Three hours into debugging a broken scraper, the last thing you're thinking about is parsing speed. You're thinking about why the authentication flow suddenly requires a CAPTCHA, or why the site structure changed overnight, or why the selector that worked yesterday returns nothing today. The HTML parsing itself? That takes milliseconds either way.
Yet Cheerio dominates HTML parsing for web automation with nearly 30,000 GitHub stars and over 11 million weekly npm downloads—despite being 5-6x slower than alternatives in benchmarks. Meanwhile, faster options like node-html-parser sit at 1,200 stars and 4 million downloads.
Cheerio dominates with 30,000 stars and 11M weekly downloads despite being 5-6x slower than alternatives—revealing what actually matters in production web automation.
The gap tells you where the real bottlenecks live. Parsing isn't one of them.
Everything Else Takes Longer
When you're running web agents at scale, the parsing step—converting HTML strings into queryable structures—takes 2ms or 12ms. Everything else the web throws at you takes longer:
- Authentication flows spanning three redirects: 800ms
- Error recovery when sites change structure: seconds added
- Bot detection systems requiring careful rate limiting
- Regional variations demanding different extraction logic
- A/B tests showing different content to different users
At the volumes where web agents become infrastructure—thousands of sites, millions of runs—the parsing speed becomes invisible. The 10ms you might save gets lost in the hours you spend on everything else that breaks.
Cheerio wins because it optimizes for the real constraint: the human work of understanding, maintaining, and adapting to a constantly changing web.
Developer Velocity as Infrastructure
The jQuery-like API isn't just familiar. It's debuggable. You can scan a Cheerio selector chain and immediately understand what it's trying to extract. You can modify it quickly when the site structure changes. You can hand it to another developer who'll understand it without context.
Site maintenance is where teams actually spend their time. When you're maintaining extraction logic across thousands of sites, the bottleneck isn't how fast you parse HTML. It's how fast your team can adapt when sites change, debug when things break, and write new extractors when you add coverage.
CircleCI's scraping tutorial doesn't focus on parsing performance. It focuses on building CI/CD pipelines that catch breakages early, writing maintainable extraction code, and creating monitoring that surfaces issues before they impact operations. That's the real work of web automation at scale—the organizational challenge of keeping extraction logic working across hundreds of sites that change without warning.
The library with nearly 19,000 dependent packages wins because it's fast enough for the parsing step, and optimized for the steps that actually take time: reading code at 2am, modifying selectors when sites break, onboarding new developers who need to understand the extraction logic.
Teams choose Cheerio because they've learned where performance matters in their systems. For most web automation work, that's not the parsing layer. It's the layer where humans interact with code.