AnyCrawler

AnyCrawler's real differentiator is workflow selection rather than raw scraping horsepower. For a known URL, you choose Fetch when the useful content sits in the initial HTML and Render only when JavaScript has to execute first — a decision the API makes explicit instead of defaulting everything to a headless browser. That matters because the cost gap is wide: Fetch starts at 2 credits, Render at 10, and a full-page screenshot at 50. Optional fields (include_metadata, include_links, include_media) each add 1 credit, and markdown_variant=readability adds none, so the marginal cost of a richer response is predictable. The rendering cache is the most interesting line item: with accept_cache=true, a valid render cache hit drops to a 1-credit base, which makes repolling a known JavaScript page several times cheaper than a cold render. Fetch deliberately does not read from or write to that cache. On the search side, five channels — web, image, news, video, and scholar — all bill at 2 credits per returned result and return links to candidate pages. The intended pattern is discover first, then spend the expensive fetch or render credits only on the strongest candidates. Scholar search is the unusual one here; most scraping APIs stop at web and news, so citation enrichment and academic reference workflows are a genuine niche. Context control is the second selling point. Pages come back as structured Markdown in a consistent envelope alongside title, description, canonical and final URL, status code, total milliseconds, credits used, and a markdown token count. For RAG engineers, that token count is the number you actually need to size chunking and context windows without guessing. Strengths: transparent per-operation credit pricing, per-call credits_used accounting, a cache that materially lowers repeat-render cost, five distinct search channels, and an Agent Skill that gives agent callers the same web access surface as your engineers. Weaknesses to weigh: everything is synchronous, so long-running or streaming crawl jobs aren't the model here. Browser-heavy pipelines are the margin risk — at 10 credits base per render, the Free plan's 5,000 one-time credits only fund about 500 renders versus 2,500 fetches. The free grant is one-time, not monthly, so validation has a hard floor. Screenshots are separate requests; you pay for the PNG and then pay again if you also want Markdown from the same page. Where it fits: agent developers and RAG teams who need live web context behind one API and want to forecast spend from credits_used rather than from a monthly bill surprise. Where it doesn't: non-developers wanting a point-and-click scraper, and anyone whose workload is dominated by JavaScript rendering on a small budget.