I remember the exact moment a client saw the AI pipeline cost. It was a Tuesday morning, and the number made them say "shut it down."
That pipeline was rewriting job descriptions for a platform with over a million listings. The idea was solid: use a capable LLM to turn raw ATS text into structured, SEO-friendly content. But the cost per listing added up fast. The feature was technically impressive and completely uneconomical.
That was a hard lesson. But it taught me something I've used on every AI project since: building cost-efficient AI features isn't about picking the cheapest model. It's about architecture. You can cut costs dramatically without cutting quality if you design the system right.
Here's what actually works.
Function Calling Cuts Token Waste by Half or More
