When your Gemini API spend cap trips, paying users go down too — isolating the blast radius with per-tier projects
A Project Spend Cap stops the entire project at once. To keep a runaway free tier from taking paying users down with it, this is a design note on isolating the cap's blast radius across per-tier projects and closing the ~10-minute delay with an application-side soft budget gate.
Gemini API × TypeScript Type-Safe AI Application Architecture — Integrating Zod Schemas, Structured Output, and Streaming
Type-safe AI applications with the Gemini API and TypeScript: Zod validation, Structured Output, streaming pipelines, and error handling that holds up in production.
When Gemini's Structured Output Quietly Drifts From Your Schema — Field Notes on Measuring Validation and Retries
Even with response_schema set, Gemini's structured output occasionally drifts in production. Stop swallowing failures, measure them, split causes by finish_reason, and feed errors back for a corrected retry. Field notes from stabilizing a validation pipeline.
The Morning a Preview Image Model Went Dark — Migrating to GA Gemini Image Models and Building a Deprecation-Resilient Pipeline
With gemini-3.1-flash-image-preview and gemini-3-pro-image-preview retired, here is how to migrate to the GA models and design an image pipeline that no longer gets caught off guard by deprecation dates — with code and cost math, plus video-to-image thumbnail automation.
Gemini API × Cloudflare D1: A Zero-Cold-Start AI Backend Under $10/Month — Implementation Notes
Build a zero-cold-start, globally distributed AI backend with Cloudflare Workers + D1 (edge SQLite) and Gemini API — conversation history, rate limiting, post-stream write latency, and a real $8.50/month cost breakdown, from a deployment I actually operate.
Your File Search Store Goes Stale in Production — Catalog Sync and Drift Detection That Actually Hold
Load a catalog into File Search once and forget it, and within weeks it starts confidently pointing users at assets you already pulled. Here is the sync pipeline I run: hash-based incremental import, a blue/green rebuild that swallows deletions, and a nightly drift audit.
Gemini API on Google Cloud: Diagnosing Production Errors Layer by Layer
Systematically diagnose Gemini API errors in Google Cloud production environments. Covers IAM permissions, Vertex AI vs AI Studio, VPC Service Controls, quota management, service accounts, and multi-region failover with full code examples.
Compressing Gemini API Chat History with Rolling Summaries — Designing Chatbots That Survive Hundreds of Turns
When a Gemini chatbot grows long enough, your bills balloon and one day a request hits the token ceiling. The rolling-summary pattern keeps long chats stable.
Track Gemini API Costs in Production with usageMetadata — A Per-Request Logging Pattern That Reconciles With Your Bill
A production pattern for capturing Gemini API's usageMetadata per request to attribute spend by endpoint, user, and model — hardened for the 3.5 Flash GA era where the default model can shift under you. Covers pricing keyed on resp.model_version and a nightly audit that flags model drift and unknown models before the invoice does.
When Your pgvector Search Quietly Gets Worse — Field Notes on Protecting Recall with Gemini Embeddings
A semantic search built on Gemini Embeddings and PostgreSQL pgvector tends to lose precision over months without throwing a single error. These are field notes on the real causes — model pinning, operator/index mismatch, HNSW reindexing, and recall collapse under filters — with working code.
Your Managed Agents Bill Has a Second Axis: Drawing a Budget Boundary Around Sandbox Runtime
Managed Agents in public preview bills for tokens and for how long its Google-hosted sandbox stays alive. A single hung run quietly drains your budget on that second axis. Here is a working Python design for wall-clock caps, idle teardown, and a concurrency ceiling.
Stop a Batch Before It Overspends — A Budget Gate Built on countTokens That Survives a Default-Model Swap
Nightly batches overspend because you only learn the cost after billing. Starting from countTokens, this guide builds a budget gate that folds in thinking tokens and keeps your estimate intact even when the default model changes underneath you.