Curious how companies with AI-heavy products are actually forecasting their AI costs.
Tracking historical spend seems relatively straightforward now; you can see token usage, API spend, model usage, etc. But how do you actually model what that cost will look like in 6–12 months?
For example, if customer numbers grow, AI adoption increases, usage per customer changes, or you switch between models, how do finance teams translate all of that into a forecast?
Do you have a proper driver-based model for things like:
customers × AI usage × model calls × token usage × model pricing
and then use that to forecast COGS / gross margin?
Or in practice is it much simpler?? e.g. taking historical AI/cloud spend and applying a growth assumption?
Particularly interested in hearing from AI-native / AI-heavy SaaS companies where inference costs are actually meaningful to the economics of the product.
Would be great to hear how people are handling this today and whether it’s actually a difficult problem or fairly straightforward once you have the right data...