Make Gemini request storage an explicit application decision

From 26 October 2026, new paid-tier projects store Generate Content requests by default (store=true). Earlier projects retain their existing setting. When moving application code to a new project, review the destination configuration instead of assuming the previous project’s behaviour followed the code. (Google)

Write down the storage decision before choosing a toggle

Start with a short purpose statement: for example, “retain selected synthetic troubleshooting requests during a bounded pilot”. That is easier to assess than “enable logs”. Name the person who can access the records, the work the records support and the event that ends the need to keep them. If those answers are missing, an inherited default is doing policy work that nobody has accepted.

Keep application content separate from the evidence needed to operate the service. A release identifier, error category and request reference may help investigation without putting an entire customer conversation in every internal ticket. Determine the minimum useful evidence for the particular application; do not assume that every support problem requires keeping full payloads.

Check the endpoint and the installed SDK

The request-level store property takes precedence over the project setting. Google documents Generate Content support from google-genai 2.33.0 for Python and @google/genai 2.31.0 for JavaScript. The logging guide also lists unsupported models and inputs, so these logs should not be treated as a complete application audit trail. (Google)

Trace one request path from the application to the provider. Record the project, endpoint, SDK actually installed and any shared wrapper that sets generation options. A setting in a dashboard and a setting in application code can be owned by different people. Review both in the same change so that one does not quietly reverse the other.

Give retained records their own lifecycle

The policy describes private developer logs separately from abuse monitoring and voluntary model-improvement sharing. Its normal retention window marks logs for deletion after 55 days, with shorter selectable windows; saved datasets have no fixed expiry. Turning off future storage is therefore a different decision from handling records already retained. (Google)

Google prohibits data donation from API clients offered to users in the European Economic Area, Switzerland or the United Kingdom. Personal, sensitive or confidential data is also excluded. These limits concern contribution for product improvement and training, separately from private paid-tier logging. Internal permission cannot make an excluded contribution available. (Google)

For an application team, this suggests three separate checks: confirm the intended behaviour for new requests, inventory records already saved, and establish how those records are reviewed or removed. Treat “we stopped collecting” and “we finished handling the previous collection” as separate items in the change record. Neither should be inferred from the other.

If you need a sample for a defect, create a small synthetic example that reproduces the relevant structure. Keep identifiers fictional and label the sample. When the issue depends on real content, establish the permitted private diagnostic handling before moving that content. Approval for this workflow does not authorise a prohibited training contribution. This guide does not assess an individual organisation’s legal obligations.

Verify the behaviour with a harmless request

1. Define a synthetic test request and the expected storage result before sending anything.
2. Inspect the effective project and application settings, including request-level overrides.
3. Run the check only in an authorised environment and inspect the expected evidence; record unknowns rather than treating an empty view as proof.
4. Check the opposite permitted configuration separately if your application supports both paths.
5. Remove the test material according to the chosen lifecycle and record who owns future configuration changes.

This is a proposed verification sequence, not a test carried out by KIURO. The outcome should be a reviewable configuration record, not simply a screenshot of a green API response.

Availability and costs still need the project context

Switzerland is listed as a supported country for Gemini API and AI Studio. That country list establishes neither Swiss data residency nor the eligibility of a particular account or feature. Check the applicable access and contractual conditions for the intended use. (Google)

Google publishes separate free and paid model access and usage pricing. This article does not establish an incremental logging charge or calculate a project budget. Include the selected model, expected use, diagnostic work and record handling when estimating operating effort. (Google)

A useful next step is a one-page record of the effective configuration and its owner. If the unresolved problem is how an AI feature fits the application and operating workflow, use that record in the software and applied AI scope discussion.