From Google AI Studio or Gemini to a real app: deploying API, frontend, and data

Published on 2026-10-27 by frfrand

Taking a Gemini/Google AI Studio app to production: hiding API keys, controlling costs, quotas, files, backend and server.

From Google AI Studio or Gemini to a real app: deploying API, frontend, and data

What's missing after generating a demo with Gemini?

That the button works once doesn't mean you can charge for the service. Many demos call AI models with a key in the test environment and use temporary storage. When real users come in, you need to protect credentials, control costs per request, limit files, log errors, and decide what data the system retains.

Do not publish a private key on the frontend

Variables included in browser bundles can be readable by anyone, even if their names seem secret. If your product uses an external AI API, the usual approach is to make the call from a backend under your control, apply quotas, and validate the user's identity. Additionally, model results can be incorrect or contain malicious instructions: they should not be treated as trusted code or as final decisions without validation.

Example: contract summarizer

A startup builds an interface where a PDF is uploaded and Gemini prepares a summary. In staging, you check document size, per-account limits, privacy rules, data deletion, and error handling if the quota is exhausted. The backend receives the request, verifies permissions, calls the provider with a private credential, and returns a controlled response. The frontend only shows progress and the summary. A user cannot request summaries of another user's private documents.

How to get to production

1. Identify the exportable code, its frameworks and Google services used. 2. Separate frontend from private logic and configure credentials in the backend. 3. Define size, volume and cost limits per user. 4. Design storage and file deletion according to your obligations. 5. Publish with HTTPS, logs without secrets and provider outage tests. 6. Monitor errors, latency and monthly model consumption.

Comparison: visual app vs commercial product

A prototype shows an answer. A product must be able to explain how much it costs to answer, what happens when it fails, who accesses documents and when they are deleted. Infrastructure is only one of these pieces.

Frequently asked questions

### Does ArduMaker automatically include Gemini keys? No: you need the provider account and credentials or an explicit alternative solution. ### Can I save keys in VITE_? Not private ones; variables exposed to the frontend can remain in the compiled JavaScript. ### What do I do if Gemini does not respond? Show clear errors, retry when it makes sense and do not charge twice. ### Does migration eliminate token costs? No. The model provider still has its own terms of use.

Sources

OWASP Top 10 · Docker Production.

Where ArduMaker fits

The VPS or ArduMaker services can be the destination for exported code if the architecture allows it, along with domain, HTTPS, and operational processes. But migrating the app does not automatically fix its bugs, permissions, or data design. At ardumaker.com/lovable (coming soon) the specialized service for migrating AI-generated code and converting it into a deployed application with technical review will be presented. The page has not been created yet: for current information, check ArduMaker.

Review: October 2026. Each platform and its export modalities change. Check official sources and work on staging with test data.

What the photograph shows

Operator following metrics of an AI application in a controlled environment. Illustrative photograph generated for this article about taking code created with AI to a production application; it is not an official capture of the tool nor does it demonstrate a specific implementation.