> ## Documentation Index
> Fetch the complete documentation index at: https://mintlify.com/portkey-AI/gateway/llms.txt
> Use this file to discover all available pages before exploring further.
# Azure OpenAI
> Integrate OpenAI models through Microsoft Azure with enterprise-grade security and compliance
## 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/openai`
## Supported 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
```python Python theme={null}
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)
```
```javascript JavaScript theme={null}
import Portkey from 'portkey-ai';
const client = new Portkey({
provider: "azure-openai",
apiKey: "***", // Azure API key
resourceName: "my-resource", // Your Azure resource name
deploymentId: "gpt-4-deployment", // Your deployment name
apiVersion: "2024-02-15-preview" // API version
});
const response = await client.chat.completions.create({
model: "gpt-4",
messages: [
{role: "system", content: "You are a helpful assistant."},
{role: "user", content: "What is Azure OpenAI?"}
]
});
console.log(response.choices[0].message.content);
```
```bash cURL theme={null}
curl https://my-resource.openai.azure.com/openai/deployments/gpt-4-deployment/chat/completions?api-version=2024-02-15-preview \
-H "Content-Type: application/json" \
-H "api-key: ***" \
-d '{
"messages": [
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": "What is Azure OpenAI?"}
]
}'
```
## Configuration Options
### Required Parameters
| Parameter | Description | Example |
| --------------- | -------------------------- | -------------------- |
| `resource_name` | Azure OpenAI resource name | `my-openai-resource` |
| `deployment_id` | Deployment name in Azure | `gpt-4-deployment` |
| `api_version` | Azure API version | `2024-02-15-preview` |
| `api_key` | Azure API key | `***` |
### Authentication Methods
Azure OpenAI supports multiple authentication methods:
#### 1. API Key (Default)
```python theme={null}
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
```python theme={null}
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)
```python theme={null}
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
```python theme={null}
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)
```python theme={null}
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-preview` | Latest features, GPT-4 Turbo | Preview |
| `2023-12-01-preview` | GPT-4 Vision, DALL-E 3 | Preview |
| `2023-05-15` | Stable release | GA |
| `v1` | OpenAI-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-4` | Advanced reasoning |
| GPT-4 Turbo | `gpt-4-turbo` | 128K context |
| GPT-3.5 Turbo | `gpt-35-turbo` | Fast, cost-effective |
| text-embedding-ada-002 | `text-embedding-ada-002` | Embeddings |
| DALL-E 3 | `dall-e-3` | Image generation |
| Whisper | `whisper` | Speech-to-text |
## Advanced Features
### Streaming
```python theme={null}
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
```python theme={null}
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
```python theme={null}
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
```python theme={null}
response = client.embeddings.create(
model="text-embedding-ada-002",
input="Azure OpenAI provides enterprise-grade AI"
)
embedding = response.data[0].embedding
```
### Image Generation
```python theme={null}
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:
```python theme={null}
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:
```python theme={null}
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
```python theme={null}
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` parameter | `deployment_id` |
| API 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
1. **Use Managed Identity** - Most secure for Azure-hosted applications
2. **Deploy to multiple regions** - Better availability and latency
3. **Set appropriate api\_version** - Use stable versions for production
4. **Monitor quota limits** - Azure has per-deployment quotas
5. **Use private endpoints** - Enhanced security for enterprise
6. **Implement retry logic** - Handle transient failures
7. **Cache responses** - Reduce costs and latency
## Enterprise Features
* **Private Endpoints**: Connect via Azure Private Link
* **Customer Managed Keys**: Bring your own encryption keys
* **Virtual Networks**: Restrict access to your VNet
* **Managed Identity**: Eliminate credential management
* **Azure Monitor**: Full observability integration
* **Compliance**: HIPAA, SOC 2, ISO 27001, GDPR
* **Data Residency**: Keep data in your region
## Pricing
Azure OpenAI pricing is similar to OpenAI but billed through Azure:
View Azure OpenAI Service pricing
## Related Resources
Standard OpenAI integration
Multi-region load balancing
Fallback configurations
Enterprise deployment guide