For decades, web scraping projects followed a predictable, costly pattern.
Each new website added to a data stream required custom coding, extensive manual setup, and high upfront costs. Businesses often spent upwards of $2,000 per website, quickly accumulating costs in the hundreds of thousands for large-scale projects.
These inefficiencies often left companies grappling with:
Limited scalability, as high costs restricted the number of websites they could source from.
Speed bottlenecks, slowing down access to market-critical insights.
Technical complexity, requiring specialized teams to manage custom setups and frequent maintenance.
Even the “best” traditional options—pre-scraped data vendors, custom feed services, or in-house solutions—come with significant trade-offs. Pre-scraped data lacks accuracy and flexibility, custom feed services are cost-prohibitive, and in-house solutions demand constant maintenance.
But thanks to advancements in AI and automation, those trade-offs are no longer necessary.