PH Deck

Current users rely on existing proxy services for web scraping, which can be expensive, slow, or unreliable. Expensive, slow, or unreliable proxy services limit the efficiency and cost-effectiveness of large-scale data collection.

Users facing scraping proxy use cases struggle with finding cost-effective and reliable residential proxy service providers

Drawbacks: High costs associated with residential proxies, limited number of available proxies leading to delays and restrictions in scraping tasks

Users need efficient methods to extract data from websites, but they face challenges with traditional methods.

The current situation for users involves manually using disparate web scraping tools, which can be inefficient and time-consuming. The drawbacks of this old situation include the need to identify, learn, and manage multiple tools, and potential lack of expertise in finding the right tools.

In the current situation, users involved in web scraping, AI model training, and data extraction typically rely on finding and maintaining proxy servers manually. The drawbacks of this old situation include the need to manually manage proxies, ensure reliability, and deal with the complexity of scaling their operations.

Users rely on lower-quality residential proxy IPs that are less concealed, less safe, and slower, leading to higher detection risks and inefficiencies in tasks like web scraping or geo-restricted access.

Users need to manually code or use complex tools for web scraping, facing coding requirements and website blocks that slow data extraction.

Users attempting to scrape data from the web often encounter challenges with proxies, CAPTCHAs, headers, and utilizing headless browsers, which makes data extraction cumbersome and sometimes impossible.

Users need to extract data from websites but face challenges with manually configuring selectors for each website and dealing with CAPTCHAs, proxies, and JavaScript rendering.