Most Scraped Websites of 2025: The Platforms Powering the AI Revolution

Which Platforms Should You Prioritize? 5 Key Questions

Before investing in scraping infrastructure or choosing data sources, answer these strategic questions to identify which platforms matter most for your use case:

• 1. What's your primary objective?

  • AI training (multimodal) → TikTok, YouTube (video + audio + text combined).
  • AI training (factual accuracy) →ScienceDirect, Crunchbase (authoritative sources).
  • E-commerce intelligence → Amazon, Walmart, Coupang (pricing, inventory, reviews)
  • Market research & trends → Google, Crunchbase (search intent, business data) .
  • SEO & content strategy → Google, YouTube (search rankings, trending content).

• 2. What data modalities do you need?

  • Multimodal (video + audio + text) → TikTok, YouTube.
  • Text-heavy structured data →Google, ScienceDirect, Crunchbase.
  • Product & transaction data → Amazon, Walmart, Coupang.
  • User-generated content → TikTok, YouTube, Airbnb (reviews, comments) .

• 3. What's your geographic focus?

  • US-centric operations → Amazon, Walmart, Google, eBay.
  • Asian market expansion →Coupang (South Korea and broader Asia).
  • Global/multilingual needs → TikTok, YouTube (worldwide coverage).
  • Regional intelligence → Mix platforms by market (Coupang for Asia, Walmart for US) .

• 4. What's your technical capability and risk tolerance?

  • Need high reliability (99%+ success rates) → Amazon, Walmart, Google, eBay, Crunchbase, Airbnb.
  • Can handle moderate complexity (91-97%) → YouTube, ScienceDirect, Coupang.
  • Advanced infrastructure available (87-90%) → TikTok (most sophisticated defenses).
  • Limited technical resources → Start with easier platforms, consider managed scraping solutions.

• 5. What's your operational scale?

  • Testing & research (< 10K requests/month) → Start with easier platforms (Google, eBay).
  • Production systems (10K-1M requests/month) → Multi-platform approach with robust infrastructure.
  • Enterprise AI training (1M+ requests/month) → Requires sophisticated proxy management and anti-bot evasion.
  • Real-time applications → Prioritize platforms with frequently updated content (TikTok, Google).

Your answers determine not just which platforms to target, but what infrastructure you'll need. Companies scraping TikTok at scale face fundamentally different technical challenges than those monitoring Amazon prices.

The Top 10 Most Scraped Websites of 2025

Based on comprehensive industry analysis from scraping service providers, bot traffic analytics, and market intelligence, here are the platforms companies target most frequently:

#1 TikTok

Category:Video/Social Media

Why It Dominates: AI companies desperately need short-form video content that combines visual, audio, and text data with engagement signals

With 1.5 billion active users and an algorithm-driven discovery system, TikTok offers exactly what companies building multimodal AI require. The platform's unique value lies in providing real-time cultural signals—what resonates with audiences, how trends spread, and how people communicate across different demographics and regions.

Companies target TikTok for video metadata, hashtag trends, creator analytics, engagement metrics, audio usage patterns, comment sentiment, and geographic trending data. Beyond AI training, businesses use TikTok data for trend forecasting, consumer behavior analysis, and content strategy development.

Technical Reality:TikTok employs some of the most sophisticated anti-bot defenses in the industry, including behavioral analysis that tracks mouse movements and scroll patterns, advanced device fingerprinting, and aggressive rate limiting. Industry reports suggest average success rates hover around 87% even for well-configured scraping operations, making it the hardest major platform to access reliably at scale. Companies scraping TikTok typically require advanced proxy rotation, realistic browser fingerprinting, and behavioral simulation capabilities.

#2 Google

Category:Search Engine

Technical Profile: 99.98% success rate, moderate anti-bot measures

Google's dominance as a data source isn't surprising—it processes 13.7 billion searches daily and provides unmatched insights into search intent, consumer behavior, and market demand across every industry and geography. What is surprising is that it's no longer #1, overtaken by video platforms for the first time.

Companies collect search result rankings, featured snippets, local business listings, Google Shopping data, image search results, news aggregation, and auto-suggest keywords. Use cases span from traditional SEO analysis to training AI agents that need real-time knowledge updates. Financial institutions scrape Google News for sentiment analysis, while e-commerce companies monitor Shopping results for competitive pricing intelligence.

The platform's high success rate stems from relatively predictable anti-bot measures compared to newer video platforms. However, Google continues evolving its detection systems, particularly for high-volume automated requests.

#3 Amazon

Category:E-Commerce

Business Impact: 60% of retailers scrape Amazon; average 30% sales increase from pricing optimization.

Amazon dominates e-commerce intelligence despite dropping from its traditional #1 position. Industry research consistently shows that businesses leveraging Amazon data see significant competitive advantages, particularly in dynamic pricing strategies.

The use case has evolved beyond simple price monitoring. Companies now scrape product listings, customer reviews (increasingly for AI sentiment analysis), seller information, inventory availability, best-seller rankings, sponsored product data, and Q&A sections. This comprehensive data supports everything from competitive intelligence to training conversational AI that needs to understand how customers discuss products.

While success rates remain near 100% for sophisticated operations, Amazon has significantly strengthened its fingerprinting and behavioral analysis. The platform quickly catches poorly designed automation, making infrastructure quality critical for sustained access.

#4 YouTube

Category:Video/Social Media

AI Training Value: Essential for speech recognition, video summarization, multimodal understanding.

YouTube's prominence reflects insatiable demand for video and audio training data. With 500 hours of content uploaded every minute and 2.7 billion monthly users, the platform offers training data depth that short-form platforms can't match—longer-form content, educational material, detailed tutorials, professional production, and diverse languages.

Companies collect video metadata, engagement metrics, channel analytics, trending videos, comment threads, transcripts, audio tracks, and the video content itself for computer vision training. The platform serves organizations training speech recognition systems, video summarization algorithms, and multimodal AI that must understand context across different media types.

Technical Challenge: YouTube has significantly improved bot detection capabilities. Headless browser identification and behavioral analysis have pushed average success rates to approximately 91%, requiring more sophisticated approaches than simple HTTP requests. Companies targeting YouTube typically deploy headless browsers with realistic fingerprints and natural interaction patterns.

#5 Walmart

Category:E-Commerce

Market Position: 25% of US online grocery sales.

As America's largest retailer, Walmart provides essential e-commerce intelligence, particularly for companies operating in or targeting the US market. The platform's value increases exponentially when combined with data from Amazon, Target, and regional marketplaces for comprehensive cross-platform competitive analysis.

Companies scrape product availability, pricing data, customer reviews, seller marketplace information, seasonal trends, grocery and pharmacy data, and local market pricing variations. Cross-platform analysis helps businesses understand how pricing differs across major retailers and identify opportunities where competitors may be underserving customer needs.

Success rates remain high (99.98%) for properly configured operations, though Walmart has begun deploying more sophisticated bot detection in response to increased scraping activity.

#6 Coupang

Category:E-Commerce

Strategic Importance: Gateway to Asian e-commerce intelligence.

Coupang's prominence reflects the globalization of data collection strategies. As South Korea's leading online retailer with growing influence across Asia, Coupang provides critical insights into consumer behavior in one of the world's most dynamic e-commerce markets—insights that US-centric data from Amazon and Walmart simply cannot provide.

Companies collect product listings that reveal Korean market preferences, pricing strategies adapted for Asian consumers, cross-border shipping data, local brand performance metrics, category-specific trends, and mobile commerce patterns (Asia leads globally in mobile shopping adoption). Businesses expanding internationally recognize that assuming US consumer behavior translates globally leads to expensive mistakes.

Average success rates around 95% reflect moderate anti-bot sophistication—more advanced than traditional US retailers but less complex than TikTok or YouTube.

#7 eBay

Category:E-Commerce

Unique Value: Only major platform revealing auction dynamics and price elasticity.

eBay offers value proposition that fixed-price retailers can't match: auction format data revealing what consumers actually pay versus list prices. This price elasticity data helps businesses understand willingness-to-pay across different product conditions, categories, and market conditions.

Companies scrape auction results, historical sales data, seller performance metrics, product condition information, international shipping patterns, category performance trends, and comparative pricing between auction and buy-it-now formats. The secondary market intelligence proves particularly valuable for businesses in collectibles, electronics, fashion, and other categories where pre-owned markets significantly influence new product pricing.

Success rates near 100% reflect eBay's relatively traditional anti-bot approach compared to newer platforms investing heavily in AI-powered detection.

#8 ScienceDirect

Category:Professional/Academic

Why It Matters: Factual accuracy for AI systems requiring authoritative sources.

ScienceDirect's appearance in the top 10 represents perhaps the most significant shift in scraping priorities: the demand for authoritative, factually accurate training data. As AI systems move beyond generating plausible-sounding text to providing reliable information in medical, scientific, and technical domains, peer-reviewed sources become not just valuable but essential.

Companies collect research paper abstracts and full text, citation networks showing how research builds on prior work, author collaboration patterns, emerging research trends before they reach mainstream awareness, technical terminology and precise definitions, and publication timelines revealing when discoveries entered the literature. Organizations training AI requiring factual accuracy—medical diagnosis assistants, scientific research tools, technical documentation generators—cannot afford the reputational risk of unreliable information.

Success rates around 97% reflect academic paywall protections and access controls rather than sophisticated bot detection, though institutions are increasingly concerned about large-scale automated access.

#9 Crunchbase

Category:Professional/Business Intelligence

Use Case: Market intelligence, competitive analysis, investment research.

Crunchbase provides comprehensive structured business data that supports both AI training and strategic analysis. With detailed information on startups, funding rounds, acquisitions, and industry trends, the platform serves companies building market intelligence systems and conducting competitive research.

Companies scrape funding rounds and investment amounts, company growth trajectories, founder and executive information, industry trend data, M&A activity, startup ecosystem health metrics, and geographic investment patterns. The data helps investment firms identify emerging opportunities, enterprises track competitive threats, and AI systems understand business relationships and market dynamics.

Success rates near 99% stem from the platform's business model—Crunchbase actually wants this information circulating (within reasonable limits) as it drives their freemium conversion strategy.

#10 Airbnb

Category:Travel/Hospitality

Business Application: Pricing optimization, demand forecasting, competitive benchmarking.

Airbnb rounds out the top 10 as a critical travel industry data source. As one of the largest peer-to-peer accommodation platforms, Airbnb provides pricing trends, availability patterns, and traveler preference data across global markets that traditional hotel data doesn't capture.

Companies collect property listings and their characteristics, pricing trends across different locations and seasons, host performance metrics, guest review sentiment revealing service quality insights, seasonal demand patterns for tourism planning, and alternative accommodation growth tracking the shift from traditional hotels.

Travel companies, hospitality groups, property management firms, and real estate investors use this data to benchmark competitiveness, optimize their own pricing strategies, and identify emerging destination trends before they saturate.

Success rates near 100% reflect relatively straightforward anti-bot measures focused primarily on rate limiting rather than sophisticated behavioral analysis.

Technical Challenges: Why Success Rates Vary Dramatically

One striking finding from industry analysis is the dramatic variation in scraping success rates across platforms—ranging from 87% for TikTok to 99-100% for traditional e-commerce sites. This isn't random. Platforms offering the most valuable AI training data have invested heavily in sophisticated anti-bot defenses:

• Tier 1 - Highest Success (99-100%): Google, Amazon, Walmart, eBay, Crunchbase, Airbnb.
• Tier 2 - Moderate Success (91-97%):YouTube, ScienceDirect, Coupang.
• Tier 3 - Challenging (87-90%):TikTok.

The pattern reveals which platforms view bot traffic as existential threats worthy of significant investment. TikTok and YouTube, sitting on enormously valuable AI training data, deploy behavioral analysis, advanced fingerprinting, and real-time detection systems. Industry data suggests sophisticated platforms can now block 82.3% of poorly configured automated traffic.

For companies scraping at scale, these technical realities mean infrastructure quality matters enormously. Managing proxy rotation, maintaining realistic browser fingerprints, simulating human behavioral patterns, and handling sophisticated anti-bot detection becomes complex quickly. This explains the growing adoption of managed scraping solutions that handle these challenges automatically while maintaining high success rates across platform tiers.

Companies attempting DIY scraping of Tier 3 platforms often underestimate the engineering effort required. What starts as "we'll just use Puppeteer with some proxies" evolves into full-time infrastructure management as platforms deploy new countermeasures.

What This Means for Different Industries

• AI and ML Companies: The dominance of video platforms isn't optional anymore—it's table stakes. Text-only training data produces models that fundamentally can't compete with systems trained on TikTok and YouTube's combined video, audio, and text content. Companies building conversational AI, recommendation systems, or content moderation tools need multimodal understanding that only comes from diverse video data.
• E-Commerce Businesses: Cross-platform intelligence has moved from competitive advantage to necessity. The 81% of retailers using automated price scraping have measurable advantages over competitors relying on manual research or single-platform monitoring. Comprehensive intelligence requires combining data from Amazon, Walmart, regional platforms like Coupang, and auction dynamics from eBay. Single-source strategies miss critical market movements.
• Marketing and SEO Teams: While Google remains essential for search rankings and keyword research, understanding what content actually resonates with audiences now requires scraping TikTok and YouTube for cultural signals that search data alone doesn't capture. The platforms where people discover and share content have shifted—your data sources must shift accordingly.
• Financial Services and Consulting: The prominence of ScienceDirect and Crunchbase reveals that alternative data sources increasingly drive investment decisions and strategic planning. Traditional financial data combined with real-time sentiment from social platforms and structured business intelligence from Crunchbase provides timing advantages in identifying market opportunities before they become obvious.
• Enterprise AI Development: Companies building customer service chatbots, recommendation engines, or autonomous agents need the diverse training data these platforms collectively provide. Single-source training produces narrow-capability systems. The best conversational AI combines natural language patterns from social platforms, product knowledge from e-commerce sites, and authoritative information from academic sources.

Looking Forward: What to Expect in 2026 and Beyond

Based on current trajectories and emerging patterns, several trends will likely reshape the landscape further:

• Platform Convergence and TikTok Shop: TikTok's aggressive expansion into e-commerce ("TikTok Shop") could make it even more dominant by combining video content, social signals, and transaction data in one platform. When a single source provides multimodal content plus purchase behavior, its value for AI training increases exponentially. Watch for TikTok to potentially widen its lead over competitors.
• Escalating Anti-Bot Arms Race: Platforms generating high-value AI training data will continue investing heavily in detection systems. Expect success rates on top-tier platforms to decline further—TikTok may drop below 85%, YouTube below 90%—pushing more companies toward managed scraping infrastructure that can maintain reliability despite evolving defenses. The technical barrier to entry for sophisticated scraping will keep rising.
• Regional Platform Expansion: Non-US platforms will increasingly appear in global rankings as companies need multilingual AI training data and international market intelligence. Expect Southeast Asian e-commerce platforms, European social networks, and Latin American marketplaces to gain prominence alongside established Western platforms.
• Real-Time and Continuous Collection: AI agents requiring up-to-date information will drive demand for continuous scraping rather than periodic batch collection. This favors platforms with frequently updated content (TikTok, Google News, real-time inventory systems) over more static sources. Infrastructure requirements shift from "scrape once daily" to "scrape continuously with change detection."
• Authoritative Source Premium: As AI-generated content proliferates across the web, verifying information against authoritative sources like ScienceDirect becomes more critical, not less. Expect academic publishers, government databases, and verified business registries to see increased scraping activity as companies prioritize training data quality over quantity.
• API-ification Trends: Some platforms may respond to scraping pressure by offering official APIs for specific use cases—potentially monetizing data access rather than fighting an unwinnable technical battle. However, APIs typically provide only sanitized subsets of available data, meaning comprehensive intelligence will still require scraping.

The Bottom Line

The 2025 web scraping landscape reveals a fundamental restructuring driven by AI training data requirements. Video platforms capturing 38% of activity, TikTok's dominance, and the emergence of ScienceDirect and Crunchbase in the top 10 all tell the same story: companies need diverse, multimodal content at scales that would have seemed impossible just three years ago.

Traditional use cases—price monitoring, SEO analysis, competitive intelligence—remain important and continue growing. But AI training has become the dominant force reshaping what gets scraped, where companies get it, and how sophisticated the infrastructure must be to access it reliably.

For businesses building data pipelines in 2025 and beyond, several strategic imperatives emerge:

Diversify data sources beyond traditional platforms.No single source provides the breadth of content modern AI systems require. Multi-platform strategies are now standard, not exceptional.

Invest in infrastructure capable of handling sophisticated anti-bot defenses. The gap between platforms is widening—TikTok's 87% success rate versus eBay's 100% reflects fundamental differences in technical complexity that simple scrapers can't bridge.

Prioritize platforms offering multimodal content.Text-only strategies produce limited-capability AI systems. Video, audio, and visual data have become essential, not optional.

Plan for continuous escalation.The technical arms race between scrapers and anti-bot systems will intensify. Today's working solutions may fail tomorrow. Infrastructure must be adaptable, not static.

Consider build versus buy carefully.For companies scraping TikTok, YouTube, or other sophisticated platforms at scale, the engineering effort required to maintain high success rates often exceeds the cost of managed solutions that handle complexity automatically.

Whether you're training customer service chatbots, building pricing algorithms, developing market intelligence systems, or conducting competitive research, the platforms on this list represent the essential data sources for competing in the AI era. The question facing every business isn't whether to collect this data—it's whether your infrastructure can reliably access it at the scale and quality your applications demand.

As we continue tracking these trends throughout 2025 and into future years, we'll publish updated analyses showing how the landscape evolves. For companies serious about data-driven decision making and AI development, understanding which platforms matter most—and why—has never been more critical.

Methodology Note:This analysis is based on publicly available data from web analytics platforms, industry surveys, scraping service provider reports, bot traffic analysis, and third-party market research published throughout 2024 and early 2025. Rankings reflect aggregated patterns across the data extraction ecosystem rather than any single proprietary dataset. We've synthesized data from multiple credible sources to provide the most comprehensive view of current scraping priorities and trends.