The Unit Economics of AI Inference: What a Million Tokens Actually Costs

“Fractions of a cent per call” is how a feature looks cheap right up until the invoice. Here's how to actually cost a million tokens — and why “AI is strategic” is the reason to know your unit economics, not skip them.

Somewhere in the last two years, “AI is strategic” became permission to stop asking what it costs. The word does a lot of quiet work: it elevates a line item above scrutiny, makes the person questioning it look small, and defers the bill to a future quarter.

But strategic and unmeasured is exactly the combination that produces a six-figure surprise. The features that matter most are the ones whose unit economics you should understand best, not least. A cost you've declared too important to track is a cost that will eventually track you down.

Being strategic is the reason to demand unit economics, not an excuse to skip them. What does a thousand inferences cost? How does that scale with adoption? Where's the break-even against the value created? You can hold the conviction that AI matters and still insist on knowing the number. The leaders who get burned treated importance as a substitute for arithmetic.

Unit economics is the cost and revenue of a single unit of activity — here, one inference, one request, one user. It's the lens that turns “AI is expensive” into a number you can actually manage.

The trap with AI features is that they look trivially cheap per call and can still be ruinous at volume; “fractions of a cent” becomes a fortune at a billion of them. You don't understand the bill until you understand the unit — and “it's strategic” is a reason to know your cost per request, not an excuse to skip measuring it.

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“It's pennies per call!” they all said,
And shipped it, then toddled to bed.
A billion calls on,
The margin was gone—
The fractions add up. See? In red.

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