ScrapingBee is a well-known scraping API. It handles JavaScript rendering, proxy rotation, and basic extraction rules, and it works for many standard use cases. But teams that push it harder tend to run into the same friction: credit costs that are hard to predict, output that still needs parsing before it is usable, and limited options when a site is heavily protected or structured data is the actual goal. This comparison covers the most relevant alternatives available today

Firecrawl has built a strong reputation as a go-to tool for converting web pages into LLM-ready Markdown. It handles JavaScript rendering, offers a clean API, and integrates well with frameworks like LangChain and LlamaIndex. For many developers building AI pipelines, it was a natural first choice. But as projects scale, a few structural issues tend to surface: credit costs that grow faster than expected, an AGPL 3.0 license that complicates commercial use without enterprise

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 $