ScrapeGraphAI

ScrapeGraphAI is a strong contender for developers who want to replace fragile CSS/XPATH selectors with natural language prompts. Its graph-based pipeline makes extraction context-aware, reducing the iteration time on scraping projects. The platform supports multiple output formats and includes stealth mode for sites that block scrapers. When to pick this: you're a developer building RAG pipelines, monitoring competitors, or scraping at moderate scale. The Python SDK and Playground let you prototype quickly. The credit-based model means you only pay for what you use. When to pass: you need a visual point-and-click interface — ScrapeGraphAI is API-first. If your volume is very high, the Pro plan (750k credits/mo at $500) may still be exceeded, requiring Enterprise custom pricing. Also, it's cloud-only as of now, so no offline scraping. Compared to alternatives like Firecrawl or Apify, ScrapeGraphAI's LLM extraction is more flexible but pricier per request. The free tier (500 credits) is extremely limited compared to Firecrawl's 500 pages/month free, so evaluating before committing requires a paid plan. In practice, we'd reach for ScrapeGraphAI when we need to scrape varied sites that frequently change structure, avoiding selector maintenance. The partnership with Orthogonal for AI agent marketplaces and the MCP server show they're leaning into agentic use cases. However, the new Workspaces feature (v3.4.0) is a welcome addition for team collaboration. Where it bites: pricing can be opaque without the calculator, and stealth mode adds 5 credits extra per call, quickly inflating costs. Also, the brand analysis endpoint at 25 credits is expensive for casual use. But for targeted, LLM-powered extraction, ScrapeGraphAI delivers.