Google AI Studio - Feature Request: Add Native "One-Click" Firebase/Firestore Provisioning

Google AI Studio
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TL;DR

Adding Firebase and Firestore should become as easy to add to an app developed with Google AI Studio as deploying apps to Cloud Run.

The current “Deploy to Cloud Run” feature in Google AI Studio is a friction-free deploy-to-web experience for stateless apps. This request describes how adding memory (Firestore) and identity (Authentication) should be equally seamless, moving from a complex manual configuration to a “one-click” provisioning model.

1. The Problem

Google AI Studio has mastered the “stateless” prototype (AI-assisted vibe coding). With the current Deploy to Cloud Run feature, an app developer can go from a prompt to a live, scalable URL in a very short time and with low effort. This is a great user experience.

However, lots of useful apps require two things that Cloud Run alone does not provide out-of-the-box: Memory (Database) and Identity (Authentication).

Currently, to add persistence to an AI Studio generated app, a user must:

  1. Leave AI Studio.

  2. Go to the Firebase Console.

  3. Create a project and toggle on Firestore/Auth.

  4. Copy configuration objects/API keys.

  5. Manually paste them back into the AI Studio code editor or environment variables.

  6. Prompt the model to write the specific initialization code.

This context switch kills the flow, causes trial-and-error, and raises the barrier to entry for rapid full-stack prototyping.

2. The Proposed Solution

Add a “Resources” or “Integrations” side panel within the AI Studio interface, similar to the existing deploy options.

  • One-Click Database: A toggle to “Enable Firestore”. This should auto-provision a Firestore instance (perhaps in Test Mode by default) in the linked Google Cloud project.

  • One-Click Auth: A toggle to “Enable Authentication” (defaulting to Google Sign-In).

  • Auto-Injection: Crucially, this should automatically inject the necessary firebaseConfig and initialization code into the app, or expose them as pre-set environment variables that the generated code can reference immediately.

  1. Strategic Value to Google

  • Parity with “Deploy to Cloud Run”: It bridges the gap between a “demo” (stateless) and an “MVP” (stateful).

  • Ecosystem Stickiness: It prevents users from asking the model to use easier-to-setup external databases (like Airtable or Supabase) simply because the Firebase config friction is too high.

  • Empowerment: It allows “AI-first” developers (who may be less familiar with cloud consoles) to use professional-grade Google infrastructure without the learning curve.

AI studio app builder with Firebase Authentication authorized domain error
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Totally agree — this would make the whole prototyping workflow so much smoother.
Right now the jump from AI Studio → Firebase → back to AI Studio really breaks the flow. A native “one-click” setup for Firestore + Auth (just like Deploy to Cloud Run) would be a huge quality-of-life upgrade for anyone building apps with memory or user login. Hope the team considers this!

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Hi @paulvancotthem ,

Thank you for your feedback. We appreciate you taking the time to share your thoughts with us, and we’ll be filing a feature request.

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UPDATE 2025-11-30: I can now successfully add Firebase Authentication using the email/password sign-in method to any app I build in Google AI Studio. It’s not exactly rocket science, but it does involve several steps and required thoroughly studying the documentation. The apps are deployed using Google AI Studio’s built-in Cloud Run service, so there was no need to set up any backend, aside from Firebase, of course.

Still, my feature request stands, adding Firebase Authentication (and data storage) needs to become far simpler.