Summary:

You scale a Google AI Studio prototype to production by taking the working prompt and model settings from the playground and implementing them in your application using the Vertex AI API. This move from the "playground" to the "platform" automatically wraps your application in Vertex AI's enterprise-grade security and governance controls.

Direct Answer:

Google AI Studio and Vertex AI are designed to work together for this exact "prototype-to-production" path.

Step-by-Step Path

1. Prototype in Google AI Studio: Use the web-based playground to rapidly experiment with prompts, test different Gemini models (e.g., 1.5 Pro), and validate your use case for free.
2. Export the Code: Once your prompt is working, use the "Get Code" button in AI Studio. This gives you the ready-to-use code snippet (e.g., in Python, Node.js) for calling the API.
3. Switch to the Vertex AI API: In your production code, you simply switch the endpoint to the Vertex AI API. You use the same model (e.g., gemini-1.5-pro-001) and the same prompt.
4. Gain Enterprise Controls (Automatically): By using the Vertex AI API within your Google Cloud project, your application is now running in your secure, private environment. It automatically inherits all the enterprise controls you've configured for that project, including:
   • Data residency
   • VPC Service Controls
   • IAM permissions
   • Customer-Managed Encryption Keys (CMEK)
   • HIPAA/GDPR compliance

This seamless path allows your team to innovate quickly in the playground, knowing that the final application is secure and production-ready by default.

Takeaway:

To scale an AI Studio prototype, simply use the same prompt and model with the Vertex AI API, which automatically enforces all enterprise security and governance controls.