Businesses are increasingly scraping AI platforms to study what chatbots tell users—not just to collect information from the open web. A report published by TechRadar Pro on October 5, 2026, says Decodo’s analysis placed ChatGPT and Perplexity among the most-scraped websites for the first time in that analysis. The figures describe one vendor’s reported findings, not a universal measure of scraping activity.
Why are businesses scraping ChatGPT and other AI platforms?
Traditional web scraping can help organizations track what people search for or gather information from websites. Scraping AI platforms serves a different purpose: it can show businesses how chatbots describe brands, compare products, mention companies, and cite sources in answers.
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Decodo CEO Vaidotas Juknys described the shift to TechRadar Pro this way: “Two years ago, businesses scraped the web to see what people were searching for. Now they scrape AI platforms to see what people are being told.” The report presents this as an emerging use case, not evidence that every business monitors chatbot answers.
For a company, those answers may offer clues about how its products or brand appear in AI-generated responses. The same report quotes Juknys predicting that agents might eventually make decisions rather than simply read answers; that is a forecast, not an established description of how agents currently behave.
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What did the reported scraping ranking find?
TechRadar Pro’s October 5, 2026 report says Decodo’s analysis ranked TikTok first, ChatGPT third, and Perplexity fifth among the websites it examined. The reported shares were 18% for TikTok, 14% for ChatGPT, and 10% for Perplexity.
| Website | Reported position | Share of activity observed in Decodo’s analysis |
|---|---|---|
| TikTok | 1st | 18% |
| ChatGPT | 3rd | 14% |
| Perplexity | 5th | 10% |
TechRadar’s article does not give the sampling dates, sample, collection method, or denominator behind those figures. They should not be read as a share of all web scraping, all AI traffic, or all business activity. TechRadar Pro’s report attributes the analysis to Decodo.
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“AI crawling” can mean several different things
A request from an automated system does not necessarily mean a company is studying chatbot answers. Cloudflare separates AI-related crawling into three broad purposes, which can involve different bots and traffic patterns:
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- Training crawls: Gather web content for developing or training models.
- Search or retrieval crawls: Find content to support search features and, potentially, retrieval-augmented generation.
- User-action crawls: Fetch a page in response to a chatbot user’s question, to help produce an answer.
These categories describe why content is fetched; they are not interchangeable measures of businesses scraping AI platforms. In Cloudflare’s 2025 network observations, training traffic at peak reached as much as seven to eight times search crawling and 32 times user-action crawling. Those comparisons apply to Cloudflare’s observed traffic, not every website.
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How quickly is user-triggered AI crawling growing?
Cloudflare reported that user-action crawling rose more than 15-fold during 2025. It also observed ChatGPT-User request volume peak at up to 16 times its level at the start of that year. These are Cloudflare network observations, and bot activity can vary over time; they are not a census of all websites or AI requests.
Other providers report different measures and scopes. DataDome says it detected 52.7 billion AI agent and LLM crawler requests across its customer base from July 2025 through June 2026. It reports that AI traffic in that dataset grew 82.3% over the interval and made up 1.7% of the total web traffic it analyzed by June 2026. For the first half of 2026, DataDome attributed 46.3% of its observed AI traffic to Meta and 34.6% to OpenAI. These figures use DataDome’s traffic classification and customer base; they should not be combined with Decodo’s website ranking or Cloudflare’s crawler-specific measures.
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Cloudflare’s 2025 Radar Year in Review details its crawler categories and measurements. DataDome’s AI traffic trends report describes its customer-base findings.
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Does AI crawling send traffic back to the websites it fetches?
Not necessarily. A crawler’s request to fetch a page and a chatbot’s referral to that page are different events. Cloudflare measures one relationship between them using a crawl-to-refer ratio: relevant HTML requests associated with a platform divided by HTML referrals whose Referer header includes that platform’s hostname.
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In Cloudflare’s 2025 observations, the ratio varied widely by provider and over time. Anthropic’s ratio reached as high as 500,000:1; after May, its values were more consistent but remained roughly 25,000:1 to 100,000:1. OpenAI reached 3,700:1 in March. Perplexity’s later-2025 peak values generally stayed below 400:1, and below 200:1 from September onward.
These figures describe requests and referrals Cloudflare observed on its network, not the value of each referral or the full economic relationship between a platform and a website. A high ratio does not by itself show how useful any resulting visit was. Cloudflare explains the measurement and its limitations.
Can automated AI requests overload a website?
They can add operational load, even when the requests are not malicious. Fastly’s August 19, 2025 release, summarizing its Q2 2025 Threat Insights Report, says some user-triggered fetcher volumes exceeded 39,000 requests per minute. Fastly warned that surges can consume bandwidth and burden origin servers. The figure comes from activity observed across Fastly’s network and products; it is not a rate that every site should expect.
Fastly also reported that AI crawlers made up almost 80% of the AI bot traffic it observed in its mid-April to mid-July 2025 analysis. That finding has the same provider- and period-specific scope. As Fastly Senior Security Researcher Arun Kumar put it: “Whether scraping for training data or delivering real-time responses, these bots create new challenges for visibility, control, and cost.” Fastly’s release provides its findings and context.
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