The Real Cost Structure of AI Products Is Not AI
A r/SaaS post from the founder of HawkerSense, a street food scanning app with 7,000 scans and a top-30 App Store ranking, dropped a number that rewired the conversation. Maps API was 92% of their costs. AI was 8%. They had assumed the opposite and nearly made fatal pricing decisions based on that wrong assumption. The thread on tracking LLM API costs per feature in production showed the same problem from the other side: a founder discovered after the fact that one summarization feature was consuming 60% of their OpenAI budget.
The r/SaaS thread on the broken outbound tool stack for SaaS companies described a seven-tool Rube Goldberg machine just to do outbound sales: Apollo, Instantly, a standalone dialer, Zapier, HubSpot, LinkedIn Sales Navigator, and an email verification tool. The cost and coordination overhead of this stack is not trivial, and it is representative of how invisible operational costs compound.
The pattern across all three is the same: founders building AI products are systematically misidentifying where their costs actually live, which means their unit economics models are wrong. The AI bill is often not the problem. The ancillary infrastructure around AI is.
So what?
Before you build your next feature, instrument your cost stack at the feature level, not just the service level. You almost certainly have a cost center you have not identified, and it is probably not the one you are watching. The founders who know their per-feature, per-user economics precisely are the ones who can actually price and scale their products.
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Maps API is 92% of my cost; AI is 8%. I had it completely backwards.
Just made my first $136 on the internet
The outbound tool stack for saas companies is so broken it's almost funny
How are you tracking LLM API costs per feature in production?