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Azure Video Generation

LiteLLM supports Azure OpenAI's video generation models including Sora with full end-to-end integration.

PropertyDetails
DescriptionAzure OpenAI's video generation models including Sora-2
Provider Route on LiteLLMazure/
Supported Modelssora-2
Cost Tracking✅ Duration-based pricing ($0.10/second)
Logging Support✅ Full request/response logging
Guardrails Support✅ Content moderation and safety checks
Proxy Server Support✅ Full proxy integration with virtual keys
Spend Management✅ Budget tracking and rate limiting
Link to Provider DocAzure OpenAI Video Generation ↗

Quick Start​

Required API Keys​

import os 
os.environ["AZURE_OPENAI_API_KEY"] = "your-azure-api-key"
os.environ["AZURE_OPENAI_API_BASE"] = "https://your-resource.openai.azure.com/"
os.environ["AZURE_OPENAI_API_VERSION"] = "2024-02-15-preview"

Basic Usage​

from litellm import video_generation, video_status, video_content
import os
import time

os.environ["AZURE_OPENAI_API_KEY"] = "your-azure-api-key"
os.environ["AZURE_OPENAI_API_BASE"] = "https://your-resource.openai.azure.com/"
os.environ["AZURE_OPENAI_API_VERSION"] = "2024-02-15-preview"

# Generate video
response = video_generation(
    model="azure/sora-2",
    prompt="A cat playing with a ball of yarn in a sunny garden",
    seconds="8",
    size="720x1280"
)

print(f"Video ID: {response.id}")
print(f"Initial Status: {response.status}")

# Check status until video is ready
while True:
    status_response = video_status(
        video_id=response.id,
        model="azure/sora-2"
    )
    
    print(f"Current Status: {status_response.status}")
    
    if status_response.status == "completed":
        break
    elif status_response.status == "failed":
        print("Video generation failed")
        break
    
    time.sleep(10)  # Wait 10 seconds before checking again

# Download video content when ready
video_bytes = video_content(
    video_id=response.id,
    model="azure/sora-2"
)

# Save to file
with open("generated_video.mp4", "wb") as f:
    f.write(video_bytes)

Usage - LiteLLM Proxy Server​

Here's how to call Azure video generation models with the LiteLLM Proxy Server

1. Save key in your environment​

export AZURE_OPENAI_API_KEY="your-azure-api-key"
export AZURE_OPENAI_API_BASE="https://your-resource.openai.azure.com/"
export AZURE_OPENAI_API_VERSION="2024-02-15-preview"

2. Start the proxy​

model_list:
  - model_name: azure-sora-2
    litellm_params:
      model: azure/sora-2
      api_key: os.environ/AZURE_OPENAI_API_KEY
      api_base: os.environ/AZURE_OPENAI_API_BASE
      api_version: "2024-02-15-preview"

3. Test it​

curl --location 'http://0.0.0.0:4000/videos/generations' \
--header 'Content-Type: application/json' \
--header 'Authorization: Bearer sk-1234' \
--data '{
    "model": "azure-sora-2",
    "prompt": "A cat playing with a ball of yarn in a sunny garden",
    "seconds": "8",
    "size": "720x1280"
}'

Supported Models​

Model Name
sora-2
sora-2-pro
sora-2-pro-high-res

Logging & Observability​

Request/Response Logging​

All video generation requests are automatically logged with:

  • Request details: prompt, model, duration, size
  • Response details: video ID, status, creation time
  • Cost tracking: duration-based pricing calculation
  • Performance metrics: request latency, processing time

Logging Providers​

Video generation works with all LiteLLM logging providers:

  • Datadog: Real-time monitoring and alerting
  • Helicone: Request tracing and debugging
  • LangSmith: LangChain integration and tracing
  • Custom webhooks: Send logs to your own endpoints

Example: Enable Datadog logging

general_settings:
  alerting: ["datadog"]
  datadog_api_key: os.environ/DATADOG_API_KEY

Video Generation Parameters​

  • prompt (required): Text description of the desired video
  • model (optional): Model to use, defaults to "azure/sora-2"
  • seconds (optional): Video duration in seconds (e.g., "8", "16")
  • size (optional): Video dimensions (e.g., "720x1280", "1280x720")
  • input_reference (optional): Reference image for video editing
  • user (optional): User identifier for tracking

Video Content Retrieval​

# Download video content
video_bytes = video_content(
    video_id="video_1234567890",
    model="azure/sora-2"
)

# Save to file
with open("video.mp4", "wb") as f:
    f.write(video_bytes)

Complete Workflow​

import litellm
import time

def generate_and_download_video(prompt):
    # Step 1: Generate video
    response = litellm.video_generation(
        prompt=prompt,
        model="azure/sora-2",
        seconds="8",
        size="720x1280"
    )
    
    video_id = response.id
    print(f"Video ID: {video_id}")
    
    # Step 2: Wait for processing (in practice, poll status)
    time.sleep(30)
    
    # Step 3: Download video
    video_bytes = litellm.video_content(
        video_id=video_id,
        model="azure/sora-2"
    )
    
    # Step 4: Save to file
    with open(f"video_{video_id}.mp4", "wb") as f:
        f.write(video_bytes)
    
    return f"video_{video_id}.mp4"

# Usage
video_file = generate_and_download_video(
    "A cat playing with a ball of yarn in a sunny garden"
)

Video Remix (Video Editing)​

# Video editing with reference image
response = litellm.video_remix(
    prompt="Make the cat jump higher",
    input_reference=open("path/to/image.jpg", "rb"),  # Reference image as file object
    model="azure/sora-2",
    seconds="8"
)

print(f"Video ID: {response.id}")

Error Handling​

from litellm.exceptions import BadRequestError, AuthenticationError

try:
    response = video_generation(
        prompt="A cat playing with a ball of yarn",
        model="azure/sora-2"
    )
except AuthenticationError as e:
    print(f"Authentication failed: {e}")
except BadRequestError as e:
    print(f"Bad request: {e}")