Implementing a Fallback Strategy for Experimental Vertex AI Models

When integrating experimental AI models into your application, there's always a risk that they may become unavailable due to frequent updates, deprecations, or API changes. To mitigate this risk and enhance the resilience and operational stability of your application, having a well-planned fallback mechanism using a Generally Available (GA) model can be highly effective.

This blog post explores the advantages of maintaining a fallback model strategy in Vertex AI and provides an implementation guide using Python.

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Why a Fallback Model is Essential

1. Ensuring Service Continuity

Experimental models can sometimes be temporarily or permanently deprecated. Having a GA model as a backup allows your application to continue running without interruptions.

2. Handling API Changes & Compatibility Issues

Experimental models undergo frequent API updates that may introduce breaking changes. GA models, on the other hand, offer a more stable and backward-compatible alternative.

3. Maintaining Output Quality & Stability

Experimental models may produce unpredictable or inconsistent outputs. A GA model ensures a baseline of output quality when the experimental model fails.

4. Managing Costs Effectively

GA models are often more cost-effective. You may choose to use the experimental model only for specific high-value use cases while keeping the GA model as the default option.

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Considerations When Implementing a Fallback Strategy

Automatic Failover Handling

Your application should detect API failures such as:

• 404 Not Found (Model deprecated or removed)
• 500 Internal Server Error (Service outage)
• Rate-limiting issues (429 Too Many Requests)

When such failures occur, your system should automatically switch to a GA model.

Note on Rate Limits and Fallback Strategy for Error 429

When applying a fallback strategy for handling error 429 (Too Many Requests), be aware that it may not always be effective if both the experimental and GA models share the same base model. For example, gemini-2.0-flash-thinking-exp-01-21 and gemini-2.0-flash are both based on gemini-2.0-flash. In Gemini models, rate limits are not only applied to individual models but also to the underlying base model.

This means that if you attempt to switch to another model that shares the same base model, you might still be subject to the same rate limit, rendering the fallback ineffective.

For more details, refer to the official documentation: Vertex AI Quotas.

Handling Model Output Differences

Experimental and GA models may generate different responses. Implementing pre-processing and post-processing logic can help normalize outputs.

Parallel Testing Before Deployment

To prevent unexpected issues in production, test both models in parallel and evaluate their responses to ensure the fallback model meets your requirements.

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Python Implementation: Fallback from Experimental to GA Model

Here's how you can implement a fallback strategy using Vertex AI's generative models in Python:

from vertexai.generative_models import GenerativeModel, GenerationConfig

def predict_with_fallback(prompt: str):
    models = ["gemini-2.0-flash-thinking-exp-01-21", "gemini-2.0-flash"]  # Experimental first, then GA model
    config = GenerationConfig(
        temperature=1.0,
        max_output_tokens=1024
    )

    for model in models:
        try:
            print(f"Trying model: {model}")
            response = GenerativeModel(model).generate_content(
                contents=prompt, generation_config=config
            )
            print("Success with model:", model)
            return response.text
        except Exception as e:
            print(f"Model {model} failed: {e}")
            continue  # Fall back to the next model

    print("All models failed.")
    return None

# Example usage
prompt = "Explain the significance of Kubernetes in modern cloud computing."

result = predict_with_fallback(prompt)
if result:
    print("Generated text:", result)
else:
    print("Failed to generate text with all models.")

How This Works

1. Prioritizes the experimental model (gemini-2.0-flash-thinking-exp-01-21).
2. If it fails, falls back to the GA model (gemini-2.0-flash).
3. Handles API errors and exceptions to ensure continuous operation.
4. Prints logs to track which model is being used.

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Conclusion

Using an experimental AI model without a fallback mechanism is risky, as these models frequently change or become unavailable. By implementing a fallback strategy with a stable GA model, you ensure:

• Seamless service continuity
• Consistent API compatibility
• Quality assurance in generated outputs
• Cost-effective AI usage

When designing AI-driven applications, always plan for model unavailability scenarios. A structured fallback mechanism allows your system to adapt dynamically while maintaining a high-quality user experience.