What Is a Web Scraper and How Do You Use One to Extract Web Data?
A web scraper is a tool that loads a web page and pulls structured data out of it — prices, reviews, posts, contact details — instead of showing you the page to read. You use one when you need that data in bulk, on a schedule, or fed into another system. The basic workflow is: pick a target, choose a scraper that already handles that site, run it, then export or connect the output. Apify's marketplace, for example, lists 73,229 ready-to-run tools ("Actors"), including site-specific scrapers for TikTok, Google Maps, Instagram, Facebook, and e-commerce sites, plus a general Website Content Crawler for feeding AI models and RAG pipelines.
Web scraper vs. web crawler
The two terms get used interchangeably, but they do different jobs:
| Web scraper | Web crawler | |
|---|---|---|
| Job | Extract specific fields from pages | Discover and follow links across a site |
| Output | Structured records (rows, JSON, CSV) | A list of URLs / crawled pages |
| Typical use | Prices, reviews, profiles, posts | Site mapping, content inventory, feeding an index |
In practice they overlap. Apify's Website Content Crawler, for instance, crawls a site and extracts text content — because to feed an LLM or vector database you need both the discovery step and the extraction step.
The basic scrape workflow
- Pick a target and define the fields you need. "All Google Maps listings for coffee shops in one city, with name, address, phone, and rating" is a usable spec. "Everything about a business" is not.
- Choose a scraper. If a ready-to-run Actor exists for your site, start there — it already handles that site's layout and pagination. If not, look for a general-purpose tool or plan to build your own.
- Provide input. Most scrapers take either specific URLs or a search query. The TikTok Scraper, for example, accepts URLs or search queries to pull profiles, hashtags, posts, shares, followers, and music data.
- Run it. You can run from the UI, via API, or on a schedule.
- Export or connect the output. Download the dataset, or push it into another tool or AI workflow.
Ready-to-run scrapers vs. building your own
Ready-to-run is the right default when a maintained tool already covers your target. The trade-off is less control over exactly which fields you get.
Build your own makes sense when your target is niche, your fields are unusual, or you need logic no existing tool covers. Apify supports building, deploying, and scaling your own Actors for this case.
A quick way to decide:
- Target is a major platform (TikTok, Instagram, Google Maps, Facebook, common retail sites) → try a ready-to-run scraper first.
- Target is a small or internal site, or you need custom parsing → build your own.
- You need text for an LLM/RAG pipeline → use a content crawler rather than a field-by-field scraper.
Ways to run a scraper
- UI — run it manually and watch the results, good for testing and one-off jobs.
- API — trigger runs programmatically and pull results into your own app.
- Scheduling — run on a recurring basis to track changes over time (e.g., monitoring price details across e-commerce sites).
- AI/LLM integration — feed extracted content into models, vector databases, or RAG pipelines. The Website Content Crawler is built for this and integrates with LangChain and LlamaIndex.
Common failure points and how to handle them
- Blocking and anti-bot defenses. Sites may refuse automated requests. Site-specific scrapers usually handle this for you; custom scrapers often need proxies or request throttling.
- Layout changes. When a site redesigns, field mappings break. This is the main reason to prefer a maintained ready-to-run scraper over a hand-built one for major platforms.
- Rate limits. Sending too many requests too fast gets you throttled. Slow down or spread runs out.
- Pagination and dynamic content. Data loaded by JavaScript or spread across many pages needs the scraper to follow pagination and render the page — check that your chosen tool does this before committing.
- Scope creep. Pulling more fields than you need increases breakage risk and cost. Extract only what you'll actually use.
What to check before you commit
- Does a maintained scraper already exist for your target site?
- Does it accept the input you have (URLs vs. search queries)?
- Can you run it the way you need — UI, API, or schedule?
- Does the output format match where the data is going (spreadsheet, database, LLM pipeline)?
- How will you handle the site changing or blocking you?
Answer those five and you can pick a scraper and get a first dataset out without guessing.