The bill is the part nobody talks about when they demo AI pipelines. You see the cool output, the semantic matching, the ranked results. You don't see the spreadsheet where you realize each token costs real money.
I was building an AI scoring pipeline for a job board platform that ingests listings from multiple ATS sources and needs to surface relevant content for candidates. The system processes over 10,000 new listings each day, and every single one gets scored against candidate profiles. This is what I learned about architecture, cost management, and knowing when to reach for an LLM versus reaching for something simpler.
Why LLMs Made Sense Here
Traditional ML could solve some of this. A good classification model can tag skills, seniority levels, and job categories reliably. But the problem was richer than that.
A job listing might say "looking for a Python ninja who knows their way around React." A traditional classifier sees keywords. An LLM understands that "ninja" is informal for "expert" and that "knows their way around" means practical experience, not academic knowledge. When you're matching candidates to roles, that nuance matters.
