Your AI Bill Isn't a Model Problem. It's an Architecture Problem.

Short summary

LLM costs aren't usually a model problem—they're an architecture problem. Most AI workflows route everything through expensive LLMs when cheaper deterministic ML, triggers, or API calls can handle specific steps 100-1000x cheaper. The post shows a four-component framework with a working support-ticket triage example: classify intent with lightweight ML, draft responses only when needed, use API calls for data updates.

• •Route each workflow step through the right tool, not everything through expensive LLMs
• •Use deterministic ML for structured predictions, LLMs only for language reasoning and generation
• •Four-component framework (Trigger, Deterministic ML, LLM/Generative, Tool/API) with vastly different cost profiles

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