Enterprise Cloud

Azure OpenAI Service

Enterprise-grade access to OpenAI models (GPT-4o, GPT-4, o1, DALL-E, Whisper) with Azure security, compliance, and regional data residency.

Why Azure OpenAI?

Enterprise Security

Private endpoints, VNet integration, Azure AD authentication

Data Residency

Choose deployment region. Data stays in your geography.

Compliance

SOC 2, HIPAA, GDPR, ISO 27001 certifications

Same API

OpenAI-compatible API. Minimal code changes required.

Available Models (2024-2025)

Model	Context	Best For
GPT-4o	128K	Multimodal (text + vision), fastest GPT-4
GPT-4o-mini	128K	Cost-effective, high volume
o1	200K	Advanced reasoning, math, coding
o1-mini	128K	Fast reasoning, code
GPT-4 Turbo	128K	Complex tasks, JSON mode
GPT-3.5 Turbo	16K	Simple tasks, low cost
DALL-E 3	-	Image generation
Whisper	-	Speech-to-text
text-embedding-3-large	-	Embeddings (3072 dim)

Setup Steps

Create Azure OpenAI Resource

Azure Portal → Create Resource → "Azure OpenAI" → Select region

Deploy a Model

Azure AI Studio → Deployments → Deploy Model → Choose GPT-4o → Set deployment name

Get Credentials

Keys and Endpoint → Copy API Key and Endpoint URL

Python SDK

pip install openai

Chat Completion

from openai import AzureOpenAI

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

response = client.chat.completions.create(
    model="gpt-4o",  # Your deployment name
    messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "What is the capital of France?"}
    ],
    temperature=0.7,
    max_tokens=1000
)
print(response.choices[0].message.content)

Streaming Response

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

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

Vision (GPT-4o with Images)

response = client.chat.completions.create(
    model="gpt-4o",
    messages=[{
        "role": "user",
        "content": [
            {"type": "text", "text": "What's in this image?"},
            {"type": "image_url", "image_url": {
                "url": "https://example.com/image.jpg"
            }}
        ]
    }]
)

Embeddings

response = client.embeddings.create(
    model="text-embedding-3-large",  # Your embedding deployment
    input="The quick brown fox jumps over the lazy dog"
)
embedding = response.data[0].embedding  # 3072-dim vector

LangChain Integration

from langchain_openai import AzureChatOpenAI, AzureOpenAIEmbeddings

# Chat model
llm = AzureChatOpenAI(
    azure_deployment="gpt-4o",
    api_version="2024-10-21",
    temperature=0.7
)

# Embeddings
embeddings = AzureOpenAIEmbeddings(
    azure_deployment="text-embedding-3-large",
    api_version="2024-10-21"
)

# Usage
response = llm.invoke("Explain RAG in simple terms")
print(response.content)

Pricing Overview

Model	Input (1M tokens)	Output (1M tokens)
GPT-4o	$2.50	$10.00
GPT-4o-mini	$0.15	$0.60
o1	$15.00	$60.00
GPT-3.5 Turbo	$0.50	$1.50

*Prices approximate. Check Azure pricing page for current rates.

Azure OpenAI vs OpenAI Direct

Aspect	Azure OpenAI	OpenAI Direct
Data Residency	You choose region	US only
VNet Support	Yes	No
Enterprise SSO	Azure AD	Limited
Model Availability	Delayed by weeks	Immediate
Billing	Azure subscription	Credit card
SLA	Enterprise SLA	Best effort