Thinking on LLM costs, AI infrastructure, and building predictable products.
Variable costs feel manageable when you are small. They become a structural problem as you scale. Here is why converting your cost base from variable to fixed is one of the most important decisions an AI company can make.
Most teams assume cutting LLM spend means a worse user experience. It does not. Prompt compression, semantic caching, model routing, and batching can cut costs by 50 to 65 percent with no product changes.
Pay-per-token pricing sounds simple until your users start behaving like real users. Here is what causes LLM cost spikes, why they are structurally unavoidable with current pricing models, and what you can do about it.
Your customers want a predictable number. Your LLM provider gives you anything but. Here is how to close that gap and build a pricing model that protects your margins regardless of usage patterns.
For most startups a traffic spike is the best problem to have. For AI startups on pay-per-token pricing it can mean a five-figure surprise on your next invoice. Here is how to protect yourself.