Building a Web Scraping App in 2025: The Complete Guide

Many businesses wish they could click a button and grab all the info from a competitor's website or other resources. It'd be handy to get prices, reviews, job listings, or contacts and drop them straight into a spreadsheet for further analysis, right? Well, many companies already do it.

What makes it possible is the use of web scrapers, or personal data assistants that pull data from websites automatically instead of copying and pasting it. They save hours (sometimes weeks) of manual work and become irreplaceable in many tasks, including content creation, data analytics, and beyond.

If you think you and your team will benefit from such a tool, read on. We'll show you how to build web scraper solutions (yup, even if you're new to coding), break down different scraper types, and share how AI helps collect web data.

What Is a Web Scraper, and Why Build Such a Tool?

The thing is that a big chunk of data lives on public websites, and lots of companies are taking advantage of such relevant data from the pool of over 402 million terabytes created every single day. However, most of it is not easily downloadable.

A web scraper is a tool (or a little program) that pulls data from websites. Instead of you sitting there filling out a spreadsheet for hours, a web scraper does it for you fast and without complaining. 

Let's say you're:

• a small business owner tracking product prices on Amazon, Walmart, and Target;
• a realtor watching new listings and price changes on Zillow or Redfin;
• a recruiter scraping job posts from Indeed, LinkedIn, and company career pages;
• or maybe a developer building a web scraping app to track sports scores, news updates, or stock prices in real time.

This is just a brief rundown of the use cases of web scraping, but it makes data collection faster, smarter, and easier than ever. From market research to launching a web scraping mobile app that tracks sneaker prices in real time, the possibilities are endless. No wonder the global web scraping software market is expected to hit $2.2 billion by 2032.

Market Research: Meaning, Types, Methods and Examples

Article by

Maria Arinkina

Why Should You Build a Web Scraper?

Because websites are full of useful things. Prices, reviews, emails, phone numbers, job listings, product descriptions, and more. And when you need that information at scale, doing it manually won't cut it. But you can utilize or build web scraper tools to give you a hand with that.

Some other everyday use cases include:

• Market research: collect reviews, blog comments, or tweets for sentiment analysis.
• Lead generation: scrape business directories for emails and phone numbers.
• Academic or news research: gather articles and stats from hundreds of websites.

Basically, if it's on a website and you need it in a spreadsheet, you can scrape it. Oh, and now there's AI web scraping, too. We'll get into that later, but spoiler: it's making scraping smarter, faster, and more flexible than ever.

Types of Web Scrapers to Build

Not all web scrapers for all tasks are built the same way, though. You've got options depending on your needs, time, and budget. So, before we give you instructions on how to write a web scraper, here are some of their most common types worth noting.

1. Self-built or Pre-built

Just like how anyone can build a website, anyone can build their own web scraper. However, building your own usually means diving into code, using languages like Python or JavaScript, and tools like requests, BeautifulSoup, or Selenium. They give you full control and flexibility, especially for complex tasks like handling dynamic content or simulating user behavior.

The downsides of this approach are that:

• you'll need some programming knowledge;
• if the site structure changes, your scraper might break and need updating.

If that sounds like too much work, don't worry, there are plenty of pre-built web scrapers you can download and run right away. These often come with features like scheduling, exporting to Google Sheets or JSON, and more, so you can focus on the data instead of the code. However, ready-made tools might not cover your specific needs.

If you don't want to make one yourself or can't find an out-of-the-box solution, you can opt to outsource development. There's always an option of hiring a reliable web scraper development team, tap into their expertise, and create and maintain a powerful custom tool.

2. Browser Extension vs Software

In general, web scrapers also come in two forms: browser extensions or full-blown software.

Browser extensions are app-like tools you can add to browsers like Chrome or Firefox.

• Pros: they're super convenient: just install and go. Many of them let you click on the data you want right inside the webpage. 
• Cons: there are limitations as they're tied to the browser, can't really handle advanced tasks like IP rotation, and usually struggle with more complex scraping jobs.

Standalone software, on the other hand, runs directly on your computer. 

• Pros: they offer a lot more flexibility and power, and you can scrape large amounts of data, handle login sessions, use proxies, and more without being boxed in by browser limitations.
• Cons: they take a little more effort to install.

3. Cloud vs Local

It's about where your web scraper actually does its job.

Local scrapers run on your computer, using your system's resources and internet connection. 

• Pros: often faster for small jobs.
• Cons: if your scraper is processing tons of data, it could slow things down or eat up your bandwidth.

Cloud-based scrapers, on the other hand, run on remote servers (usually managed by the tool provider). So, your computer stays free for other tasks while the scraper does its thing in the background. Cloud scrapers include Octoparse, Scrapy Cloud, and similar services.

• Pros: it's ideal if you don't want to deal with code at all, as well as for long and large-scale scrapes (some tools even let you set alerts when the job is done). They often have drag-and-drop interfaces, built-in scheduling, and even auto-adjust features when websites change. Some platforms let you export straight to your favorite tools like Excel, Google Sheets, or APIs.
  
  
• Cons: the tradeoff is that you're working within their framework. Customization is often limited, too, and pricing usually scales with usage. You'll likely need a cloud expert to control the costs to make sure your budget is not wasted, as 70% of cases show.

4. Robotic Process Automation (RPA)

RPA tools are like little digital assistants that mimic exactly what a human would do on a website, like clicking, copying, pasting, and scrolling. Tools like Fortra's Automate are great when you're building a web scraping mobile app , since they handle scraping on dynamic sites, inject JavaScript, read table structures, and integrate with other software. Some even come with UI recorders that let you train the bot by showing it what to do once.

One of the biggest perks of RPA is that you can build web scraping right into larger business processes, like: Scrape this → update CRM → send email notification. It's great for companies looking to automate more than scraping only.

5. User Interface

Not all web scrapers look or feel the same either, and the user interface can make a big difference. Some tools are barebones, running in the command line with text. These are usually faster and more powerful but come with a steeper learning curve.

Others offer a full visual interface, where the website is rendered like a browser, and you can click the elements you want to scrape. Some tools even guide you with built-in tips or tutorials right inside the UI while you're building a web scraper.

6. Web Scraper AI

AI is changing web scraping like everything else. Instead of writing code to find and extract elements like <div class="price">, an AI web scraper figures it out by looking at the page and understanding context. Much like a human would do. You tell it, "Grab all product names and prices," and it goes, "Gotcha."

Whether you're building a web scraping app from scratch or using an existing platform, AI will scale you faster. There are tools out there using web scraping with AI to:

• automatically detect patterns in messy data;
• handle CAPTCHA and anti-bot logic;
• understand different page layouts (even when they change!);
• normalize messy info like addresses, names, and phone numbers.

They are a catch because AI helps with image recognition, natural language understanding, and dynamic content. Some popular tools that use AI for web scraping are:

• Browse AI
• ScraperAPI
• Diffbot
• Apify with LLM integration

How to Build a Web Scraper in 6 Steps (With a Little Help from AI)

Okay, now that we know what types of scrapers exist, let's walk through how to build a web scraper step by step.

Step 1: Pick the Right Toolset

First, choose your programming language and tools. Most people use Python because it's beginner-friendly, and there are tons of libraries like:

• requests – for grabbing the page
• BeautifulSoup – for parsing HTML
• Selenium or Playwright – for interacting with JavaScript-heavy pages

For a more AI-focused approach, check out LLM libraries and tools like:

• LangChain + OpenAI for interpreting content
• Puppeteer with GPT-based tagging
• Scrapy with AI-based cleaning modules

Step 2: Identify the Target Data

Decide exactly what you need. Product titles? Prices? Images? Let's say you're scraping Etsy:

• Product name: div.title
• Price: span.price
• Ratings: div.review-stars

With web scraping AI, you might skip the code and just write: "Grab all product names, prices, and ratings." Some tools like Browse AI or Bardeen do this with natural language.

Step 3: Fetch the Web Page

Use requests .get(URL) or launch a headless browser with Selenium.

Example:

import requests
from bs4 import BeautifulSoup
url = "https://example.com/products"
page = requests.get(url)
soup = BeautifulSoup(page.content, "html.parser")

Step 4: Extract the Data

Now, dig into the HTML and grab what you need.

titles = soup.find_all("div", class_="title")
for title in titles:
   print(title.text)

Or if you're using an AI web scraper, you might upload a screenshot and ask it to pull out product details with no code.

Step 5: Store the Data

You can save it to:

• A CSV file
• A Google Sheet
• A database (like MongoDB or MySQL)

Example:

import csv
with open("products.csv", "w", newline="") as file:
   writer = csv.writer(file)
   writer.writerow(["Name", "Price", "Rating"])
   writer.writerow(["Cute Mug", "$9.99", "4.5"])

Step 6: Scale It

Want to scrape hundreds of pages? Add a loop, delay (so you don't get blocked), and maybe even proxies or a rotating IP setup. And yes, add AI web scraping to make it smarter. Let the AI detect layout changes or suggest fixes when something breaks.

Here's a quick table to recap:

Browsers or Bots, What Will You Build?

So, what did we learn?

Web scraping is a super useful skill, and with tools and tech improving every day, building a web scraper has never been easier. Whether you're a beginner or building a serious web scraping app, there's a path for you.

And now, having web scraping with AI, you don't always need to know code. You need to know what you want, and the tools will handle the rest.

So if you're wondering how to build a webscraper that can adapt and scale, start with the basics, then add AI as you grow. Need help deciding or developing a solution? Don't be shy to reach out to us, Upsilon's experts will be glad to provide a consultation or data scraping services to get things done!