Systems | Development | Analytics | API | Testing

If you run a software engineering tools team, you have almost certainly had this conversation: a developer asks for production data access to debug a real incident, and someone in the room says no. Not because the request is unreasonable (it isn’t), but because nobody wants to be the person who said yes when something goes wrong. That instinct is understandable. Production environments carry real risk. But the reflex to lock everything down has a cost that rarely gets accounted for.

Testing a FastAPI app that calls OpenAI, Anthropic, or Gemini gets expensive fast. The problem is not just the API bill in production. It is all the repeated traffic in development: prompt tweaks, CI runs, regression checks, and the load tests you keep putting off because every run burns tokens. Hand-written mocks do not help much once the app is doing multi-step LLM work.

Most teams know they are spending money on AI in production. Far fewer realize how much they are spending outside production. It’s easy to get lost as you evaluate which model has the best responses, is fast enough, and cheap enough to run in production. That is because the AI bill usually shows up as a giant blob. It is easy to see the total.

Recent outages tied to AI-assisted code changes have pushed companies into a corner. After several incidents with massive “blast radius” impacts, organizations like Amazon introduced stricter controls—mandating that senior engineers manually review all AI-generated code before it hits production. That response makes sense on paper, but it exposes a fatal flaw in the modern development pipeline.

Your tests are not flaky. Your test data is. That 401 Unauthorized that fails every Monday morning? The OAuth token in your test fixture expired 72 hours ago. The order_id that works in staging but not in CI? It was hardcoded six months ago and the format changed from integer to UUID in January. The timestamp assertion that passes at 2pm and fails at midnight? You are comparing a hardcoded 2026-01-15T14:30:00Z against Date.now(). These are not test infrastructure problems. Retrying them will not help.