AI Cost Tracking KPIs Every Engineering Leader Should Watch
Total AI spend is the KPI every engineering leader has and the one that tells them the least. It reports
Total AI spend is the KPI every engineering leader has and the one that tells them the least. It reports
Every AI cost decision ultimately traces back to a prompt. The system prompt, the context assembled, and the instructions sent
An AI cost number in isolation is just a number. The question that matters is whether it is earning its
Engineering tracks AI cost by team and feature; finance tracks everything by cost center. For AI spend to fit into
The ideal way to track AI costs is to tag every call with its team, project, and feature. In practice,
Ask most teams what an AI feature costs and they will quote the model API bill. That is the visible
Most AI cost tracking focuses on production, and for good reason, but development and staging quietly consume real budget too.
Retrieval-augmented generation looks like one feature and behaves like a small system. Between the embedding, the retrieval, the context
Fine-tuning is one of the easier AI costs to lose track of, because it is lumpy. A training run
Training gets the headlines, but inference is where the money actually goes for most organizations running AI in production. Every
AI cost tracking is not complicated, but it is easy to do badly. Most teams either ignore it until a
AI costs behave one way in development and a completely different way in production. In development you control the inputs,