_bearer_token_provider

# DefaultAzureCredential works with:
# - Managed Identity (in Azure VM, AKS, App Service, Functions)
# - Azure CLI (local development)
# - Visual Studio / VS Code credentials
credential = DefaultAzureCredential()
token_provider = get_bearer_token_provider(
    credential,
    "https://cognitiveservices.azure.com/.default"
)

client = AzureOpenAI(
    azure_endpoint="https://my-resource.openai.azure.com/",
    azure_ad_token_provider=token_provider,
    api_version="2024-10-21",
)

response = client.chat.completions.create(
    model="gpt-4o",  # deployment name
    messages=[{"role": "user", "content": "Hello!"}],
)
print(response.choices[0].message.content)

TypeScript / Node.js

import OpenAI from "openai";

const client = new OpenAI({
  apiKey: process.env.AZURE_OPENAI_API_KEY,
  baseURL: `${process.env.AZURE_OPENAI_ENDPOINT}openai/deployments/${process.env.AZURE_OPENAI_DEPLOYMENT}`,
  defaultQuery: { "api-version": "2024-10-21" },
  defaultHeaders: { "api-key": process.env.AZURE_OPENAI_API_KEY },
});

const response = await client.chat.completions.create({
  model: process.env.AZURE_OPENAI_DEPLOYMENT!,
  messages: [{ role: "user", content: "Explain TypeScript generics." }],
});

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

Streaming

from openai import AzureOpenAI

client = AzureOpenAI(
    api_key="...",
    azure_endpoint="https://my-resource.openai.azure.com/",
    api_version="2024-10-21",
)

stream = client.chat.completions.create(
    model="gpt-4o",
    messages=[{"role": "user", "content": "Write a sonnet about cloud computing."}],
    stream=True,
)

for chunk in stream:
    if chunk.choices[0].delta.content:
        print(chunk.choices[0].delta.content, end="", flush=True)
print()

Function Calling

import json
from openai import AzureOpenAI

client = AzureOpenAI(
    api_key="...",
    azure_endpoint="https://my-resource.openai.azure.com/",
    api_version="2024-10-21",
)

tools = [
    {
        "type": "function",
        "function": {
            "name": "get_azure_resource_cost",
            "description": "Get the cost of an Azure resource for the current month",
            "parameters": {
                "type": "object",
                "properties": {
                    "resource_group": {"type": "string"},
                    "resource_name": {"type": "string"},
                },
                "required": ["resource_group", "resource_name"],
            },
        },
    }
]

messages = [{"role": "user", "content": "How much is my vm-prod01 costing this month?"}]

response = client.chat.completions.create(
    model="gpt-4o",
    messages=messages,
    tools=tools,
    tool_choice="auto",
)

if response.choices[0].finish_reason == "tool_calls":
    tool_call = response.choices[0].message.tool_calls[0]
    args = json.loads(tool_call.function.arguments)
    print(f"Called: {tool_call.function.name} with {args}")

    messages.append(response.choices[0].message)
    messages.append({
        "role": "tool",
        "tool_call_id": tool_call.id,
        "content": json.dumps({"cost_usd": 142.53, "currency": "USD"}),
    })

    final = client.chat.completions.create(model="gpt-4o", messages=messages)
    print(final.choices[0].message.content)

Image Generation with DALL-E 3

from openai import AzureOpenAI

client = AzureOpenAI(
    api_key="...",
    azure_endpoint="https://my-resource.openai.azure.com/",
    api_version="2024-02-01",  # DALL-E uses a different API version
)

response = client.images.generate(
    model="dall-e-3",  # your DALL-E 3 deployment name
    prompt="A futuristic city skyline with solar panels, photorealistic, golden hour",
    size="1024x1024",
    quality="hd",
    n=1,
)

print(response.data[0].url)
print(f"Revised prompt: {response.data[0].revised_prompt}")

Speech-to-Text with Whisper

from openai import AzureOpenAI

client = AzureOpenAI(
    api_key="...",
    azure_endpoint="https://my-resource.openai.azure.com/",
    api_version="2024-06-01",
)

with open("recording.mp3", "rb") as audio_file:
    transcript = client.audio.transcriptions.create(
        model="whisper",  # your Whisper deployment name
        file=audio_file,
        language="en",
        response_format="text",
    )

print(transcript)

Embeddings

from openai import AzureOpenAI

client = AzureOpenAI(
    api_key="...",
    azure_endpoint="https://my-resource.openai.azure.com/",
    api_version="2024-10-21",
)

response = client.embeddings.create(
    model="text-embedding-3-large",  # deployment name
    input=["The quick brown fox", "Jumps over the lazy dog"],
)

for item in response.data:
    print(f"Embedding {item.index}: {len(item.embedding)} dims")

Deployment vs Model Name

A critical Azure OpenAI concept: deployments are your named instances of a model:

Azure Portal:
  Resource: my-openai-resource
  Deployments:
    - Name: "gpt-4o"          → Model: gpt-4o (2024-11-20)
    - Name: "gpt-4o-mini"     → Model: gpt-4o-mini (2024-07-18)
    - Name: "text-embed-large" → Model: text-embedding-3-large

In code,

model=

takes the deployment name you configured, not the OpenAI model name.

Content Filtering Configuration

Content filters are configured in Azure Portal under your deployment settings:

• Hate, Violence, Sexual, Self-harm — each configurable (low/medium/high threshold)
• Prompt injection protection — detects jailbreak attempts
• Custom blocklists — add domain-specific blocked terms

# When content is filtered, the API returns an error:
from openai import AzureOpenAI, BadRequestError

client = AzureOpenAI(...)

try:
    response = client.chat.completions.create(
        model="gpt-4o",
        messages=[{"role": "user", "content": "user_input"}],
    )
except BadRequestError as e:
    if e.code == "content_filter":
        print(f"Content filtered: {e.error.innererror}")

Guidelines

• Use Managed Identity in production (AKS, App Service, Functions) — never store API keys in code.
• Each deployment is a separate Azure resource with its own quota and settings.
• Pin

  api_version

  to a stable version — latest is not always most stable.
• Azure Content Filtering is always on by default — configure thresholds per deployment.
• For HIPAA compliance, ensure your Azure subscription has a Business Associate Agreement (BAA).
• VNET private endpoints prevent traffic from leaving your Azure network — required for strict isolation.
• Monitor usage and costs with Azure Monitor and set budget alerts on the Cognitive Services resource.