Web crawling vs. web scraping: What's the difference?

Say you want to collect product prices from an e-commerce website. If you already have the product URLs, you can write a scraper that visits each page and extracts the price.

But what if you only have the website's homepage and need to find every product first?

Your scraper still needs the product pages, but something has to find them first.

That's where web crawling comes in.

The difference between web crawling and web scraping is what each process is trying to accomplish. Crawling discovers and navigates web pages, while scraping extracts information from those pages.

They often work together, which is why people use the terms interchangeably. But understanding the distinction can save you from building a crawler when all you need is a scraper, or writing a scraper that can't find half the pages you're trying to collect.

Web crawling finds the pages you need

Web crawling is the process of systematically discovering and visiting web pages, usually by following links from one page to another.

A crawler starts with one or more URLs, often called seed URLs, and explores the links it finds.

Imagine a website with this structure:

example.com
├── products/
│   ├── laptops/
│   │   ├── laptop-a/
│   │   └── laptop-b/
│   └── phones/
│       ├── phone-a/
│       └── phone-b/
└── blog/
    └── latest-news/

If you start crawling at example.com/products/, the crawler can follow links to the laptop and phone categories and discover the individual product pages.

The important part is that you didn't need to know those product URLs beforehand.

A typical crawler works like this:

1. Start with a known URL and fetch the page.
2. Find links to other pages in its content.
3. Add relevant links to a queue of URLs to visit.
4. Visit those pages, discover more links, and repeat.
5. Stop when it reaches the configured scope, page limit, or another stopping condition.

In practice, a crawler also needs to handle duplicate URLs, pagination, redirects, and pages that shouldn't be visited.

Not every crawler visits an entire website. You can limit one to a particular section, follow only certain URL patterns, or stop after discovering a specific number of pages.

This is essentially how search engines discover content.

According to Google Search Central, Google discovers URLs through previously visited pages, links, and submitted sitemaps. Its crawler then visits accessible pages to retrieve their content.

But there's an important distinction here: crawling is not the same thing as indexing.

Crawling discovers and fetches pages. Indexing is the separate process of analyzing content and storing information in a searchable index. A page can be crawled without being indexed.

And crawling isn't exclusively for search engines. You might crawl a documentation website to find reference pages, an online store to discover products, or a blog to collect its articles.

Web scraping extracts information from pages

Web scraping is the process of automatically collecting content or specific data from web pages and turning it into something you can use.

Unlike crawling, scraping doesn't necessarily involve discovering new URLs.

Suppose you've already found this product page:

example.com/products/laptop-a/

The page contains a product name, price, description, availability, and customer reviews.

Your scraper might extract:

{
  "name": "Laptop A",
  "price": 899,
  "currency": "USD",
  "in_stock": true
}

That's web scraping. You've taken information displayed on a webpage and converted it into structured data your application can work with.

A scraper typically fetches a page, reads its HTML or rendered content, identifies the information you want, and returns it in a format such as JSON, CSV, or Markdown.

For static websites, you can do this with Python's requests and Beautiful Soup. You send an HTTP request, parse the HTML, and select the elements containing the information.

Things become more complicated when the website loads content through JavaScript, requires interaction, or uses bot protection. You may need browser automation tools such as Playwright or a managed scraping API.

But the objective stays the same: get the information you need from the page.

And you don't necessarily need to extract specific fields. Scraping can also mean retrieving the article text from a blog post, converting a documentation page to Markdown, or collecting the HTML for further processing.

The important distinction is that you're interested in the page's content, rather than discovering where other pages are located.

Web crawling vs. web scraping: The main differences

The easiest way to distinguish the two is to look at their objectives and the information they return.

The distinction isn't absolute.

A crawler must retrieve enough information from a page to discover its links, and many modern crawling tools also extract the content of every page they visit.

Similarly, a web scraper can follow pagination links or navigate between pages to collect more records.

In fact, Scrapy, a popular Python framework, supports both operations in the same spider. You can define how it follows links and how it extracts items from the pages it visits.

So it's more accurate to think of crawling and scraping as two parts of a web data workflow rather than two competing technologies.

A crawler can use scraping techniques, and a scraper can contain crawling logic.

The distinction is what you're trying to accomplish.

Let's see the difference using a real website

Consider Books to Scrape, a practice ecommerce website built for testing scraping tools.

We're going to try two scenarios.

First, we already know the URL of a book and want its information. Then we'll start with a book category and discover individual books within it.

We'll use Python and Spidra for both examples, although you can build the same workflows yourself with libraries such as Beautiful Soup, Requests, and Scrapy.

Install the Python SDK:

pip install spidra

You'll also need a Spidra API key available as the SPIDRA_API_KEY environment variable.

First, scrape a book page we already know

Suppose we want the title and price of A Light in the Attic.

We already have the product URL, so there's no reason to crawl the website.

With Spidra's scraping API, we can request those fields directly:

# books.py
import os
from spidra import Spidra

client = Spidra(api_key=os.environ["SPIDRA_API_KEY"])

book = client.scrape(
    "https://books.toscrape.com/catalogue/a-light-in-the-attic_1000/index.html",
    prompt="Return {title, price}",
)

print(book.content)

The recorded response was:

{
    "title": "A Light in the Attic",
    "price": "£51.77"
}

We supplied a known URL, requested two fields, and received the information.

There was no need to discover other pages, follow category links, or explore the website.

That's a scraping problem.

Now, crawl a category to discover book pages

Let's change the request.

Suppose we want to collect books from the Mystery category, but we don't have the individual product URLs.

We only know the category page:

https://books.toscrape.com/catalogue/category/books/mystery_3/index.html

We could manually open the page, copy each product URL, and pass those URLs to a scraper.

But that means doing the discovery ourselves.

Instead, we'll use Spidra's Crawl feature. It accepts a starting URL and an instruction describing the type of pages we're looking for.

Add this to the previous Python script:

from spidra import CrawlParams

job = client.crawl(CrawlParams(
    base_url="https://books.toscrape.com/catalogue/category/books/mystery_3/index.html",
    crawl_instruction="Individual book pages",
    max_pages=3,
))

print(job.total_pages)
for page in job.result:
    print(page.url)

The recorded crawl returned three individual book pages:

• In a Dark, Dark Wood
• Sharp Objects
• The Past Never Ends

Each result contained the book's URL, alongside its fetched page content.

Something interesting happened during this crawl.

Spidra visited the Mystery category page to discover the book links, but the category page itself wasn't included in the results.

It was a navigation page. Its purpose was to help the crawler find the actual pages we requested.

The crawl report showed one navigation page and 51 links skipped because they didn't match the requested page type.

This is where the difference becomes practical.

When we scraped the first book, we already had its URL and only wanted its information.

When we crawled the Mystery category, we knew where to start but needed to discover the individual book pages.

Spidra can also extract structured fields while crawling. Instead of only collecting URLs or Markdown, you can pass an extraction instruction or JSON schema, and the crawler will apply it to each selected page.

That means discovery and extraction can happen in the same workflow.

The Crawl documentation explains how to select pages, limit a crawl to specific paths, and return structured data.

You don't always need to crawl a website to scrape multiple pages

Here's where many scraping projects become unnecessarily complicated.

Imagine you've been given a CSV file containing 500 product URLs.

Do you need a crawler?

No. You already know exactly where the pages are.

You need a scraper that can process multiple URLs, preferably in batches, and return a separate result for each page.

Now imagine you're given a company's homepage and asked to collect information from every product detail page.

That's different. You need some way to find those pages first.

You could write a crawler, read the website's sitemap, or use a URL discovery tool.

The right choice depends on what you already know.

If you have one URL, scrape it directly.

If you have a list of known URLs, use batch scraping.

If you know the website but not the target pages, use crawling to discover and optionally extract them.

If you only want to inspect the site's URLs before deciding what to collect, mapping may be enough.

That last option is particularly useful when you don't want to extract hundreds of pages just to find the ten that matter.

Website mapping can save you an unnecessary crawl

Suppose you're building a dataset from a documentation website, but only care about authentication-related pages.

You don't need the content of every page on the site. You first need a list of relevant URLs.

Spidra has a Map feature for this.

It discovers URLs from the site's sitemap and links, and can filter the results by a search term without returning each page's extracted content.

In one recorded test on FastAPI's documentation website, Map found 36 dependency-related URLs from 619 URLs scanned.

The discovery request cost one credit.

There was a catch, though. The results included German, Spanish, and French versions of the documentation.

If we had scraped every returned URL without filtering, we would have collected multiple language versions of similar content.

Instead, we could pick the URLs we needed and batch scrape only those.

This is a useful distinction because crawling doesn't always have to be your first step when you don't know the URLs.

Sometimes you need a URL inventory. Sometimes you need a crawler that follows links until it finds certain pages. And sometimes you need both discovery and extraction.

Knowing which problem you're solving helps you avoid processing pages that won't contribute anything to the final dataset.

Crawling and scraping share some of the same problems

Despite their different purposes, crawlers and scrapers often encounter similar challenges.

For example, a website might render its content through JavaScript. A crawler that only reads the initial HTML could miss links that appear after the page loads. A scraper using the same approach could miss the product information it was supposed to extract.

Both may need browser rendering to access the complete page.

Pagination presents another challenge.

A product category might show only 20 items initially, with the remaining products spread across additional pages. A crawler needs to discover and follow those pagination links, while a scraper working from the first page alone would only extract the items visible there.

Then there are duplicate URLs, redirects, unavailable pages, and bot protection.

Neither crawling nor scraping guarantees complete coverage.

A crawler cannot reliably discover pages that have no accessible links or sitemap entries unless you provide another way to reach them. Similarly, a scraper cannot extract information from a page it cannot access.

And even when a page loads successfully, extraction can be imperfect. Fields might be missing, the layout might change, or a structured extractor could interpret the content incorrectly.

This is why a successful request is not always the same as a correct result.

For production data collection, you still need validation, sensible request limits, and handling for pages that fail or return incomplete data.

You should also respect the website's access policies.

The robots.txt standard provides rules that automated crawlers can follow, but it isn't an authentication or authorization system. It also doesn't replace a website's terms of service or applicable laws.

Before collecting data, review the site's policies, avoid excessive requests, and make sure you're permitted to use the information you're collecting. Our guide on whether web scraping is legal covers the broader considerations.

Which should you use for your project?

The choice becomes easier when you look at your final goal.

If you're building a price comparison application and already have the product URLs, focus on scraping. You don't need discovery, and adding a crawler would introduce work you don't need.

If you're building a search engine, content inventory, or website audit tool, crawling is central because you need to discover pages and understand what's available.

If you're building a RAG application from a documentation website, you'll probably need both. The crawler finds the relevant documentation pages, and extraction turns their content into a format such as Markdown that you can clean, split into chunks, and store for retrieval.

And if you have a large website but only need a handful of pages, consider mapping the website first and scraping a selected list.

The goal isn't to use as many tools as possible. It's to collect the information you need without unnecessary requests or complexity.