I’ve been working on a fairly large web scraping project for market research, and I keep hitting roadblocks with CAPTCHAs and IP blocks. It’s getting frustrating because just when I think I’ve got a stable workflow going, the target sites update their protection measures.
I’ve tried:
Using rotating proxies (expensive and still get detected)
Adding random delays between requests (helps a little)
User-agent rotation (websites seem to see through this now)
The most frustrating part is that different sites use different CAPTCHA systems, so there’s no one-size-fits-all solution. I’m wondering if anyone has experimented with using multiple AI models to analyze and bypass these protections?
I heard some people are using LLM-based approaches where different AI models look at the CAPTCHA from different perspectives and collaborate on a solution. Has anyone tried this? What’s your experience been with modern anti-scraping systems?
I was in the same boat last quarter when scraping competitor pricing data. The traditional proxy rotation wasn’t cutting it anymore - sites have gotten much smarter about detecting patterns.
What worked for me was Latenode’s multi-model approach. Instead of just rotating IPs, I set up a system using their 400+ AI models that analyzes each site’s protection patterns and dynamically adjusts the scraping behavior.
The game-changer was using different AI models in sequence - one to analyze the CAPTCHA type, another to solve it, and a third to mimic human-like browsing patterns. This rotating perspective approach means the scraper doesn’t have a consistent fingerprint that can be easily detected.
I’ve been running daily scrapes across 50+ sites for 3 months now with minimal blocks. The best part is I don’t have to constantly update the system as the sites change their protections - the AI models adapt automatically.
I’ve dealt with this extensively when building price comparison tools. CAPTCHAs have definitely gotten more sophisticated.
The most effective approach I’ve found combines a few techniques:
Browser fingerprint randomization - modern anti-bot systems check more than just user agents. They look at canvas fingerprints, WebGL, fonts, and dozens of other browser characteristics. You need to randomize all of these.
Behavioral patterns - timing of clicks, mouse movements, and scrolling patterns need to mimic human behavior. Sites track how you interact with elements.
Session management - maintain cookies and session data like a real user would.
As for CAPTCHA solving specifically, I’ve had success with specialized services that use a combination of machine learning and human solvers. They’re not perfect but get through about 85% of challenges at a reasonable cost.
The multi-model LLM approach is promising but still emerging. I’d be curious to hear how it’s working for others.
After struggling with this exact issue for months, I found a reliable solution for my e-commerce scraping project. The key insight was that modern anti-scraping systems aren’t just looking at individual requests - they’re analyzing patterns across multiple requests.
I created what I call a “behavioral fingerprint rotation” system. Rather than just changing IPs and user agents randomly, I created several complete “personas” - each with consistent browser fingerprints, cookies, session handling, and even realistic timing patterns between clicks and page loads.
The system rotates between these personas in a non-predictable way, which makes it much harder for anti-bot systems to detect patterns. Each persona maintains its own session state and history, just like a real user would.
For the actual CAPTCHAs, I found that using specialized CAPTCHA-solving services integrated into this workflow was more reliable than trying to build my own solving capability. The cost is justified by the much higher success rate.
The CAPTCHA and anti-bot landscape has evolved dramatically in the past few years. Modern systems use behavioral biometrics, browser fingerprinting, and machine learning to detect patterns that no human would exhibit.
In my experience building large-scale scrapers, the most effective approach is a multi-layered strategy:
Infrastructure diversity: Use residential proxies from multiple providers, not just data center IPs which are easily flagged.
Complete browser environments: Headless browsers that maintain full state, including cookies, localStorage, and IndexedDB.
Human-like interaction patterns: Implement realistic typing speeds, mouse movements with proper acceleration/deceleration curves, and natural navigation flows.
For sophisticated CAPTCHAs, I’ve found that a hybrid approach works best - automated solving for simpler challenges, with human-in-the-loop fallback for the more complex ones. This provides a good balance of cost and success rate.
The multi-model AI approach is interesting, but in my testing, it’s still not matching the success rates of specialized CAPTCHA-solving services.
residential proxies work better than datacenter ones. try using headless browser with full cookies and browser fingerprint management. captcha solving services worth the cost.