Web Scraping
The automated extraction of data from websites by sending requests and parsing the returned HTML or API responses.
Web scraping is the automated collection of data from websites. A scraper sends HTTP requests to target pages, receives the HTML (or JSON from an API), and parses out the specific data it needs - prices, product details, reviews, listings, search results. It automates what a person could do by hand, at a scale no person could match.
The workflow has three stages: fetch (request the page), parse (extract structured data from the response), and store (save it to a database or file). Tools range from simple HTTP libraries like Python's requests to full headless browsers like Playwright that render JavaScript-heavy pages before scraping.
Proxies are essential to scraping at scale. Sending thousands of requests from one IP gets that IP rate-limited or banned quickly. Rotating requests across a large residential proxy pool spreads them so no single IP trips a limit, and geo-targeted proxies let you see the localized content real users in each country receive.
Common applications include price monitoring, market research, SEO rank tracking, lead generation, ad verification, and training data collection. Scraping publicly available data is widely practiced, but legality depends on jurisdiction, the site's terms, and what data is collected - see whether web scraping is legal for the nuances.
Automating what a browser does
Web scraping means retrieving pages programmatically and extracting structured data from them. The retrieval half is ordinary HTTP. The extraction half is parsing, and the difficulty of both varies enormously with the target.
Sites that render on the server give you the data in the HTML. Sites that render in the browser require you either to execute JavaScript, which means a headless browser and much more traffic, or to find the API the page itself calls, which is usually faster and cheaper.
The reason proxies enter the picture is volume. A single address making thousands of requests trips rate limits and reputation checks. Spreading the same volume across many addresses is what makes a scraper sustainable rather than a burst that gets blocked.
Choosing the address type by what the target checks
Cost differences between approaches are large, so this decision is worth making deliberately:
Routing by target
Public pages, no filtering -> datacenter, from $1.15/mo, unmetered
Hosting ranges blocked -> residential, $1.00 per GB
Login required -> ISP or mobile, one address per account
1M pages at 80 KB gzipped = 76 GB = $76 on residential
or 20 datacenter addresses at $27- Look for the underlying API before rendering pages. A JSON endpoint is often a tenth of the traffic and none of the parsing pain.
- Request gzip and block images. On metered products this is the difference between a sensible bill and a silly one.
- Spread across addresses rather than raising threads on one. Rate limits count per address.
- Record which address served which response. Debugging without that is guesswork.
Scraping misconceptions
A proxy does not defeat fingerprinting
The address is one signal. A Python TLS handshake announces itself regardless of the exit.
Residential is not the default answer
Most pages in a typical crawl never check. Paying per gigabyte for those is the usual waste.
More threads is not more data
Past the target's tolerance, extra concurrency produces 429s rather than pages.
A 200 response is not proof of good data
Soft blocks return reduced content with a success code.
See this in practice
Ready to use web scraping?
SotaProxy gives you access to rotating residential, mobile, datacenter, and ISP proxies. No minimum commitment.
Get started