Gemini API Free Tier Project Restricted — 403 PERMISSION_DENIED on GenerateConten

Gemini API
1

Subject: Gemini API Free Tier Project Restricted — 403 PERMISSION_DENIED on GenerateContent

Hello Google Gemini API Support Team,

I am unable to use the Gemini API Free Tier from my Google Cloud projects.

My current situation:

• Google AI Studio shows:
“Project quota tier unavailable”
and
“API access is restricted. Please set up billing to continue.”

• I have completed:

  • Age verification
  • Phone verification
  • 2-Step Verification

• I am using the Free-Tier-eligible Gemini Flash models.

I tested two separate Google Cloud projects:

  1. TEST CODE 1440
    Project ID: test-code-1440
    Project Number: 455366084776

  2. CODE 1440 Gemini
    Project ID: code-1440-gemini

Both projects show the same Restricted status.

I also performed a direct API test:

GET /v1beta/models
→ HTTP 200 OK

This confirms that the API key and project can access the Gemini model catalogue.

I then tested the currently available Flash model:

gemini-3.6-flash

POST …/models/gemini-3.6-flash:generateContent
→ HTTP 403 PERMISSION_DENIED

Response:

“Your project has been denied access. Please contact support.”

The same project-level denial occurs regardless of the prompt.

Therefore, this does not appear to be an invalid API key, disabled API, or model-specific problem. The model is recognized by the API, but GenerateContent access is denied.

Could you please review the Gemini API access/quota entitlement for these projects and determine why they are marked Restricted?

I would specifically like to use the Gemini API Free Tier and would prefer not to enable paid billing unless it is genuinely required for my account.

Please let me know what restriction is preventing Free Tier GenerateContent access and whether the project/account can be reviewed and enabled for the Free Tier.

Thank you.

2

Hi everyone,

I invite developers, researchers, and users working with Google AI Studio to conduct an independent verification of the platform’s data deletion lifecycle and backend persistence.

Steps to Reproduce the Deletion Verification:

  1. Create a session: Open Google AI Studio and start a new prompt / multi-turn chat session.
  2. Delete the prompt: Trigger the deletion command on the prompt within the AI Studio user interface.
  3. Empty the Trash: Navigate to Google Drive, locate the associated .json file in the Trash folder, and select ‘Empty Trash’ (permanent deletion).
  4. Run recovery: After a period of time (e.g., several days), execute the official Google Drive File Recovery tool on the account.
  5. Check AI Studio: Re-open Google AI Studio and click on the restored .json file. Observe whether the complete conversational memory, prompt context, and token history are reloaded from the backend servers.

Technical Background:

The purpose of this test is to verify whether triggering deletion in Google AI Studio executes a cascading backend purge (DELETE/DROP) on persistent storage, or merely performs a client-side disconnection by moving the frontend metadata pointer (.json) to the Trash while retaining the underlying interaction data on persistent servers.

Verifying whether data remains recoverable following explicit permanent deletion commands is an essential aspect of evaluating data lifecycle transparency and statutory compliance under GDPR Article 17 (Right to Erasure).

Documentation & Forensic Reports: