How to Track AI Costs by Cost Center
Engineering tracks AI cost by team and feature; finance tracks everything by cost center. For AI spend to fit into
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,
Every organization tracking AI spend eventually faces the build-versus-buy question. Start with a spreadsheet, graduate to internal scripts,
Tracking AI spend as it happens is valuable, and its real payoff is forward-looking: a trend you can track
Every AI bill is built on tokens. Understanding and tracking token consumption is the difference between a bill you can