🕷️ScrapingTools.dev
🚀 For Creators & Providers

List your B2B Tool

Building a Web Scraping API, proxy network, or data automation platform? Add your tool to our directory to reach thousands of engineers and data teams.

Share your affiliate signup or referral link so we can feature your official partner tracking.

If Featured is selected, we will review your tool and send a secure Stripe invoice upon approval to activate your top banner.

⚖️ Comparison 2026

Relevance AI vs ScrapeGraphAI

In-depth technical comparison to help you choose the right data infrastructure tool for your stack.

Option 1

Relevance AI

Relevance AI is a platform for building, orchestrating, and deploying autonomous AI agent workforces for sales research, scraping, and ops.

$199/mo
✓ Free Tier
Visit Relevance AI →
Option 2

ScrapeGraphAI

ScrapeGraphAI is an open-source Python library that uses LLMs and direct graph logic to extract web data by describing what you want in plain English.

$20/mo
✓ Free Tier
Visit ScrapeGraphAI →

📊 Comparison Table

FeatureRelevance AIScrapeGraphAI
Starting Price/mo$199$20★ Cheaper
Pricing Modelcreditspay_per_use
JS Rendering
CAPTCHA Bypass
Residential Proxies
Free Tier
Base Concurrency20 threads★ Higher20 threads
SDK & LanguagesPython, JavaScript, REST API IntegrationPython, REST API Integration

🎯 Relevance AI is best for

Operations, sales, and data teams wanting to deploy autonomous multi-agent systems for market research, company enrichment, and outreach.

🎯 ScrapeGraphAI is best for

Data scientists and Python engineers who want to extract structured web data using LLMs without maintaining CSS selectors or XPath rules.

🏆 The Verdict

Relevance AI is the recommended choice if you prioritize advanced feature depth.Operations, sales, and data teams wanting to deploy autonomous multi-agent systems for market research, company enrichment, and outreach.

ScrapeGraphAI stands out if your workload demands specialized infrastructure features.Data scientists and Python engineers who want to extract structured web data using LLMs without maintaining CSS selectors or XPath rules.