ScrapingBee vs AgentFetch — which is better for agents?

ScrapingBee wins when you need rotating residential proxies and CAPTCHA-solving at scale; AgentFetch wins for the AI agent use case. ScrapingBee is a mature scraping API ($49/mo for 100k API credits, $99/mo for 250k, custom higher) with JS rendering, premium residential proxies, and built-in CAPTCHA solving via 2Captcha integration. It targets traditional scraping pipelines — e-commerce price monitoring, SEO rank tracking, ad verification — where bypass is the core problem. AgentFetch targets the AI agent use case where the core problem is LLM context economics, not bot evasion: it returns markdown sized for token cost, integrates as an MCP server with one config line, and handles the polite 85% of public-web fetches at low cost. For Claude Desktop, Cursor, Cline, or LangChain agents reading docs, news, GitHub, arXiv, and Wikipedia, AgentFetch is a strictly better fit. For an agent that needs to scrape Amazon product pages at scale through CAPTCHAs, ScrapingBee is the right tool — but you'll write a custom MCP wrapper around their HTTP API. Cost comparison: a 10k-page-per-month research-agent workload costs ~$5-15/month on AgentFetch's paid tier (with included markdown conversion and JSON extraction) vs ~$49/month on ScrapingBee plus the extra LLM token cost from raw HTML output.