Cost Monitoring

CostMonitor

Part of: Deploying & Monitoring LLM Apps

A startup launched a free AI feature on a Friday. It went mildly viral over the weekend. On Monday they opened a five-figure model bill, one script in a retry loop had hammered the API thousands of times, and nobody was watching. The model did exactly what it was told. There was simply no cost monitoring to catch it, and no limit to stop it. What it is Cost monitoring is tracking what your LLM usage is spending, in real time, and alerting or cutting it off before a surprise becomes a disaster. Because APIs bill per token , cost is fully computable from data you already trace: input tokens times the input price, plus output tokens times the output price. The trick is to compute it continuously, attribute it (per user, per feature), and put a ceiling on it. A useful cost record for each call is just input tokens, output tokens, and the prices, multiply and you have the exact spend. How it works You attach a cost calculation to the traced call from lesson 2: Three moves matter. Compute cost from token counts on every call. Attribute it to a user or feature so you know where the money goes. Cap it with a budget that refuses the call once a limit is hit, so a runaway loop dies instead o

Challenge: The Budget Throttle