Web scraping: A comprehensive guide

There are two ways to catch a price drop on that obnoxiously priced all-terrain dog stroller you’ve been eyeing (but won’t admit out loud to the general public—which, smart). You could check the product page every morning and hope today’s the day for good boy Professor Waffles. Or you could let a price tracker like camelcamelcamel watch the listing for you and send an alert the moment it dips below a set price. That second option is web scraping at work: an automated tool visits the page, reads it, and pulls out the one number you actually care about.

Deal hunting is just the entry point, though. Web scraping powers job boards and price comparison sites, and it’s how plenty of teams keep tabs on competitors without refreshing pricing pages all day. It’s also become a standard way to feed AI tools fresh data from the web. 

Here’s everything you need to know about web scraping.

Table of contents:

What is web scraping?

Web scraping is the use of automated programs to visit websites, extract specific data, and save it in a structured format you can actually use, like a spreadsheet or database. This process is also called web data extraction.

It’s not a new technology, either. The World Wide Web Wanderer, which lots of folks consider the first web scraper, came about in 1993, just a few years after the web itself. But modern scrapers are a lot more sophisticated: they can handle everything from monitoring competitor prices to gathering lead data for your CRM.

Web scraping vs. web crawling

Web scraping and web crawling are related, but they’re not the same thing. Web crawlers (like the bots search engines send out) find pages and catalog what’s on them, while web scrapers extract specific data from the pages themselves. Plenty of tools today do both. 

How does web scraping work?

Web scraping mimics what you do when browsing the web, just at machine speed and scale. Here’s the basic process (though what’s happening under the hood is a lot more complicated):

1. The scraper visits a web page.

2. It reads the HTML code (the underlying structure).

3. It finds the specific data you’re looking for—for example, product prices, article titles, or company sizes.

4. It extracts that data into a usable format for you.

Most modern tools go beyond this, too. Instead of relying on basic HTML, for example, they execute JavaScript to load dynamic content, take screenshots, fill out forms, click buttons, and even solve simple CAPTCHAs. 

AI has also changed the extraction step itself. Traditional scrapers need you to tell them exactly where the data lives on a page, which means they break whenever a site redesigns its layout. AI-powered scrapers read a page more like a human does: you describe what you want in plain language (“get me the product name and price”), and the model figures out where that data is, even if the page structure changes underneath it.

But here’s the challenge: not all websites roll out the welcome mat for scrapers. Many deploy anti-bot measures like rate limiting (controlling the rate of requests from external parties), IP blocking, and those annoying (but effective) select-every-box-with-a-car-in-it puzzles. 

Types of web scraping

Most web scraping falls into a handful of categories based on how the scraper gets to the data. Here are the most common ones. 

• Static scraping (HTML parsing): The scraper downloads a page’s raw HTML and parses it for the data you want. It’s the fastest and simplest approach, and it works well when the content lives right in the source code, like news articles or public data tables. The tradeoff is that it can’t see content that loads dynamically. (Many sites use JavaScript to fill in the actual content—like prices or reviews—after the page loads, so a static scraper sees only the emptier version that arrives first.)

• Dynamic scraping (browser automation): The scraper controls a real browser, so it can render JavaScript, click buttons, scroll, and fill out forms just like a person would. It’s slower and more resource-intensive than static scraping, but it can handle the modern, app-like websites that static scrapers can’t read.

• No-code scraping: Point-and-click tools let you “train” a scraper by selecting the data you want directly on the page, and the tool handles the technical work behind the scenes. It’s the most accessible option if you don’t code.

• AI-powered scraping: An AI model reads the page and extracts whatever you describe in plain language, so the scraper isn’t tied to the page’s structure and keeps working when a site changes its layout. It’s the newest category, and it’s increasingly built into AI assistants and agents.

• Screen scraping: The scraper works from what’s visually rendered on screen (often using optical character recognition) rather than the underlying code. It’s mostly useful for legacy systems and PDFs, where there’s no clean HTML to parse.

One approach that often gets lumped in with web scraping but isn’t quite the same: pulling data through a site’s official API. If a site offers an API, that’s almost always the better route, since you get structured data with permission instead of playing defense against anti-bot measures. (Jump ahead to learn the difference between web scraping vs. APIs.)

Web scraping examples

Web scraping examples show up anywhere someone needs data from a website faster than they could ever copy and paste it. Most use cases fall into a few buckets.

• Price and product monitoring: Retailers and eCommerce teams might scrape competitor sites to track prices and product launches, and then adjust their own strategy accordingly. (This is also what price trackers like camelcamelcamel are doing on your behalf.)

• Lead generation and enrichment: Scraped data can fill in the details on your leads so you have more information to work with in your outreach. Tools like Apollo and Clay gather that data for you. From there, Zapier can put it to work across an AI-powered workflow: AI by Zapier scores and summarizes each lead using that scraped context, and adds it to your CRM, ready for outreach before anyone on your team has even looked at it.

• Feeding AI tools: AI assistants and agents are only as useful as the data they can access, so scraping has become a standard way to hand them fresh web data—whether that’s market research for a report or live details a model’s training data doesn’t include.

• Powering entire products: There are thousands of job boards built on top of web scraping. LinkedIn itself uses web scraping to help companies publish jobs from their career page to LinkedIn automatically. (It’s worth emphasizing that scraping content from LinkedIn itself is against their terms of service.) Price comparison sites and real estate portals are built the same way, too. 

• Content and SEO research: You can scrape your own website (or the competition’s) to audit pages and gather details like titles, metadata, and publish dates. That’s the premise of a tool like Screaming Frog.

The best web scraping tools

Zapier

Best for: Building web scraping into automated workflows

Pros:

• Connects with 9,000+ apps, so scraped data lands directly in the apps you already use

• Works on JavaScript-heavy pages and PDFs

Cons:

Scraping a website is only half the job—the data still needs to end up somewhere useful, and Zapier’s features are built to work together on that. The typical pattern: use Web Search by Zapier to find relevant pages and Web Reader by Zapier to fetch and parse each one, returning clean markdown or HTML, even from JavaScript-heavy pages or PDFs.

From there, AI by Zapier can extract, analyze, or summarize exactly what you need before routing the result across your entire tech stack—all in one AI-orchestrated workflow.  

You don’t have to build any of this by hand, either. Simply describe what you’re trying to do, and Zapier’s built-in AI assistant will configure the workflow for you.

Browse AI

Best for: No-code scraping and monitoring 

Pros:

• Handles pagination, infinite scroll, and dynamic content out of the box

• Integrates natively with other apps, including Zapier

Cons:

• Usage-based credit pricing can make costs harder to predict 

• Best suited to structured, repeatable page layouts (one-off or highly irregular sites may need more manual setup)

Browse AI turns any webpage into a live data feed. Instead of writing selectors or scripts, you visit the page you want to scrape, train a “robot” by clicking on the fields you care about, and Browse AI handles the rest, including future runs on similar pages.

It’s what makes Browse AI a strong web scraping tool for ongoing monitoring, where you want fresh data on a schedule rather than a single extraction. Pair it with Zapier, and you can push newly scraped rows straight into a spreadsheet, CRM, or Slack channel the moment Browse AI detects a change. Learn more about how to automate web scraping with Browse AI and Zapier.

Apify

Best for: Developers who need scale, customization, or a pre-built scraper for a specific site

Pros:

• A large marketplace of ready-made “Actors” covers everything from Google Maps and Amazon to social platforms, so you often don’t have to build a scraper from scratch

• Full support for custom code (JavaScript/Python), plus managed proxies and headless browser infrastructure

Cons:

• The credit system can be difficult to estimate ahead of time, especially for JavaScript-heavy scrapes

• Steeper learning curve (required reading Actor documentation or writing code)

Apify sits between a no-code scraper and a full engineering platform. Instead of hand-rolling a scraper for every new target, you can browse its Actor Store for one that already handles your use case, or build a custom Actor with Apify’s own Crawlee framework if you need something bespoke. Actors run on Apify’s cloud, so you’re not managing proxy pools or headless browsers yourself.

Where Apify shines is scale and specificity: pulling structured data from sites like TikTok, running thousands of pages a day, or feeding a data pipeline that needs consistent, scheduled output. Apify also has its own MCP server, so an AI agent can call its Actors directly from a chat interface. Or you can connect Apify with Zapier so your agentic workflow can actually do something with that scraped data across your entire tech stack. 

Beautiful Soup + Requests

Best for: Developers writing lightweight, custom scrapers for static HTML pages

Pros:

• Free, open source, and one of the most approachable ways to start scraping in Python

• Pairs easily with the Requests library, so you have full control over headers, cookies, and request logic

Cons:

• Neither library can execute JavaScript, so pages that load content dynamically often return empty or incomplete results

• No built-in handling for proxies, retries, rate limiting, or CAPTCHAs—you’re building and maintaining all of that yourself

Unlike the other web scraping tools on this list, Beautiful Soup and Requests aren’t apps you log in to. They’re free building blocks you can use to hand-build your own scraper. Requests tells your scraper to go fetch a webpage, while Beautiful Soup makes sense of that scraped data, extracting specific pieces you asked for, like pricing plans or a list of names. 

The catch is that this combination only captures a page the moment it’s requested. If a site loads some of its content a moment later, Beautiful Soup and Requests won’t see it. For those cases, you’ll need to reach for something like Playwright, which behaves more like an actual browser sitting there, waiting for the whole page to finish loading.

Playwright

Best for: Scraping JavaScript-heavy sites 

Pros:

• Auto-waits for elements to load, which cuts down on the flaky timing issues common in older browser automation tools

• Supports parallel execution across multiple browser contexts, useful for scraping at higher volume

Cons:

Playwright is a browser automation framework originally built for testing, but it’s become a go-to tool for scraping sites that Beautiful Soup and Requests can’t handle—specifically, anything that relies on JavaScript to load its content, like infinite-scroll feeds or single-page applications. Because it controls an actual browser, it can wait for content to render, interact with the page, and capture what a real visitor would see.

That power comes at a cost, though. Browser automation is slower and heavier than static scraping, and it leaves fingerprints that anti-bot systems increasingly know how to detect. For teams scraping at scale or targeting protected sites, Playwright is usually one piece of a larger setup that also includes proxy rotation and bot-detection evasion, which is where dedicated scraping platforms like Apify or Browse AI can pick up the slack.

Note: Playwright isn’t the only option here. Selenium and Puppeteer solve the same problem, but Playwright’s generally considered the fastest and most reliable of the three, with built-in support for multiple browsers.

Is web scraping legal?

Ready for a highly unsatisfying answer? It depends. 

Generally speaking, if you’re using web scraping for things like competitor analysis, price comparison, or data enrichment, it’s considered legal and acceptable under fair use principles. But if web scraping goes against a website’s terms of service (like LinkedIn), it could be considered copyright infringement. 

Then there’s the case of AI crawlers using web data to train large language models (LLMs) without explicit permission. There are legal battles happening over this as we speak. There are emerging solutions, too. For example, publishers are now striking deals with AI crawlers to receive compensation for associated AI activity. Another example: Cloudflare blocks many AI crawlers by default unless a site owner opts in, and its Pay Per Crawl program lets publishers charge AI companies for access.

Web scraping best practices

Web scraping sits in a legal and ethical gray area, so here are a few best practices to help you stay on the right side of the law. 

Check the terms of service first

Before scraping a site, read its terms of service. Many sites explicitly prohibit automated data collection, and scraping in violation of those terms can expose you to breach-of-contract claims even when no copyright’s involved.

Respect robots.txt

Most websites publish a robots.txt file that specifies which pages automated crawlers can and can’t access. Following these files is a widely accepted signal of good-faith scraping. That said, at least one U.S. court has ruled that robots.txt files are requests rather than legally enforceable barriers, so treat it as an ethical baseline, not proof that you’re in the clear.

Stick to publicly accessible data

Scrape only data that’s visible without logging in, paying, or bypassing a security measure. Data sitting behind a login wall, paywall, or CAPTCHA is a strong signal that the site owner doesn’t want it accessed programmatically.

Rate-limit your requests

Space out your requests instead of hammering a server with thousands of hits in a short window. Aggressive scraping can look identical to a denial-of-service attack, get your IP blocked fast, and degrade the site’s performance for everyone else.

Identify your scraper

Use a descriptive user agent and provide contact information (if it’s available). Many legitimate crawlers do this so site owners can reach out instead of just blocking traffic outright.

Avoid scraping personal or sensitive data

Be cautious about collecting personal information since doing so can trigger privacy regulations like GDPR or CCPA, even if the data was publicly visible. If personal data is central to your use case, consult a privacy or legal professional.

Don’t republish scraped content wholesale

Using scraped data for internal analysis (competitor pricing, market research) is different from copying and republishing someone else’s articles, images, or copy on your own site. The latter risks copyright infringement, regardless of how the data was obtained.

Use an official API when one exists

If a site offers an API, use that instead of scraping. APIs are explicitly sanctioned by the site owner and are usually more stable than scraping. 

Connecting to an API usually means writing code to handle authentication or managing API keys, but Zapier lets you securely connect to 9,000+ apps’ APIs while handling the OAuth authentication and credential storage for you. 

Store and handle scraped data responsibly

Keep scraped data secure, retain only what you need, and have a plan for deleting it if a site owner requests removal. This matters even more as AI crawlers and data licensing disputes make headlines, which emphasizes how responsible data policy is good practice regardless of what the law eventually settles on.

Expect the legal landscape to keep shifting

Web scraping law is unsettled and moving quickly, especially where AI training data is involved. What’s considered acceptable today may not be tomorrow, so revisit your scraping practices periodically rather than treating any single approach as permanently safe.

Web scraping: FAQ

Still have questions about web scraping? Here are answers to the ones that come up most.

What is website scraping used for?

Web scraping automatically pulls structured data from websites so you can use it somewhere else—like a CRM or database. Many organizations use it to track competitor pricing, monitor product or job listings, gather sales leads, and round up news or research content. It’s also increasingly used to feed AI tools fresh, structured web data for training or retrieval.

Web crawling vs. web scraping vs. API: what’s the difference?

These three terms get used interchangeably, but they solve different problems:

• Web crawling discovers what pages exist by following links from page to page. It’s how you index (catalog) a website or the web at large.

• Web scraping extracts specific data from a page once you’re on it. 

• An API is a direct, sanctioned channel a company provides for requesting its data in a structured format, so you don’t have to crawl or scrape anything.

Will AI replace web scraping?

No, but it’s changing who (or what) does the scraping. AI’s automating more of the process, not eliminating it. Traditional scraping relies on humans writing rules to visit specific pages and pull specific fields, and that logic breaks every time a site redesigns. AI-powered tools can now interpret a page’s structure and adapt when the layout changes, and in some cases, they can figure out on their own how to find the data you’re after.

That said, scraping at scale still needs real infrastructure, like proxy rotation and rate limiting, that an AI agent isn’t well-suited to manage in a live conversation (yet). And when a site puts up serious defenses like CAPTCHAs or advanced bot detection, AI agents tend to escalate rather than push through, the same way a human would. More realistically, AI keeps taking on the tedious parts of scraping while people design the systems, set the guardrails, and step in when something breaks.

Related reading:

This article was originally published in August 2025 by Maddy Osman. The most recent update was published in July 2026.