Google Veo 2

Fast Inference

REST API

Model Information

Response Time:~40 sec

Status:Active

Version:

0.0.1

Updated:8 days ago

veo-2

Live Demo

Average runtime: ~40 seconds

Input

Configure model parameters

Prompt

Text prompt for video generation

A chubby baby duck wearing a tiny backpack waddles confidently across a rainbow-colored crosswalk as bubbles float around it

Output

View generated results

Result

Preview, share or download your results with a single click.

Each execution costs $0.5 With $1 you can run this model about 2 times.

API Reference

View Full Documentation

Prerequisites

Create an API Key from the Eachlabs Console
Install the required dependencies for your chosen language (e.g., requests for Python)

API Integration Steps

1. Create a Prediction

Send a POST request to create a new prediction. This will return a prediction ID that you'll use to check the result. The request should include your model inputs and API key.

import requests
import time

API_KEY = "YOUR_API_KEY"  # Replace with your API key
HEADERS = {
    "X-API-Key": API_KEY,
    "Content-Type": "application/json"
}

def create_prediction():
    response = requests.post(
        "https://api.eachlabs.ai/v1/prediction/",
        headers=HEADERS,
        json={
            "model": "veo-2",
            "version": "0.0.1",
            "input": {
  "duration": "5s",
  "aspect_ratio": "16:9",
  "prompt": "your prompt here"
},
            "webhook_url": ""
        }
    )
    prediction = response.json()
    
    if prediction["status"] != "success":
        raise Exception(f"Prediction failed: {prediction}")
    
    return prediction["predictionID"]

2. Get Prediction Result

Poll the prediction endpoint with the prediction ID until the result is ready. The API uses long-polling, so you'll need to repeatedly check until you receive a success status.

def get_prediction(prediction_id):
    while True:
        result = requests.get(
            f"https://api.eachlabs.ai/v1/prediction/{prediction_id}",
            headers=HEADERS
        ).json()
        
        if result["status"] == "success":
            return result
        elif result["status"] == "error":
            raise Exception(f"Prediction failed: {result}")
        
        time.sleep(1)  # Wait before polling again

3. Complete Example

Here's a complete example that puts it all together, including error handling and result processing. This shows how to create a prediction and wait for the result in a production environment.

try:
    # Create prediction
    prediction_id = create_prediction()
    print(f"Prediction created: {prediction_id}")
    
    # Get result
    result = get_prediction(prediction_id)
    print(f"Output URL: {result['output']}")
    print(f"Processing time: {result['metrics']['predict_time']}s")
except Exception as e:
    print(f"Error: {e}")

Additional Information

The API uses a two-step process: create prediction and poll for results
Response time: ~40 seconds
Rate limit: 60 requests/minute
Concurrent requests: 10 maximum
Use long-polling to check prediction status until completion

Related AI Models

You can seamlessly integrate advanced AI capabilities into your applications without the hassle of managing complex infrastructure.

Eachlabs | AI Workflows for app builders

Kling v1.6 Standard Text to Video

Kling v1.6 Standard Text to Video generates stable videos from text inputs, focusing on simplicity and reliability.

Seedance V1 | Pro | Text to Video

Seedance V1 Pro Text to Video is a high-quality text-to-video generation model developed by Bytedance, designed for creating cinematic and visually compelling video content.

Kling v1 Pro Text to Video

Kling v1 Pro Text to Video converts written text into high-quality videos with stable and consistent results.

Kling v1 Standard Text to Video

A text-to-video model that generates short videos from written prompts. Kling v1 Standard Text to Video , available on Eachlabs, focuses on clarity and motion consistency.