LLM cost assumptions are wrong, Maps API is the real expense

A Reddit founder building HawkerSense, a street food scanner with 7,000 scans and a top-30 App Store ranking, shared a cost breakdown that surprised them: Maps API is 92% of their infrastructure cost, while AI is only 8%. They had built their entire unit economics model assuming AI would dominate costs and nearly made pricing and scaling decisions based on that wrong assumption.

This is a specific instance of a broader pattern appearing in multiple Reddit threads about LLM cost management. Founders are asking how to track LLM costs per feature in production, how to protect margins against API price hikes, and whether to architect products so AI features can be turned off if costs spike. The anxiety around AI costs is real, but the HawkerSense case suggests the actual cost distribution in production often differs dramatically from pre-launch assumptions.

A separate Reddit thread asked directly how founders are protecting against rising AI API costs, with the key question being whether the product is usable if AI features are scaled back. This is good product architecture thinking regardless of cost, but it is being driven by cost anxiety rather than pure design intent.