azure-openai-2025

name

azure-openai-2025

description

Azure OpenAI Service and Azure AI Foundry models (2025-2026). PROACTIVELY activate for: (1) deploying Azure OpenAI models (GPT-5, GPT-4.1, GPT-4o), (2) Azure reasoning models (o3, o4-mini), (3) Azure AI Foundry model catalog selection, (4) Azure OpenAI SDK usage (Python, .NET, JavaScript), (5) Sora on Azure for video generation, (6) deployment SKUs (Standard, Provisioned Throughput Units, Global Standard, DataZone), (7) regional availability and quota management, (8) content filters and safety policies, (9) on-your-data scenarios with retrieval, (10) embedding models and vector search. Provides: model selection matrix, SKU/quota guidance, SDK setup recipes, content-filter configuration, and on-your-data patterns.

Azure OpenAI Service - 2025 Models and Features

Complete knowledge base for Azure OpenAI Service with latest 2025 models including GPT-5, GPT-4.1, reasoning models, and Azure AI Foundry integration.

Overview

Azure OpenAI Service provides REST API access to OpenAI's most powerful models with enterprise-grade security, compliance, and regional availability.

Latest Models (2025)

GPT-5 Series (GA August 2025)

Registration Required Models:

• gpt-5-pro: Highest capability, complex reasoning
• gpt-5: Balanced performance and cost
• gpt-5-codex: Optimized for code generation

No Registration Required:

• gpt-5-mini: Faster, more affordable
• gpt-5-nano: Ultra-fast for simple tasks
• gpt-5-chat: Optimized for conversational use

GPT-4.1 Series

• gpt-4.1: 1 million token context window
• gpt-4.1-mini: Efficient version with 1M context
• gpt-4.1-nano: Fastest variant

Key Improvements:

• 1,000,000 token context (vs 128K in GPT-4 Turbo)
• Better instruction following
• Reduced hallucinations
• Improved multilingual support

Reasoning Models

o4-mini: Lightweight reasoning model

• Faster inference
• Lower cost
• Suitable for structured reasoning tasks

o3: Advanced reasoning model

• Complex problem solving
• Mathematical reasoning
• Scientific analysis

o1: Original reasoning model

• General-purpose reasoning
• Step-by-step explanations

o1-mini: Efficient reasoning

• Balanced cost and performance

Image Generation

GPT-image-1 (2025-04-15)

• DALL-E 3 successor
• Higher quality images
• Better prompt understanding
• Improved safety filters

Video Generation

Sora (2025-05-02)

• Text-to-video generation
• Realistic and imaginative scenes
• Up to 60 seconds of video
• Multiple camera angles and styles

Audio Models

gpt-4o-transcribe: Speech-to-text powered by GPT-4o

• High accuracy transcription
• Multiple languages
• Speaker diarization

gpt-4o-mini-transcribe: Faster, more affordable transcription

• Good accuracy
• Lower latency
• Cost-effective

Deploying Azure OpenAI

Create Azure OpenAI Resource

# Create OpenAI account
az cognitiveservices account create \
  --name myopenai \
  --resource-group MyRG \
  --kind OpenAI \
  --sku S0 \
  --location eastus \
  --custom-domain myopenai \
  --public-network-access Disabled \
  --identity-type SystemAssigned

# Get endpoint and key
az cognitiveservices account show \
  --name myopenai \
  --resource-group MyRG \
  --query "properties.endpoint" \
  --output tsv

az cognitiveservices account keys list \
  --name myopenai \
  --resource-group MyRG \
  --query "key1" \
  --output tsv

Deploy GPT-5 Model

# Deploy gpt-5
az cognitiveservices account deployment create \
  --resource-group MyRG \
  --name myopenai \
  --deployment-name gpt-5 \
  --model-name gpt-5 \
  --model-version latest \
  --model-format OpenAI \
  --sku-name Standard \
  --sku-capacity 100 \
  --scale-type Standard

# Deploy gpt-5-pro (requires registration)
az cognitiveservices account deployment create \
  --resource-group MyRG \
  --name myopenai \
  --deployment-name gpt-5-pro \
  --model-name gpt-5-pro \
  --model-version latest \
  --model-format OpenAI \
  --sku-name Standard \
  --sku-capacity 50

Deploy Reasoning Models

# Deploy o3 reasoning model
az cognitiveservices account deployment create \
  --resource-group MyRG \
  --name myopenai \
  --deployment-name o3-reasoning \
  --model-name o3 \
  --model-version latest \
  --model-format OpenAI \
  --sku-name Standard \
  --sku-capacity 50

# Deploy o4-mini
az cognitiveservices account deployment create \
  --resource-group MyRG \
  --name myopenai \
  --deployment-name o4-mini \
  --model-name o4-mini \
  --model-version latest \
  --model-format OpenAI \
  --sku-name Standard \
  --sku-capacity 100

Deploy GPT-4.1 with 1M Context

az cognitiveservices account deployment create \
  --resource-group MyRG \
  --name myopenai \
  --deployment-name gpt-4-1 \
  --model-name gpt-4.1 \
  --model-version latest \
  --model-format OpenAI \
  --sku-name Standard \
  --sku-capacity 100

Deploy Image Generation Model

az cognitiveservices account deployment create \
  --resource-group MyRG \
  --name myopenai \
  --deployment-name image-gen \
  --model-name gpt-image-1 \
  --model-version 2025-04-15 \
  --model-format OpenAI \
  --sku-name Standard \
  --sku-capacity 10

Deploy Sora Video Generation

az cognitiveservices account deployment create \
  --resource-group MyRG \
  --name myopenai \
  --deployment-name sora \
  --model-name sora \
  --model-version 2025-05-02 \
  --model-format OpenAI \
  --sku-name Standard \
  --sku-capacity 5

Using Azure OpenAI Models

Python SDK (GPT-5)

from openai import AzureOpenAI
import os

# Initialize client
client = AzureOpenAI(
    api_key=os.getenv("AZURE_OPENAI_API_KEY"),
    api_version="2025-02-01-preview",
    azure_endpoint=os.getenv("AZURE_OPENAI_ENDPOINT")
)

# GPT-5 completion
response = client.chat.completions.create(
    model="gpt-5",  # deployment name
    messages=[
        {"role": "system", "content": "You are a helpful AI assistant."},
        {"role": "user", "content": "Explain quantum computing in simple terms."}
    ],
    max_tokens=1000,
    temperature=0.7,
    top_p=0.95
)

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

Python SDK (o3 Reasoning Model)

# o3 reasoning with chain-of-thought
response = client.chat.completions.create(
    model="o3-reasoning",
    messages=[
        {"role": "system", "content": "You are an expert problem solver. Show your reasoning step-by-step."},
        {"role": "user", "content": "If a train travels 120 km in 2 hours, then speeds up to travel 180 km in the next 2 hours, what is the average speed for the entire journey?"}
    ],
    max_tokens=2000,
    temperature=0.2  # Lower temperature for reasoning tasks
)

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

Python SDK (GPT-4.1 with 1M Context)

# Read a large document
with open('large_document.txt', 'r') as f:
    document = f.read()

# GPT-4.1 can handle up to 1M tokens
response = client.chat.completions.create(
    model="gpt-4-1",
    messages=[
        {"role": "system", "content": "You are a document analysis expert."},
        {"role": "user", "content": f"Analyze this document and provide key insights:\n\n{document}"}
    ],
    max_tokens=4000
)

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

Image Generation (GPT-image-1)

# Generate image with DALL-E 3 successor
response = client.images.generate(
    model="image-gen",
    prompt="A futuristic city with flying cars and vertical gardens, cyberpunk style, highly detailed, 4K",
    size="1024x1024",
    quality="hd",
    n=1
)

image_url = response.data[0].url
print(f"Generated image: {image_url}")

Video Generation (Sora)

# Generate video with Sora
response = client.videos.generate(
    model="sora",
    prompt="A serene lakeside at sunset with birds flying overhead and gentle waves on the shore",
    duration=10,  # seconds
    resolution="1080p",
    fps=30
)

video_url = response.data[0].url
print(f"Generated video: {video_url}")

Audio Transcription

# Transcribe audio file
audio_file = open("meeting_recording.mp3", "rb")

response = client.audio.transcriptions.create(
    model="gpt-4o-transcribe",
    file=audio_file,
    language="en",
    response_format="verbose_json"
)

print(f"Transcription: {response.text}")
print(f"Duration: {response.duration}s")

# Speaker diarization
for segment in response.segments:
    print(f"[{segment.start}s - {segment.end}s] {segment.text}")

Azure AI Foundry Integration

Model Router (Automatic Model Selection)

from azure.ai.foundry import ModelRouter

# Initialize model router
router = ModelRouter(
    endpoint=os.getenv("AZURE_OPENAI_ENDPOINT"),
    credential=os.getenv("AZURE_OPENAI_API_KEY")
)

# Automatically select optimal model
response = router.complete(
    prompt="Analyze this complex scientific paper...",
    optimization_goals=["quality", "cost"],
    available_models=["gpt-5", "gpt-5-mini", "gpt-4-1"]
)

print(f"Selected model: {response.model_used}")
print(f"Response: {response.content}")
print(f"Cost: ${response.cost}")

Benefits:

• Automatic model selection based on prompt complexity
• Balance quality vs cost
• Reduce costs by up to 40% while maintaining quality

Agentic Retrieval (Azure AI Search Integration)

from azure.search.documents import SearchClient
from azure.core.credentials import AzureKeyCredential

# Initialize search client
search_client = SearchClient(
    endpoint=os.getenv("SEARCH_ENDPOINT"),
    index_name="documents",
    credential=AzureKeyCredential(os.getenv("SEARCH_KEY"))
)

# Agentic retrieval with Azure OpenAI
response = client.chat.completions.create(
    model="gpt-5",
    messages=[
        {"role": "system", "content": "You have access to a document search system."},
        {"role": "user", "content": "What are the company's revenue projections for Q3?"}
    ],
    tools=[{
        "type": "function",
        "function": {
            "name": "search_documents",
            "description": "Search company documents",
            "parameters": {
                "type": "object",
                "properties": {
                    "query": {"type": "string", "description": "Search query"}
                },
                "required": ["query"]
            }
        }
    }],
    tool_choice="auto"
)

# Process tool calls
if response.choices[0].message.tool_calls:
    for tool_call in response.choices[].message.tool_calls:
         tool_call.function.name == :
            query = json.loads(tool_call.function.arguments)[]
            results = search_client.search(query)
            

Improvements:

• 40% better on complex, multi-part questions
• Automatic query decomposition
• Relevance ranking
• Citation generation

Foundry Observability (Preview)

from azure.ai.foundry import FoundryObservability

# Enable observability
observability = FoundryObservability(
    workspace_id=os.getenv("AI_FOUNDRY_WORKSPACE_ID"),
    enable_tracing=True,
    enable_metrics=True
)

# Monitor agent execution
with observability.trace_agent("customer_support_agent") as trace:
    response = client.chat.completions.create(
        model="gpt-5",
        messages=messages
    )

    trace.log_tool_call("search_kb", {"query": "refund policy"})
    trace.log_reasoning_step("Retrieved refund policy document")
    trace.log_token_usage(response.usage.total_tokens)

# View in Azure AI Foundry portal:
# - End-to-end trace logs
# - Reasoning steps and tool calls
# - Performance metrics
# - Cost analysis

Capacity and Quota Management

Check Quota

# List deployments with usage
az cognitiveservices account deployment list \
  --resource-group MyRG \
  --name myopenai \
  --output table

# Check usage metrics
az monitor metrics list \
  --resource $(az cognitiveservices account show -g MyRG -n myopenai --query id -o tsv) \
  --metric "TokenTransaction" \
  --start-time 2025-01-01T00:00:00Z \
  --end-time 2025-01-31T23:59:59Z \
  --interval PT1H \
  --aggregation Total

Update Capacity

# Scale up deployment capacity
az cognitiveservices account deployment update \
  --resource-group MyRG \
  --name myopenai \
  --deployment-name gpt-5 \
  --sku-capacity 200

# Scale down during off-peak
az cognitiveservices account deployment update \
  --resource-group MyRG \
  --name myopenai \
  --deployment-name gpt-5 \
  --sku-capacity 50

Request Quota Increase

1. Navigate to Azure Portal → Azure OpenAI resource
2. Go to "Quotas" blade
3. Select model and region
4. Click "Request quota increase"
5. Provide justification and target capacity

Security and Networking

Private Endpoint

# Create private endpoint
az network private-endpoint create \
  --name openai-private-endpoint \
  --resource-group MyRG \
  --vnet-name MyVNet \
  --subnet PrivateEndpointSubnet \
  --private-connection-resource-id $(az cognitiveservices account show -g MyRG -n myopenai --query id -o tsv) \
  --group-id account \
  --connection-name openai-connection

# Create private DNS zone
az network private-dns zone create \
  --resource-group MyRG \
  --name privatelink.openai.azure.com

# Link to VNet
az network private-dns link vnet create \
  --resource-group MyRG \
  --zone-name privatelink.openai.azure.com \
  --name openai-dns-link \
  --virtual-network MyVNet \
  --registration-enabled false

# Create DNS zone group
az network private-endpoint dns-zone-group create \
  --resource-group MyRG \
  --endpoint-name openai-private-endpoint \
  --name default \
  --private-dns-zone privatelink.openai.azure.com \
  --zone-name privatelink.openai.azure.com

Managed Identity Access

# Enable system-assigned identity
az cognitiveservices account identity assign \
  --name myopenai \
  --resource-group MyRG

# Grant role to managed identity
PRINCIPAL_ID=$(az cognitiveservices account show -g MyRG -n myopenai --query identity.principalId -o tsv)

az role assignment create \
  --assignee $PRINCIPAL_ID \
  --role "Cognitive Services OpenAI User" \
  --scope /subscriptions/<sub-id>/resourceGroups/MyRG

Content Filtering

# Configure content filtering
az cognitiveservices account update \
  --name myopenai \
  --resource-group MyRG \
  --set properties.customContentFilter='{
    "hate": {"severity": "medium", "enabled": true},
    "violence": {"severity": "medium", "enabled": true},
    "sexual": {"severity": "medium", "enabled": true},
    "selfHarm": {"severity": "high", "enabled": true}
  }'

Cost Optimization

Model Selection Strategy

Use GPT-5-mini or GPT-5-nano for:

• Simple questions
• Classification tasks
• Content moderation
• Summarization

Use GPT-5 or GPT-4.1 for:

• Complex reasoning
• Long-form content generation
• Document analysis
• Code generation

Use Reasoning Models (o3, o4-mini) for:

• Mathematical problems
• Scientific analysis
• Step-by-step reasoning
• Logic puzzles

Implement Caching

# Use semantic cache to reduce duplicate requests
from azure.ai.cache import SemanticCache

cache = SemanticCache(
    similarity_threshold=0.95,
    ttl_seconds=3600
)

# Check cache before API call
cached_response = cache.get(user_query)
if cached_response:
    return cached_response

response = client.chat.completions.create(
    model="gpt-5",
    messages=messages
)

cache.set(user_query, response)

Token Management

import tiktoken

# Count tokens before API call
encoding = tiktoken.get_encoding("cl100k_base")
tokens = len(encoding.encode(prompt))

if tokens > 100000:
    print(f"Warning: Prompt has {tokens} tokens, this will be expensive!")

# Use shorter max_tokens when appropriate
response = client.chat.completions.create(
    model="gpt-5",
    messages=messages,
    max_tokens=500  # Limit output tokens
)

Monitoring and Alerts

Set Up Cost Alerts

# Create budget alert
az consumption budget create \
  --budget-name openai-monthly-budget \
  --resource-group MyRG \
  --amount 1000 \
  --category Cost \
  --time-grain Monthly \
  --start-date 2025-01-01 \
  --end-date 2025-12-31 \
  --notifications '{
    "actual_GreaterThan_80_Percent": {
      "enabled": true,
      "operator": "GreaterThan",
      "threshold": 80,
      "contactEmails": ["[e-mail]"]
    }
  }'

Application Insights Integration

from opencensus.ext.azure.log_exporter import AzureLogHandler
import logging

# Configure logging
logger = logging.getLogger(__name__)
logger.addHandler(AzureLogHandler(
    connection_string=os.getenv("APPLICATIONINSIGHTS_CONNECTION_STRING")
))

# Log API calls
logger.info("OpenAI API call", extra={
    "custom_dimensions": {
        "model": "gpt-5",
        "tokens": response.usage.total_tokens,
        "cost": calculate_cost(response.usage.total_tokens),
        "latency_ms": response.response_ms
    }
})

Best Practices

✓ Use Model Router for automatic cost optimization ✓ Implement caching to reduce duplicate requests ✓ Monitor token usage and set budgets ✓ Use private endpoints for production workloads ✓ Enable managed identity instead of API keys ✓ Configure content filtering for safety ✓ Right-size capacity based on actual demand ✓ Use Foundry Observability for monitoring ✓ Implement retry logic with exponential backoff ✓ Choose appropriate models for task complexity

References

• Azure OpenAI Documentation
• What's New in Azure OpenAI
• GPT-5 Announcement
• Azure AI Foundry
• Model Pricing

Azure OpenAI Service with GPT-5 and reasoning models brings enterprise-grade AI to your applications!