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In this hypothetical investigation, the first question finds two conflicting restart instructions. The next asks which condition applies. An application needs the earlier passages, but it may also need to fetch another document. Remembering the conversation and supplying the tools for that next step are different jobs.

The Interactions API, generally available since June 2026 and recommended by Google for new projects, can continue stored history through previous_interaction_id. That carries inputs and outputs, not everything about the request. The application supplies tools, system instructions and generation settings again for each interaction. The follow-up can therefore have the earlier passages without having the lookup tool the team meant it to use.

The route into the service matters before that conversation starts. The October 6 documentation check of Google's platform comparison recorded the Gemini Developer API and Gemini Enterprise Agent Platform API using the Google GenAI SDK, the client library for calling them. Cloud deployment added service-account authentication and different region and enterprise-control considerations. Older integrations could still say Vertex AI, and the cloud generateContent example retained vertexai=True. Enterprise-platform Interactions was separately Preview and global-only. Those are the retained October 6 findings, not a fresh confirmation of that comparison page's availability or status. Check the intended route before changing a working client merely to match a new name.

Repeatedly sending the runbook raises another practical question: can the service reuse work on text it has already seen? The older generateContent API remains supported. The caching documentation checked November 3 says Interactions supports automatic, implicit cache reuse, while creating and managing explicit cache objects requires generateContent on supported models. Explicit caching is not supported for Gemini 4 Argon and onward. A similar prompt does not guarantee a hit. Read the response's cached-token usage to see what was actually reused instead of assuming the repeated document cost less.

A team can also choose to serve Gemma weights on its own hardware or a hosted service. Gemma is a separate open-model family with research lineage shared with Gemini. That choice moves attention toward the runtime and hardware doing the work; it does not give the deployment the Gemini app's entitlements.

The incident review may contain names, internal addresses or details the team would not put in a public document. Before uploading it, follow the account and product it will enter. The same file does not carry one universal Google AI data policy with it.

In the consumer Gemini app, activity and privacy controls govern the chat. With Keep Activity enabled, activity can be used for model training. Turning it off excludes future chat training unless feedback is submitted, but chats stay for 72 hours and can be reviewed for protection purposes. Temporary chats also stay for 72 hours and are not used to train Google's AI models. Work and school accounts may have different terms. Turning off training is not the same as making the uploaded text disappear immediately.

Move the document to a developer project and the Gemini API terms make a different distinction. Unpaid input and output can be used for product and machine-learning improvement and reviewed by people. Paid prompts and responses are not used to improve Google products, though limited safety and legal logging still applies. API paid status requires an active billing-linked project. AI Studio can fall under paid terms through access to such a project or a Workspace enterprise account. For users in the European Economic Area, United Kingdom and Switzerland, paid data-use terms also apply to services offered free of charge. The account and region matter, not just whether this particular request has a price.

For a cloud workload, Google's enterprise-platform data guidance says managed-model data is not used for training or fine-tuning without prior permission or instruction. Storage still depends on the feature: abuse monitoring, advanced-model safeguards, grounding and optional request-response logging have separate conditions. Google Search grounding, for example, keeps relevant logs for up to three days and offers no storage-disable option for that feature. A promise about training does not settle how long a request leaves a record.

The team might instead put both documents in Gemini Notebook to compare their passages. Its privacy notice says content is not used to directly train foundational models unless feedback is provided. Feedback can include sources, uploads and outputs, with human review and retention for up to three years. Workspace and Education users have separate protection against human review and model training, even for feedback. If the notebook shares data with another Google service, that service's notice applies. The feedback button and the destination both matter to the documents you supplied.

Back in an API investigation, keeping the conversation has a storage consequence of its own. The Interactions retention documentation defaults to stored requests: 55 days on the paid tier and one day on the free tier, with shorter paid options. Set store=false to opt out of interaction storage, and later turns cannot continue through the previous ID; background execution is incompatible with that setting, too. The application can make stateless requests instead, supplying its own history. The choice is whether to keep this continuity on the server, not whether the earlier passages still matter.

Suppose the document-comparison request hits a limit and a developer tries another API key from the same project. The next call can hit the same limit. The key changed; the pool of allowed work did not.

Gemini API rate limits apply per project, not per key. The usual measures are requests per minute, input tokens per minute and requests per day; tokens are the pieces of input the model processes. Daily quotas reset at midnight Pacific time. Models and usage tiers have different ceilings, and some previews and experimental models face extra restrictions. AI Studio shows the project's active limits. Those ceilings are not a guarantee that capacity will be available when the responder asks the next question.

A billing problem can reach farther than this one project. Google's billing guide ties keys to projects and combines usage across projects under their billing account. If a prepay balance runs out, all linked projects can stop with HTTP 402, a payment-required response. Project spend caps are experimental, and billing processing can lag by around ten minutes. Batch completions and agent sessions can incur overages. A cap or balance helps control spending, but it is not an exact financial cutoff at each request.

That is why the failure needs a name before the client tries again. For a transient 429 or 503, the troubleshooting guide recommends increasing the wait between attempts, adding random variation so clients do not all retry together, and setting a maximum number of retries. Those techniques are exponential backoff and jitter. Official SDKs also have automatic retry behavior. A malformed request, depleted credits or denied access needs a different repair: resending a 400, 402 or 403 does not fix its cause. The waiting investigator needs the request to get somewhere, not merely to be sent more often.