At low volumes, feeding HTML into an LLM for extraction looks like a reasonable shortcut. At 50,000 pages a month, it stops looking reasonable entirely. The problem is not that LLMs extract data poorly in every case. The problem is that their cost model scales with token volume, and web pages are large. A realistic full HTML page averages around 572,000 tokens. At that size, even the cheapest nano-class models charge roughly $0.03 per page. At 120,000 pages a month, that is $

The assumption has become almost automatic: if you need to extract structured data from web pages, you reach for an LLM. Feed it the HTML, write a prompt, get JSON back. It works in a demo. It works on ten pages. So teams build pipelines around it and move on. The problem shows up later, quietly, in production. When extraction fails without telling you The most dangerous failure mode in any data pipeline is not a crash. It is a wrong value that looks correct. LLM-based extrac