Hey Dev community,
While bootstrapping our platform SiteHunter, we ran into a massive technical bottleneck: how to scrape hundreds of local business sites and run deep Lighthouse audits concurrently without hitting memory ceilings or getting blocked by Cloudflare/AWS WAFs.
If you are building anything with headless browsers or data pipelines, here are the three architectural rules that saved us thousands in server bills:
Decouple the Scraper from the Parser: Never run heavy regex or DOM parsing inside the same process holding the Playwright page instance. Open the page, dump the raw HTML/metadata payload to a queue, and kill the browser instance immediately. Let a separate worker cluster parse the data asynchronously.
Dynamic Request Interception: Don't waste bandwidth downloading heavy images, web fonts, or tracking scripts (GTM, Facebook Pixel) when you only need the DOM structure and SEO meta tags. Block these assets at the network layer in Playwright using page.route().
Smart Lighthouse Throttling: Running full Lighthouse audits on unoptimized target sites will instantly spike your CPU to 100%. We had to implement a strict token-bucket algorithm to throttle concurrent audits and distribute the load across multiple stateless headless nodes.
We packaged this entire headless framework into SiteHunter Cloud, which we just launched on PH. It completely automates local business scraping, evaluates their code quality, and spits out instant technical SEO diagnostic reports you can use to pitch clients.
If you are working on scrapers or headless pipelines, what are you using to manage infrastructure limits? Let's talk architecture in the comments!
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