"Keeping the LLM bill predictable: token ceilings, attempt caps and cost budgets"
Every LLM cost horror story I've looked at had the same root cause — not an expensive model, but a missing guardrail. Here are the six controls that make an AI feature's bill boring: token ceilings, attempt caps, per-feature budgets, model tiering and batching, caching, and cost per outcome.
If you have an AI feature in production, there's a decent chance you've had the moment: you open the provider's billing page, and the number is not the number you expected. Maybe it's three times last month. Maybe it's three times last week. And the worst part is you can't immediately say why, which means you can't say it won't happen again.
Have an AI feature stuck between demo and production?
The gap — reliability, evals, cost control, the plumbing that keeps it running unattended — is exactly the work I do. If that sounds familiar, a short conversation is usually enough to point you the right way.
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