5 resources from The SaaS CFO we point people to, and the questions each answers.
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One number that tells you whether your whole GTM spend is working, with current benchmark bands (under 0.5 underinvesting, 0.75 to 1.0 efficient, over 1.0 exceptional) and the precise definition of what to include in the spend.
One metric you can start tracking this month: AI product revenue divided by inference cost. It moves before your gross margin does, which makes it the early warning you want rather than the postmortem you get from the P&L.
Inference efficiency ratio is AI product revenue divided by inference cost for the same period.
For AI infused SaaS, 8:1 is the floor and 10:1 or better is healthy, below 5:1 is a warning.
For AI native products the healthy zone is 5:1 or better, since inference is structurally about 20 percent of revenue.
Unlike gross margin, which lags, IER is a leading signal: model routing, prompt caching and tiered pricing moved one example from 4.4:1 to 8.0:1 at the same revenue.
Three minutes of a SaaS CFO doing the calculation on screen, including the gross margin step most founders skip. If you have no finance team, this is the whole method without the jargon.
This question is about which classic metrics break when revenue swings with consumption, and this is a short, concrete fix for one of them: how to compute payback when there is no stable subscription number to divide by.
Six minutes to see the magic number actually calculated, with the benchmark bands and the lag between spend and revenue that trips people up. Faster than reading the history if you just want to know whether your ratio is fine.
