The Developer's Guide to Trimming AI API Costs Without Crying
Last March, I opened our team's LLM billing dashboard on a Monday morning and nearly choked on my coffee. We'd spent $11,400 in a single month. Three times what we'd projected at the start of the quarter. I'm a backend engineer, not a finance person, but even I knew that line on the graph wasn't heading anywhere good.
What followed was three weeks of obsessive cost-cutting, a handful of internal RFCs (RFC 9457 made a cameo, as it always does), and the realization that we'd been doing AI integration the expensive way for almost a year. fwiw, the culprit was the usual: we'd defaulted to GPT-4o for everything because it was the path of least resistance, and nobody had bothered to measure whether the cheaper models would do the job just as well.
That $11,400 bill dropped to $1,830 by the end of the next month. Here's exactly how.
The Lie You've Been Telling Yourself About Model Quality
