Built to provide research that’s more actionable and strategically valuable, The Business Research Company’s 2026 market reports include market attractiveness analysis, total addressable market sizing, company benchmarking matrices, interactive Excel dashboards, broader supply chain intelligence, emerging startup tracking, and in-depth product insights.
Artificial Intelligence (AI)-Driven Web Scraping Market Poised To Hit $23.7 Billion By 2030 With A 23.5% CAGR
The artificial intelligence (AI)-driven web scraping market has experienced substantial expansion in recent years. Projections indicate it will expand from $8.24 billion in 2025 to $10.20 billion in 2026, achieving a compound annual growth rate (CAGR) of 23.8%. This historical growth can be linked to factors such as increasing volumes of digital data, heightened e-commerce activity, a surge in demand for competitive intelligence, the spread of cloud-based scraping tools, and wider adoption across various industries.
The artificial intelligence (AI)-driven web scraping market is poised for substantial expansion over the next few years. This market is projected to attain a value of $23.70 billion by 2030, demonstrating a compound annual growth rate (CAGR) of 23.5%. This anticipated growth during the forecast period is driven by factors such as the increasing need for real-time business intelligence, the growing adoption of artificial intelligence and machine learning technologies, an escalating requirement for dynamic pricing and market oversight, the expansion of no-code/low-code scraping platforms, and heightened demand from small and medium enterprises. Key trends expected in this period include technological progress in artificial intelligence (AI) and machine learning, innovations in automated data extraction tools, the development of cloud-native and scalable scraping platforms, dedicated research and development in anti-bot evasion and proxy networks, and enhanced integration with real-time analytics and decision-making systems.
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Artificial Intelligence (AI)-Driven Web Scraping Market Opportunity Drivers: What’s Unlocking New Revenue Potential?
The increasing uptake of AI-powered decision-making tools is anticipated to drive expansion in the artificial intelligence (AI)-driven web scraping market. These AI-powered decision-making tools represent software systems that utilize artificial intelligence, including machine learning and predictive analytics, to automate and refine business decisions and insights. This surge in adoption stems from escalating enterprise digitalization and the necessity for strategic decisions informed by data. Artificial intelligence (AI)-driven web scraping complements AI-powered decision-making tools by automatically gathering and structuring extensive real-time data from various online sources. It enhances the precision and promptness of analytics by supplying machine learning models with current insights, thereby facilitating swifter, data-driven strategic decisions. A notable example highlights this trend: in January 2025, Eurostat, the statistical office of the European Union, reported that in 2024, 13.5% of enterprises with 10 or more employees employed AI technologies, an increase from 8.0% in 2023, reflecting a 5.5 percentage-point increase. Consequently, the growing acceptance of AI-powered decision-making tools is propelling the advancement of the artificial intelligence (AI)-driven web scraping market.
Artificial Intelligence (AI)-Driven Web Scraping Market Breakdown By Product Type And Application
The artificial intelligence (AI)-driven web scraping market covered in this report is segmented –
1) By Scraping Type: Static Web Scraping, Dynamic Web Scraping, Application Programming Interface Scraping, Image And Text Recognition
2) By Deployment Type: On-Premises, Cloud, Hybrid
3) By Organization Size: Small Enterprises, Medium Enterprises, Large Enterprises
4) By Application: Price Monitoring, Market Intelligence, Lead Generation, Data Mining
5) By Industry Vertical: E-Commerce, Financial Services, Healthcare, Manufacturing, Retail, Technology
Subsegments:
1) By Static Web Scraping: Manual Static Page Extraction, Automated Static Content Parsing, Hypertext Markup Language Structure Based Extraction, File Based Static Data Retrieval, Script Driven Static Data Collection
2) By Dynamic Web Scraping: Browser Automation Extraction, Java Script Rendered Content Extraction, Interactive Page Element Extraction, Form Submission Based Extraction, Dynamic Hypertext Markup Language Content Parsing
3) By Application Programming Interface Scraping: Public Application Programming Interface Data Extraction, Private Application Programming Interface Data Extraction, Rest Application Programming Interface Based Data Collection, Graph Query Language Application Programming Interface Based Extraction, Automated Application Programming Interface Response Parsing
4) By Image And Text Recognition: Optical Character Recognition Based Data Extraction, Image Pattern Recognition Extraction, Document Image Data Capture, Visual Content Based Data Identification, Text Recognition From Multimedia Content
Artificial Intelligence (AI)-Driven Web Scraping Market Innovation Trends Shaping Future Development
Leading companies in the artificial intelligence (AI)-driven web scraping market are prioritizing the development of advanced platforms, such as AI-powered low-code tools, to enhance efficiency, improve accessibility, and lessen technical barriers and development timelines. AI-powered low-code tools are defined as platforms that utilize artificial intelligence to automate the extraction and structuring of web data based on natural language descriptions, eliminating the need for manual coding. For example, in July 2025, Oxylabs.io, a Lithuania-based web-intelligence and proxy services company, launched AI Studio. This is a new collection of AI-powered tools designed to simplify the process of finding, collecting, and preparing web data for use through natural language prompts. It includes functionalities like AI-Crawler and AI-Scraper, enabling seamless data extraction from multiple or single pages without requiring operator intervention. The platform also incorporates tools such as Browser Agent for dynamic interactions and AI-Search for web queries, thereby extending its utility and reducing operational complexity for developers, product teams, and data analysts.
Artificial Intelligence (AI)-Driven Web Scraping Market Leading Players And Competitive Positioning
Major companies operating in the artificial intelligence (AI)-driven web scraping market are Tungsten Automation, Hangzhou Duosuan Technology Co. Ltd., Oxylabs UAB, Bright Data Ltd., Zyte Ltd., Grepsr Pvt. Ltd., Apify Technologies s.r.o., Octopus Data Inc., Octoparse Co. Ltd., SerpApi LLC, ParseHub Inc., Diffbot Technologies Corp., Browse AI Inc., The Phantombuster Company, Scraping Robotics Inc., Scrapfly, DataHen Canada Inc., Datahut, ZenRows Inc., Smartproxy LLC
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Artificial Intelligence (AI)-Driven Web Scraping Market Regional Distribution: Which Areas Power Market Growth?
North America was the largest region in the artificial intelligence (AI)-driven web scraping market in 2025. Asia-Pacific is expected to be the fastest-growing region in the forecast period. The regions covered in the artificial intelligence (AI)-driven web scraping market report are Asia-Pacific, South East Asia, Western Europe, Eastern Europe, North America, South America, Middle East, Africa.
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Wasay has over a decade of experience in market research, data modelling, and analytics, with prior experience at GlobalData and Decision Tree Consulting Services. At The Business Research Company , he leads research operations across syndicated studies, customized consulting engagements, and the Global Market Model platform. His professional experience includes supporting organizations such as Boston Consulting Group, KPMG, and Ernst & Young. Wasay holds a degree in Electronics and Communications Engineering, postgraduate management qualifications from International Management Institute Belgium and Indian School of Business and Entrepreneurship, and completed the Integrated Program in Business Analytics from Indian Institute of Management Indore.
