Our expense app sends receipt photos to a vision model. The feature cost six times our estimate and p95 latency was 9 seconds. Phones upload 12-megapixel photos and we sent them as-is. Resizing and cropping before upload cut cost by 78 percent with no accuracy loss.
A developer added user_id as a label on a request latency histogram to debug slow accounts. Active time series went from 1.2 million to 19 million, our hosted metrics bill quadrupled and the query layer slowed down. We found the culprit, dropped the label and set cardinality limits.
A refactor changed one output prefix in an image processing Lambda, and the function started triggering itself. Invocations went from 40,000 a day to 9 million in six hours. We stopped it with reserved concurrency, then separated input and output and added loop guards.
A summarisation service trimmed documents to fit a token budget, yet some requests failed with a context length error. Our counter used a tokenizer from a different model family, ignored the chat template, tool schemas and the reserved output tokens. How we found the gap and switched to the provider's token counting.
A research agent called the same search tool 412 times in one run and spent more overnight than the whole feature spent in a normal week. The tool returned an empty string on errors, the loop had no step limit and nothing tracked spend per run.