The Web Scraping Insider #2

👋 Hello there!

Welcome to the second edition of The Web Scraping Insider, by the ScrapeOps.io team. 

With this newsletter, get a zero-fluff roundup of the most interesting content and news from the world of professional web scraping straight to your inbox every week.

We hope you enjoy it, and we’re always open to your feedback and suggestions.

Let’s get to it!

🧠 Claude + Cursor AI Scraping Assistant: Your AI-First Scraping Workbench?

The Web Scraping Club dropped a very interesting “Cursor AI Scraping Assistant”, which stitches Anthropic’s Claude LLM into the Cursor IDE via a custom MCP server. From plain-language prompts, it analyses any site, spins up a Scrapy or Camoufox spider scaffold, discovers XPath/CSS selectors, configures settings, and even runs tests, all tailored to your codebase style.

💡 It’s Still Experimental, But This Could Be A Game Changer

• 🤖 AI-driven scaffolding slashes development time. Autocomplete and full-spider generation from prompts can replace hours of manual boilerplate.

• 🔍 End-to-end site analysis baked in. Live HTML fetch, CSS stripping, JSON/schema.org detection, and selector generation turn exploration into a single command.

• 🛡️ Built-in anti-bot countermeasures. Automatic detection of protections (Cloudflare, PerimeterX, etc.) and injection of stealth browser settings keep your scrapers running.

• 🚀 IDE-integrated testing & iteration. Trigger Camoufox or Scrapy runs right from Cursor, inspect logs, tweak rules, and re-run without context switching.

Here is a breakdown of the project’s main components:

👀 Project Structure

• /.cursor/rules/ — your custom Claude “playbook” in .mdc files

• /MCPfiles/ — MCP handlers like xpath_server.py and Camoufox_template.py

• /templates/ — boilerplate for Scrapy spiders, pipelines, and settings

🧰 Interesting Cursor Rules

• prerequisites.mdc — sets project root, Python env, and imports

• website-analysis.mdc — orchestrates HTML fetch, cookie dumps, and CSS stripping

• scrapy-step-by-step-process.mdc — high-level workflow for project init → parse → pipeline

• scrapy.mdc — embeds your Scrapy best practices (retries, logging, user-agents)

• scraper-models.mdc — defines templates for PLP vs. PDP item models

🔥 MCP Endpoints

• fetch_page_content(url, html_path, cookies_path) — headful Camoufox fetch with stealth profiles

• strip_css(in_file, out_file) — regex-driven removal of styles for cleaner DOM

• generate_xpaths(template) — passes HTML to Claude to emit field-specific selectors

• write_camoufox_scraper(template, url, html_path) — fills in a Camoufox script with mapping logic

🧪 Still Experimental, but Highly Promising. With Cursor rules encoding your team’s best practices and an MCP server executing real commands, this setup is a big step toward fully autonomous, LLM-powered scraper development in 2025.

👉 Github Repo: AI-Cursor-Scraping-Assistant 

👉 Article: Build your web scraping assistant with Claude and Cursor

🔥📊 Why ELK Stack Should Be Your Scraping Monitoring Backbone

For serious scrapers, this DevOps guide shows why ELK (Elasticsearch + Logstash + Kibana) isn’t just for ops teams, it’s a game-changer for running, debugging, and scaling scraping projects.

💡 What Seasoned Scrapers Should Take From This

• 🕵️‍♂️ Scraping at scale without logs is flying blind. ELK turns raw scraper logs into structured, searchable, and actionable insights, so you can fix failures fast and spot patterns before they become outages.

• 🛠️ Centralized logging beats local logs every time. No more SSH-ing into servers or piecing together spider crashes. Ship everything to Elasticsearch and analyze failures, slowdowns, or bans in one place.

• 📈 Kibana dashboards = scraper ops control centers. Visualize proxy error rates, spider runtimes, site blockages, and more, live. Perfect for operational reviews or catching issues before clients even notice.

• ⚡ Logstash gives you parsing superpowers. Standardize messy logs across different spiders and frameworks so your dashboards stay clean and your alerting rules actually work.

• 🚨 Better observability = faster iteration. Catching subtle anti-bot trends early means less downtime, less wasted proxy spend, and happier clients.

👉 Read the full guide on Using ELK Stack for Web Scraping Monitoring

🧪 The True Cost of Browser-Based Web Scraping: DIY vs Managed

The team at Blat did a very interesting deep dive into the costs of hosting your own headless browsers for scraping versus using the managed browsers built into many proxy solutions.

🔍️ What Blat’s Article Says?

• 🛠️ DIY Can Be Cheaper. Blat benchmarks JS-rendering costs on Scaleway serverless (≈$0.240/1,000 req) and virtual machines (≈$0.085/1,000 req), then shows their own turnkey JS-rendering at just $0.364 per 1,000 requests. Demonstrating that the raw operational cost of running your own browsers can work out cheaper than 3rd party browser integrations.

• ⚖️ DIY Breakeven Volumes. Calculates a breakeven point of ~108 M req/month (~3.6 M/day) to justify vs. Blat for Serverless setups, and ~48 M req/month (~1.6 M/day) for VMs. However, they are factoring in 2 engineers on $80k salaries.

• 📈 Mentions Hidden Overheads. Highlights how proxy spend can dwarf the compute savings, but doesn’t factor these costs in.

👉️ Our Additions

Blat’s article is good, but to really make it a valuable analysis, we felt it should incorporate proxy costs and the 2 engineers cost seems excessive, so here is the expanded analysis.

• Costs are calculated per 1,000 requests.

• Datacenter proxies ($0.40/GB), Residential proxies ($2/GB) and assuming 1 page consumes 500kb of bandwidth after slimming down the response.

• Proxy API prices are based on 1M API credit plan for $99/month, with JS rendering costing 5 API credits and residential proxies + JS rendering costing 25 API credits. This is the pricing of cheaper Proxy API’s (Scrapingbee, Scrapedo, etc.)

• 🕵️‍♂️ Proxy make a big difference. Adding datacenter proxies 2-3X the cost of hosting your own browser stack, whilst adding residential proxies adds $1 per thousand requests. Proxy APIs roughly match the costs of serverless + DC proxies, but are ~2X more expensive when used with residentials.

• 🛠 Engineering Overhead. Not factored in above, but developing and maintaining your own browser fleet isn’t “set it and forget it.” You’ll need to handle updates, scale orchestration, anti-bot evasion tactics, and occasional break-fix cycles.

• ⚖️ Updated Breakeven Volumes. Assuming you need to spend 20 hours per month at $40/hour to maintain your browser stack. To justify switching from a managed browser + proxy service to your own VM-based DIY browser stack, you should be scraping a minimum of +77M pages/month with DC proxies, or +2M pages/month with residential proxies.

Takeaway: In short, unless you’re scraping truly astronomical volumes (≥ 80 M pages/mo) or need full residential–proxy control at mid‐range volumes (≈ 2–10 M pages/mo), a managed JS-rendering service from a Proxy API will save both money and developer hours.

👉 Learn more here.

🛡️ Free Proxies Reality Check: 1,200 Proxies, ~2% Success

We tested over 1,200 free proxies from four major free proxy sources, ProxyScrape, ProxyNova, Geonode, and Free Proxy List, and found that less than ~2% actually worked reliably.

Despite the temptation of "free" resources, the data paints a harsh picture: free proxies are nearly unusable for real-world scraping.

💡 Lessons Learned? Free proxies are not even worth testing.

• ⚠️ Free proxies are a false economy. Between 95–100% failure rates, connection errors, and location mismatches, free proxies cost more in lost time and broken scrapers than they save.

• 🕵️‍♂️ Quality is basically non-existent. Even among the few that connected, many provided incorrect geolocation data, making them unreliable for geo-targeted scraping.

• 💸 Paid still wins by a landslide. Even basic paid datacenter proxies outperform free proxies by orders of magnitude in success rate, speed, and stability.

• 🔬 Real-world testing beats assumptions. Instead of relying on hope, this test puts hard numbers behind what most experienced scrapers already suspected.

👉 See the full Free Proxies Benchmark Report

🚀 Until Next Time...

That’s a wrap for this issue of The Web Scraping Insider.

If you found this edition interesting, forward it to a fellow scraper.

We’ll be back soon with more deep dives, scraped truths, and tactical guides from the front lines of the data extraction world.

Ian from ScrapeOps