Businesses are increasingly reported to scrape AI platforms such as ChatGPT and Perplexity to study what their systems tell users: which products they compare, how they describe brands, and which sources they cite. This is an emerging use case, not evidence that every business is doing it. A 2026 ranking reported by TechRadar puts ChatGPT third and Perplexity fifth in one analysis, but its percentages cannot be treated as shares of all web scraping or AI traffic.
Why are businesses scraping AI platforms?
Traditional web scraping often gathers information from websites to track prices, products, search results, or public discussion. Scraping AI platforms can answer a different question: what does an AI service tell people when they ask about a brand, product, or category?
Businesses may monitor answers for product comparisons, brand descriptions, how often a company is mentioned, and which sources appear in citations. That can help them observe how their products or competitors are represented in AI-generated responses. It is a reported and developing business use, not a universal practice.
Decodo CEO Vaidotas Juknys described the change in a TechRadar report: “Two years ago, businesses scraped the web to see what people were searching for,” while now they scrape AI platforms “to see what people are being told.” He also forecast that agents might eventually make decisions rather than just read answers; that is a prediction, not an established outcome.
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What does the reported ranking actually show?
TechRadar reported on October 5, 2026, that Decodo’s analysis ranked ChatGPT third, at 14% of the activity it observed, and Perplexity fifth, at 10%; TikTok led at 18%. These figures describe that analysis only. The report does not disclose its sampling dates, collection method, exact sample, or denominator, so the percentages cannot be generalized as a share of all web scraping, all AI traffic, or business activity.
The ranking is also distinct from measurements of crawler requests or referrals. It signals that ChatGPT and Perplexity appeared prominently in Decodo’s reported analysis, but does not establish how many websites were scraped, how often the platforms were accessed, or what fraction of the internet’s automated traffic they represent.
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AI crawling can mean three different things
“AI scraping” is not one uniform activity. Cloudflare distinguishes crawling by purpose, and the distinction matters because a request made to build a model is not the same as a page fetch triggered by a user’s question.
- Training crawls: gather material that may be used to develop or train models.
- Search and retrieval crawls: collect or refresh content to support search features and potentially retrieval-augmented answers.
- User-action visits: fetch pages in response to a chatbot user’s question, so the service can use current page content in a response.
Cloudflare’s 2025 Year in Review says training traffic reached as much as seven to eight times search crawling and 32 times user-action crawling at peak in its observations. It also reports that user-action crawling overall increased more than 15-fold during 2025, while ChatGPT-User peak request volume reached up to 16 times its beginning-of-year level. These are Cloudflare network observations, not a census of all websites, and crawler activity can vary over time.
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Does scraping send visitors back to the websites being crawled?
Not necessarily. A crawler request is a request to fetch content; a referral is a visit arriving at a site from an AI platform. One does not guarantee the other. A platform may retrieve pages without sending a user to them, while an answer that cites a source may still generate a referral if the user clicks through.
Cloudflare measures this relationship with a crawl-to-refer ratio: relevant HTML requests associated with a platform divided by HTML referrals whose Referer header includes that platform’s hostname. Its 2025 measurements show that the ratio differs substantially by provider and shifts with both crawling and referrals:
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- Anthropic’s ratio reached as high as 500,000:1 in the measured period. After May, values were more consistent but remained roughly 25,000:1 to 100,000:1.
- OpenAI’s ratio 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 are ratios observed on Cloudflare’s network, not a measure of the economic value of referrals or the full relationship between a platform and every site it accesses. A high ratio means many observed crawl requests per observed referral; it does not, by itself, show whether a referral was valuable.
How large is AI-related web traffic?
Other providers report different kinds of measurements, with different customer bases and definitions. They should not be combined into a single estimate of “AI scraping.”
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| Source and period | Reported measure | How to read it |
|---|---|---|
| DataDome, July 2025–June 2026 | 52.7 billion AI agent and LLM crawler requests detected across its customer base; AI traffic grew 82.3% over the interval and made up 1.7% of analyzed traffic by June 2026. | DataDome’s customer-base and traffic taxonomy, not a count or share for the entire web. |
| DataDome, H1 2026 | Meta accounted for 46.3% and OpenAI for 34.6% of the AI traffic it observed. | Attribution within DataDome’s AI-traffic classification, not the Decodo ranking or a universal platform share. |
| Fastly, mid-April to mid-July 2025 | AI crawlers made up almost 80% of AI bot traffic observed in its analysis; some user-triggered fetcher volumes exceeded 39,000 requests per minute. | Fastly’s network and product observations. The request-volume figure describes some cases, not a typical rate for every site. |
Fastly said that surges of user-triggered fetches can consume bandwidth and put pressure on origin servers. Its measurements reflect activity visible to Fastly, not the experience of every website. The company also quoted Senior Security Researcher Arun Kumar: “Whether scraping for training data or delivering real-time responses, these bots create new challenges for visibility, control, and cost.”
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Can automated requests overload a website?
Yes. Automated traffic can create operational load even when the requests are not malicious. A large or rapid burst of page fetches can consume bandwidth, use server resources, and compete with ordinary visitors. Fastly’s report of some user-triggered fetcher volumes above 39,000 requests per minute illustrates the potential scale observed in particular cases on its network; it is not a universal rate or a threshold at which every site will fail.
The practical impact depends on request rate, the pages being fetched, caching, infrastructure capacity, and how traffic is distributed. A site operator needs visibility into request patterns to distinguish useful or user-triggered fetches from traffic that is abusive or simply too costly to serve. The cited measurements do not establish that all scraping is malicious or that a single control will stop every crawler.
What website operators and businesses should take away
- For businesses monitoring AI answers: treat observed answers, mentions, comparisons, and citations as a developing source of competitive and brand information, not as a complete or fixed measure of what all users see.
- For site operators: distinguish training, search/retrieval, and user-action traffic when evaluating crawler behavior; request volume alone does not describe purpose or referral value.
- For readers interpreting statistics: check who measured the traffic, what their network or customer sample covers, which period is included, and whether the metric counts requests, rankings, or referrals.
TechRadar’s October 5, 2026 report describes the Decodo ranking and business-monitoring rationale. Cloudflare’s 2025 Radar Year in Review explains crawler categories, activity trends, and crawl-to-refer ratios. Fastly’s August 19, 2025 release summarizes its Q2 threat research, and DataDome’s 2026 AI traffic trends presents its customer-base measurements.
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