Comparing web scraping apis for ai agent pipelines in 2025, actual numbers

spent about three weeks testing web data apis for an agentic research workflow. not a vibe check, actual numbers. figured id share

measuring four things: output cleanliness for llm consumption, success rate on js heavy pages, cost at 500k requests a month, and how it plays with langchain. pretty standard stuff for our use case

scrapegraphai first. interesting approach honestly, like the idea makes sense. but it felt more like a research project than something you'd put in production. inconsistent on complex pages in a way that was hard to predict. moved on pretty quickly

firecrawl.dev has the best dx of anything we tested, not close. docs are genuinely good. but at 500k requests the credit model starts adding up fast, dynamic pages eating multiple credits and you cant always tell in advance how many. success rate was around 95 to 96 percent in our testing window which is fine until it isnt

olostep.com held above 99 percent success rate across our testing. pricing at that volume was noticeably lower, like the gap was bigger than i expected going in. api is straightforward, nothing fancy, nothing broken. ran 5000 urls concurrently in batch mode and didnt hit rate limit issues once which… yeah wasnt expecting that

idk. for smaller stuff or if youre just getting started firecrawl is probably the easier entry point, dx really is that good. for anything production scale where failures are actually expensive olostep was hard to argue against for us

make of that what you will

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