Overview Azure OpenAI Service provides REST API access to OpenAI’s models including GPT-4, GPT-3.5-Turbo, and embeddings through Microsoft Azure’s enterprise-grade infrastructure with enhanced security, compliance, and regional availability.
Base URL: https://{resourceName}.openai.azure.com/openaiSupported Features
✅ Chat Completions (including streaming)
✅ Completions (legacy)
✅ Embeddings
✅ Image Generation (DALL-E)
✅ Image Editing
✅ Text-to-Speech (TTS)
✅ Speech-to-Text (Whisper)
✅ Audio Translation
✅ Function Calling & Tools
✅ Vision (GPT-4 Vision)
✅ Batch API
✅ Fine-tuning
✅ Multiple Authentication Methods
Quick Start Basic Configuration from portkey_ai import Portkey client = Portkey( provider = "azure-openai" , api_key = "***" , # Azure API key resource_name = "my-resource" , # Your Azure resource name deployment_id = "gpt-4-deployment" , # Your deployment name api_version = "2024-02-15-preview" # API version ) response = client.chat.completions.create( model = "gpt-4" , # Not used in Azure, deployment_id is used instead messages = [ { "role" : "system" , "content" : "You are a helpful assistant." }, { "role" : "user" , "content" : "What is Azure OpenAI?" } ] ) print (response.choices[ 0 ].message.content)
Configuration Options Required Parameters Parameter Description Example resource_nameAzure OpenAI resource name my-openai-resourcedeployment_idDeployment name in Azure gpt-4-deploymentapi_versionAzure API version 2024-02-15-previewapi_keyAzure API key ***
Authentication Methods Azure OpenAI supports multiple authentication methods:
1. API Key (Default) client = Portkey( provider = "azure-openai" , api_key = "***" , resource_name = "my-resource" , deployment_id = "gpt-4" , api_version = "2024-02-15-preview" )
2. Azure AD Token client = Portkey( provider = "azure-openai" , azure_ad_token = "Bearer eyJ0eXAiOiJKV1QiLCJhbGc..." , resource_name = "my-resource" , deployment_id = "gpt-4" , api_version = "2024-02-15-preview" )
3. Entra ID (Service Principal) client = Portkey( provider = "azure-openai" , azure_auth_mode = "entra" , azure_entra_tenant_id = "***" , azure_entra_client_id = "***" , azure_entra_client_secret = "***" , azure_entra_scope = "https://cognitiveservices.azure.com/.default" , resource_name = "my-resource" , deployment_id = "gpt-4" , api_version = "2024-02-15-preview" )
4. Managed Identity client = Portkey( provider = "azure-openai" , azure_auth_mode = "managed" , azure_managed_client_id = "***" , # Optional azure_entra_scope = "https://cognitiveservices.azure.com/" , resource_name = "my-resource" , deployment_id = "gpt-4" , api_version = "2024-02-15-preview" )
5. Workload Identity (Kubernetes) client = Portkey( provider = "azure-openai" , azure_auth_mode = "workload" , azure_workload_client_id = "***" , azure_entra_scope = "https://cognitiveservices.azure.com/.default" , resource_name = "my-resource" , deployment_id = "gpt-4" , api_version = "2024-02-15-preview" ) # Requires environment variables: # AZURE_AUTHORITY_HOST # AZURE_TENANT_ID # AZURE_FEDERATED_TOKEN_FILE
API Versions Azure OpenAI uses API versions for versioning. Common versions:
API Version Features Status 2024-02-15-previewLatest features, GPT-4 Turbo Preview 2023-12-01-previewGPT-4 Vision, DALL-E 3 Preview 2023-05-15Stable release GA v1OpenAI-compatible Special
Use api_version="v1" for OpenAI-compatible endpoints without deployment IDs.
Available Deployments You must create deployments in Azure before using them:
Model Recommended Deployment Name Capabilities GPT-4 gpt-4Advanced reasoning GPT-4 Turbo gpt-4-turbo128K context GPT-3.5 Turbo gpt-35-turboFast, cost-effective text-embedding-ada-002 text-embedding-ada-002Embeddings DALL-E 3 dall-e-3Image generation Whisper whisperSpeech-to-text
Advanced Features Streaming stream = client.chat.completions.create( model = "gpt-4" , messages = [{ "role" : "user" , "content" : "Count to 5" }], stream = True ) for chunk in stream: if chunk.choices[ 0 ].delta.content: print (chunk.choices[ 0 ].delta.content, end = "" )
Function Calling tools = [ { "type" : "function" , "function" : { "name" : "get_weather" , "description" : "Get weather information" , "parameters" : { "type" : "object" , "properties" : { "location" : { "type" : "string" } }, "required" : [ "location" ] } } } ] response = client.chat.completions.create( model = "gpt-4" , messages = [{ "role" : "user" , "content" : "What's the weather in Seattle?" }], tools = tools )
Vision response = client.chat.completions.create( model = "gpt-4-vision" , 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-ada-002" , input = "Azure OpenAI provides enterprise-grade AI" ) embedding = response.data[ 0 ].embedding
Image Generation response = client.images.generate( model = "dall-e-3" , prompt = "A futuristic datacenter in the clouds" , size = "1024x1024" , quality = "hd" ) image_url = response.data[ 0 ].url
Multi-Region Configuration Load balance across multiple Azure regions:
config = { "strategy" : { "mode" : "loadbalance" }, "targets" : [ { "provider" : "azure-openai" , "api_key" : "***" , "resource_name" : "eastus-resource" , "deployment_id" : "gpt-4" , "api_version" : "2024-02-15-preview" , "weight" : 0.5 }, { "provider" : "azure-openai" , "api_key" : "***" , "resource_name" : "westus-resource" , "deployment_id" : "gpt-4" , "api_version" : "2024-02-15-preview" , "weight" : 0.5 } ] } client = Portkey().with_options( config = config)
Fallback to OpenAI Fallback to standard OpenAI if Azure is unavailable:
config = { "strategy" : { "mode" : "fallback" }, "targets" : [ { "provider" : "azure-openai" , "api_key" : "***" , "resource_name" : "my-resource" , "deployment_id" : "gpt-4" , "api_version" : "2024-02-15-preview" }, { "provider" : "openai" , "api_key" : "sk-***" , "override_params" : { "model" : "gpt-4" } } ] } client = Portkey().with_options( config = config)
Error Handling from portkey_ai.exceptions import ( RateLimitError, APIError, AuthenticationError ) try : response = client.chat.completions.create( model = "gpt-4" , messages = [{ "role" : "user" , "content" : "Hello" }] ) except RateLimitError as e: print ( f "Rate limit: { e } " ) except AuthenticationError as e: print ( f "Auth error: { e } " ) except APIError as e: print ( f "API error: { e } " )
Key Differences from OpenAI Aspect OpenAI Azure OpenAI Authentication API key API key, AD token, Managed Identity Endpoint Fixed Custom resource name Model Specification model parameterdeployment_idAPI Versioning Not required Required api_version Regional Global Multi-region support Compliance Standard Enterprise (HIPAA, SOC 2, etc.) Data Location US Choose your region
Request URL Structure https://{resource_name}.openai.azure.com/openai/deployments/{deployment_id}/{endpoint}?api-version={api_version}
Example:
https://my-resource.openai.azure.com/openai/deployments/gpt-4/chat/completions?api-version=2024-02-15-preview
Best Practices
Use Managed Identity - Most secure for Azure-hosted applicationsDeploy to multiple regions - Better availability and latencySet appropriate api_version - Use stable versions for productionMonitor quota limits - Azure has per-deployment quotasUse private endpoints - Enhanced security for enterpriseImplement retry logic - Handle transient failuresCache responses - Reduce costs and latency
Enterprise Features
Private Endpoints : Connect via Azure Private LinkCustomer Managed Keys : Bring your own encryption keysVirtual Networks : Restrict access to your VNetManaged Identity : Eliminate credential managementAzure Monitor : Full observability integrationCompliance : HIPAA, SOC 2, ISO 27001, GDPRData Residency : Keep data in your region
Pricing Azure OpenAI pricing is similar to OpenAI but billed through Azure:
Azure OpenAI Pricing View Azure OpenAI Service pricing
OpenAI Standard OpenAI integration
Load Balancing Multi-region load balancing
Fallbacks Fallback configurations
Enterprise Deployment Enterprise deployment guide