It’s never been easier to start an AI-powered startup on Google Cloud. You grab an API key from Google AI Studio at breakfast, paste it into Antigravity, and by lunch you’ll have a nascent prototype of your product. But it’s not all one straight line to progress. It's common to bump into these three challenges as you build out your stack: A "quick" migration from AI Studio to Gemini Enterprise Agent Platform stalls the roadmap for weeks because nobody on the team owns Identity and Access Management (IAM).
The launch works, until the app starts returning HTTP 429 Too Many Requests because of default per-project quotas, and there's no clean path to more capacity without paying a premium. None of these are unique edge cases. They're default failure modes of moving fast without a plan, and we've all done it at least once.
Below are the 10 questions every startup should be ready to answer before they scale, grouped into the three phases where decisions can shape your future: Onboard (setting up your own projects and identities right) Scale (getting more throughput without breaking the bank) Govern (keeping costs, keys, and agents from running away). Each question ends with a short, runnable snippet you can copy into your own project today. These ten are scoped to the prototype-to-production transition itself.
Adjacent decisions that matter just as much but aren't specific to that move, your data layer and RAG architecture, CI/CD, network design, are deliberately out of frame here. Both surfaces expose the same Gemini family of models, but they solve different problems. Google AI Studio (with the Gemini Developer API) is the fastest path from an idea to working code. A browser IDE, an API key, a generous free tier, and no cloud project to configure. It's where most ideas should start, and Google's own guidance says as much.