Your AI Bill Isn't a Model Problem. It's an Architecture Problem.
If your LLM costs are climbing, the instinct is almost always the same: swap to a cheaper model. GPT-4 to GPT-4-mini. Claude Opus to Claude Haiku. Sometimes that helps a little. It rarely fixes the actual problem. The actual problem, in most workflows I've looked at, is that every step gets routed through the LLM, even the steps that don't need language reasoning at all. This post breaks down a simple mental model for deciding what should and shouldn't touch an LLM, with a working example you can adapt. The four components of any AI workflow Every automated workflow — whether it's a support ticket router, a fraud check, or a content pipeline — is built from some combination of four building blocks. They get treated the same once a workflow diagram is drawn flat, but they have wildly different cost and latency profiles. Component What it does Think of it as Typical cost Trigger Starts the workflow The doorbell ~$0 Deterministic ML Structured predictions — classify, score, rank The calculator Cents per 1,000 calls LLM / Generative Reads, writes, reasons in language The writer Dollars per 1,000 calls Tool / API Fetches or writes real data The hands Cents per 1,000 calls The gap between row 2 and row 3 is the whole article. A classifier and an LLM call can solve the exact same problem, but one costs roughly 100-1000x more than the other, depending on model and provider. If you're not deliberately deciding which one handles which step, you're probably defaulting to the expensive one — because in frameworks like LangChain or a quick custom agent loop, it's just easier to shove everything into a prompt. Where this actually shows up Here's a workflow I see constantly: an automated support ticket triage system. flowchart LR A[New support ticket] --> B{Classify intent} B --> C[Route to team] B --> D[Auto-draft response] D --> E[Update CRM] A naive build sends the entire ticket text to an LLM and asks it to do everything at once: classify the intent, decide routing, draft a re