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Artificial Intelligence (AI)-Driven Web Scraping Market Forecast: What Market Value Is Expected By 2030?
The market size for artificial intelligence (AI)-driven web scraping has experienced substantial growth in recent years. This market is projected to expand from $8.24 billion in 2025 to $10.20 billion in 2026, demonstrating a compound annual growth rate (CAGR) of 23.8%. Historically, this expansion has been propelled by increasing digital data volumes, rising e-commerce activity, a growing demand for competitive intelligence, the proliferation of cloud-based scraping solutions, and its broader integration across diverse sectors.
The artificial intelligence (AI)-driven web scraping market size is anticipated to expand significantly in the near future. It is projected to grow to $23.70 billion by 2030, demonstrating a compound annual growth rate (CAGR) of 23.5%. The projected expansion can be attributed to an increasing need for real-time business intelligence, greater adoption of artificial intelligence and machine learning technologies, a rising requirement for dynamic pricing and market monitoring, the development of no-code/low-code scraping platforms, and expanding demand from small and medium enterprises. Major developments foreseen in this period include technological advancements in artificial intelligence (AI) and machine learning, innovations in automated data extraction tools, the evolution of cloud-native and scalable scraping platforms, research and development efforts in anti-bot evasion and proxy networks, and growing integration with real-time analytics and decision-making systems.
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Artificial Intelligence (AI)-Driven Web Scraping Market Growth Factors Supporting Long-Term Expansion
The increasing use of AI-powered decision-making tools is anticipated to fuel the expansion of the artificial intelligence (AI)-driven web scraping market in the future. These AI-powered tools are software platforms leveraging artificial intelligence, including machine learning and predictive analytics, to automate and improve business insights and decision-making processes. Their growing adoption stems from the escalating digitalization across enterprises and the demand for strategic decisions backed by data. Artificial intelligence (AI)-driven web scraping improves AI-powered decision-making tools through the automated collection and structuring of vast amounts of real-time data from various online sources. This process boosts analytical precision and promptness by supplying machine learning models with current insights, thereby facilitating quicker, data-informed strategic choices. For example, data released in January 2025 by Eurostat, a Luxembourg-based statistical office of the European Union, indicated that in 2024, 13.5% of enterprises employing 10 or more individuals utilized AI technologies. This represented an increase from 8.0% in 2023, showing a 5.5 percentage-point rise. Consequently, the increased uptake of AI-powered decision-making tools is a key factor propelling the expansion of the artificial intelligence (AI)-driven web scraping market.
#Artificial Intelligence (AI)-Driven Web Scraping Market Segment Landscape And Growth Potential
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 Industry Trends Shaping Future Revenue Growth
Leading firms within the artificial intelligence (AI)-driven web scraping market are concentrating on creating sophisticated platforms, like AI-enabled low-code tools, aimed at improving efficiency, increasing accessibility, and lowering technical hurdles and the time required for development. These AI-powered low-code tools are defined as platforms leveraging artificial intelligence to automate the process of extracting and organizing web data using natural language prompts, thus removing the necessity for manual coding. As an example, in July 2025, Oxylabs.io, a web-intelligence and proxy services company headquartered in Lithuania, unveiled AI Studio. This new suite of AI-driven tools is designed to facilitate the discovery, collection, and preparation of web data via natural language instructions. Its features encompass AI-Crawler and AI-Scraper capabilities, allowing for smooth data extraction from either numerous or individual pages without requiring human involvement. Additionally, it integrates instruments like Browser Agent for dynamic interactions and AI-Search for conducting web queries, thereby broadening the platform’s usefulness and decreasing operational complexities for developers, product teams, and data analysts.
Artificial Intelligence (AI)-Driven Web Scraping Market Competitive Landscape: Who Are The Leading Companies?
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 Geographic Landscape: Which Region Dominates Industry 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.