Web Scraping & Data Extraction Service

Web scraping services extract data from websites for businesses needing competitive intelligence, market research, lead generation, price monitoring, or data analysis.

You build scrapers collecting specific data types—product listings and prices, business information, real estate data, job postings, news articles, or social media data.

Success requires programming skills (Python, Node.js), understanding scraping techniques and anti-bot measures, data processing and cleaning, and sales to businesses needing data.

Pricing varies widely—$500-5,000 per custom scraper, $100-1,000 monthly for ongoing data feeds, or per-record pricing for large datasets.

Projects involve understanding client data needs, developing scraper handling pagination and dynamic content, dealing with anti-scraping measures (rate limiting, CAPTCHAs), cleaning and formatting data, and delivering via API, database, or files.

Startup costs include development tools, proxy services for large-scale scraping, cloud infrastructure for running scrapers, data storage, and business formation totaling $2,000-10,000.

Building client base involves cold outreach to businesses needing data, content marketing about web scraping use cases, partnerships with data analysis consultants, developer communities, and potentially productized scraping for specific popular sites.

Revenue comes from custom scraper development, monthly subscriptions for ongoing data, per-record pricing for large datasets, or consulting and training on scraping techniques.

Operating costs include proxy and CAPTCHA solving services, cloud infrastructure for running scrapers, data storage and bandwidth, maintenance as websites change, and potentially legal review.

Challenges include websites changing structure breaking scrapers, anti-scraping measures becoming more sophisticated, legal gray areas (terms of service violations), maintaining scrapers requires ongoing work, and competition from scraping tools and offshore developers.

Success requires handling anti-scraping measures effectively, delivering clean, structured data not just raw scrapes, building scrapers that handle website changes gracefully, focusing on high-value data businesses will pay for, and understanding legal boundaries (avoid obviously problematic scraping).

Many successful scraping businesses specialize in specific industries or data types.