The Moment the Cost Problem Snapped Into Focus
The first time I ran the LLM scoring pipeline against our full backlog of job listings, I watched the OpenAI API costs climb in real time. What worked beautifully for 100 test listings was economically impossible at 10,000 per day.
This wasn't a side project. This was a production job board platform I was building for a client, processing listings from major ATS sources. Users needed scores. The client needed the system to be profitable. And I needed to rethink everything about how I was calling LLMs at scale.
The naive approach was simple: take a listing, send it to GPT-4 with a prompt, get a score back. Simple, but expensive. At scale, that pattern would have made the product economically unviable.
So I rebuilt the pipeline from the ground up. Here's what the final architecture looks like.
