Quick answer: Web crawling is discovering pages: a crawler starts from known URLs, follows links, and builds a map of what exists. Web scraping is extracting data: a scraper takes specific pages and pulls structured information out of them. Crawling answers "what pages are there?"; scraping answers "what does this page say?" Most real-world projects do both, crawling to find the pages and scraping to harvest them.
The two words get used interchangeably, and for casual conversation that's fine. But when you're designing a data project, the distinction determines your architecture, so it's worth being precise.
The Cleanest Way to Remember It
Think of a library. Crawling is walking the stacks and writing down every book's location. You don't read the books; you build the index. Google's crawler does exactly this at web scale, which is why the output of crawling is traditionally an index or a URL list.
Scraping is pulling specific books off the shelf and copying out the facts you need. The output of scraping is data: prices, articles, contact details, structured records.
| Crawling | Scraping | |
|---|---|---|
| Goal | Discover pages | Extract data from pages |
| Input | Seed URLs | Target URLs |
| Output | URL lists, page maps, indexes | Structured records (JSON, CSV) |
| Follows links? | Yes, that's the whole point | Only if told to |
| Classic user | Search engines | Price monitors, researchers, AI pipelines |
Why the Confusion Exists
Because almost every practical project interleaves them. Consider monitoring competitor prices across an e-commerce site:
- Crawl: start at the category pages, follow pagination, collect every product URL. (This specific pattern is common enough to have its own name; we covered it in what is list crawling.)
- Scrape: visit each product URL and extract name, price, and stock status.
One project, both activities, and the tooling reflects it: frameworks like Scrapy and Crawlee handle link-following and extraction in the same codebase. So when someone says "I built a scraper," there's usually a small crawler inside it, and nobody's wrong.
The distinction earns its keep in two places. First, scoping: "crawl the whole site" and "scrape these 200 pages" differ by orders of magnitude in cost and time, and mixing up the words in a project plan causes real misestimates. Second, politeness and legality: crawling touches many pages you don't need, so it carries more obligation to respect robots.txt, throttle aggressively, and avoid load, while scraping a known page list is narrow and predictable. The legal analysis differs too; our guide to whether web scraping is legal covers where the lines are.
Which One Do You Need?
Ask one question: do you already know the URLs?
You know the URLs. You need scraping only. Feed the list to an extractor and you're done. This is the common case for monitoring specific pages, enriching a URL list you already have, or feeding known sources to an LLM.
You don't know the URLs. You need crawling first: from a sitemap, from category pages, or by link-following. Then scraping takes over.
You need "everything about topic X across the web." That's a third thing: search. Instead of crawling the entire web yourself (don't), use a search engine's index to find the pages, then scrape the results. With a search API this is two calls:
import requests
# 1. Search instead of crawling the web yourself
results = requests.post(
"https://api.link.sc/v1/search",
headers={"x-api-key": "YOUR_API_KEY"},
json={"q": "industrial heat pump manufacturers europe"},
).json()["organic_results"]
# 2. Scrape each result as clean Markdown
for r in results[:10]:
page = requests.post(
"https://api.link.sc/v1/fetch",
headers={"x-api-key": "YOUR_API_KEY"},
json={"url": r["url"], "format": "markdown"},
).json()["content"]
That search-then-scrape pattern replaces a crawler entirely for discovery problems, and it's how most AI research agents work under the hood. link.sc exposes both halves through one API precisely because the two activities show up together so often.
The Bottom Line
Crawling finds pages, scraping extracts data, and real projects chain them: discover, then harvest. Use the words precisely when scoping work, know which half your problem actually is, and remember that for topic-discovery problems, a search index you didn't have to build is usually better than a crawler you did.
Need discovery and extraction in one API? Create a free link.sc account: search and fetch, 500 requests a month free.