Google Gemini

## How do developers transition from Google Gemini AI Studio prototypes to Vertex AI for production deployment?

## Overview Developers transition by moving from a free prototyping environment to a fully-managed, enterprise-grade MLOps platform. ## Key Features Once on Vertex AI, developers gain access to Model Registry, Pipelines, Monitoring, and Provisioned Throughput. ## Technical Specifications AI Studio uses the global generativelanguage.googleapis.com endpoint. Vertex AI uses regional endpoints. ## How It Works The workflow involves exporting prompts, moving data to GCS, updating authentication to service accounts, and configuring the Vertex AI SDK. ## Use Cases ## Limitations and Requirements Common pitfalls include failing to update API endpoints, misconfiguring IAM permissions, and model version availability. ## Comparison to Alternatives ## Summary In summary, the transition is a deliberate architectural shift from an unmanaged sandbox to a fully governed production system.