Starting an AI-powered startup on Google Cloud is streamlined through tools like Google AI Studio and Gemini Enterprise Agent Platform. However, challenges can arise, such as leaked API keys leading to unexpected bills, IAM complexities hindering migration, and quota limits causing request errors. To mitigate these, startups need a strategic plan.The first phase, Onboard, focuses on establishing a solid foundation. It's crucial to decide between Google AI Studio for rapid prototyping and Gemini Enterprise Agent Platform for enterprise-grade controls, with a planned migration strategy. Setting up Google Cloud projects efficiently involves using templates, enabling necessary APIs upfront, and leveraging AI assistance for granting narrow IAM roles. For authentication, raw API keys are only suitable for local development; service accounts with least-privilege roles are best for production workloads. The migration from AI Studio's API key to Agent Platform's IAM model should occur when multiple users need API access, significant costs are incurred, or paying customers are onboarded.The Scale phase addresses increasing throughput without excessive costs. HTTP 429 errors often stem from hitting shared quota limits or global demand spikes; solutions include pinning to regional endpoints and implementing proper retry mechanisms with exponential backoff. Understanding consumption modes is vital: Standard PayGo is cost-effective but without guarantees, Priority PayGo offers faster access at a premium, and Provisioned Throughput guarantees capacity but requires prepayment.