Your AI Bill Is Growing Faster Than Your Revenue

A founder told me last month that their OpenAI bill went from $400 to $6,000 in six weeks. Nobody changed the product. Users just... used it more. And that's the trap: AI features don't cost what your other infrastructure costs. A database scales in steps. An LLM call scales with every single interaction, and most teams wire it up without ever checking what a "normal" conversation actually costs.

The bill is a symptom, not the problem

Runaway AI costs almost always trace back to one of three habits. You're sending the full conversation history on every call instead of trimming it. You're using a frontier model for tasks a cheaper one handles fine, like classifying a support ticket or summarizing a form. Or you have no cap on how long a user session can run, so one confused customer can burn through more tokens than your entire onboarding flow costs.

None of these are exotic problems. They're just invisible until the invoice shows up, because token usage doesn't show up in a staging environment the way a slow query does. It shows up in production, at scale, after you've already shipped.

What actually keeps costs in line

A few habits separate teams whose AI features stay profitable from teams whose margins quietly erode:

• Route by task, not by default. Cheap, fast models for classification and extraction; expensive ones only where reasoning quality actually changes the outcome.
• Cache aggressively. If ten users ask a near-identical question, you shouldn't be paying for ten full generations.
• Set hard ceilings per session and per user, with graceful fallbacks instead of silent overage.
• Log cost per feature, not just cost per API key, so you know exactly which button is expensive.

That last one matters more than people think. Most teams can tell you their total AI spend but not which feature is driving it. That's like knowing your total ad budget without knowing which campaign converts.

Build the cost model before you build the feature

The teams that avoid the surprise invoice treat cost as a design constraint from day one, the same way they'd treat page load time or database query limits. It's not glamorous work, but it's the difference between an AI feature that makes your product better and one that quietly makes it unprofitable to serve your best customers.

This is exactly the kind of thing we think about at Cedrix when we build AI features into client products: not just "does it work," but "does it still make sense at 10x the usage." If you're weighing whether an AI feature is worth building, or you inherited one that's already gotten expensive, we'll take a real look at it. Start with our free MVP offer if you're validating something new, or just reach out if you need a second set of eyes on what you've already shipped.