What is multi-site web scraping?

Multi-site web scraping extracts consistent data from many websites that each have their own HTML structure, URL layout, and content organization. The challenge is that no two sites present the same information the same way: a company's mission statement might appear in an <h2> on the homepage of one site, buried in an about page on another, and structured as a paragraph inside a sidebar on a third. Approaches built on fixed CSS selectors require separate configuration per domain, which becomes impractical once the target list reaches more than a handful of sites.

Multi-site scraping at scale typically involves a list of target URLs (company domains in a spreadsheet, competitor sites, job boards), a consistent set of fields to extract (name, location, contact email, mission), and no reliable way to predict how any individual site is structured. The bottleneck in selector-based pipelines is not the scraping itself but the per-site configuration: maintaining selectors across hundreds of different domains is not viable. Natural language extraction removes this bottleneck because the same prompt works across all sites regardless of structure. The tradeoff is cost per page relative to a cached selector, and occasional misses on sites where the target content is embedded in images or loaded behind authentication.

Firecrawl's Scrape API accepts a plain-language prompt or JSON schema and applies it to any website without selectors. Paired with the Crawl API or Map API to find the right pages first, it handles multi-site extraction pipelines from a list of domains without any per-site configuration.