AI costs are hitting founders in the wrong places
Two threads surfaced the same uncomfortable discovery from opposite directions. On r/SaaS, the founder of HawkerSense, a street food scanner with 7K scans and a top-30 App Store ranking, shared that Maps API was eating 92% of his costs while AI was only 8%. He had budgeted mentally for the opposite. Separately, a thread on how to track LLM API costs per feature in production revealed that many founders have no per-feature cost visibility at all, with one founder only discovering a single summarization feature was burning 60% of his OpenAI budget after the invoice arrived.
On HN, Uber's COO surfaced the same signal at scale: he told engineering leaders that higher token usage did not translate proportionally into useful consumer features, coining the term 'tokenmaxxing.' The pattern is consistent across indie founders and large-scale operators. People are spending on AI without instrumentation, and the bill doesn't match the value.
The r/SaaS thread on protecting SaaS products from rising AI API costs added another layer: founders are not designing fallback modes for when they need to scale back AI features, which means a cost spike or pricing change from a model provider becomes an existential event rather than a manageable adjustment.
So what?
Founders building AI-native products need per-feature, per-user cost instrumentation before they hit meaningful scale, not after. The HawkerSense example is a useful reminder to audit every API dependency, not just the obvious AI ones. Design your product so core value survives if you have to gut the AI layer.
Read these
Maps API is 92% of my cost; AI is 8%. I had it completely backwards.
How are you tracking LLM API costs per feature in production?
How are you protecting your SaaS from rising AI API costs?
Uber’s COO says it’s getting harder to justify money spent on tokenmaxxing