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What actually breaks when you build an LLM scraper in-house?
Usually not the first scraper. What breaks is everything around it: sites block your servers, layouts change silently, boilerplate pollutes the text, duplicates pile up, and model costs for extraction grow with every page. Teams that build in-house end up running proxies, browsers, monitoring and cleaning, which is why many buy that layer and keep only the AI logic.
Updated 10 November 2026 by the ScrapeWise team.
Quick picks
If scraping is your product
Build
You need full control.
If you need data, not infrastructure
Buy the collection layer
Keep your own AI logic.
For product and price data
ScrapeWise (our product)
AI scrapers plus proxies, browsers and scheduling, pay per page.
Compared
| What breaks | Why | Fix |
|---|---|---|
| Blocking | Data-centre IPs and bot detection | Residential proxies, real browsers, or a provider that handles it |
| Silent layout changes | Sites redesign without notice | Coverage monitoring and automatic re-extraction |
| Boilerplate in text | Menus, footers and banners | Main-content extraction and cleaning rules |
| Duplicates | Same content at many URLs | Canonical URLs and near-duplicate removal |
| LLM extraction cost | A model call per page | Generate selectors once, run them cheaply |
| Missing provenance | No source or date on records | Store URL and fetch time with every record |
Build or buy?
- Build when
- Scraping is core to your product, targets are unusual, or you need full control of the stack.
- Buy when
- You need reliable data from many sites and would rather spend engineering time on your model.
- Mix
- Buy collection and anti-blocking; keep extraction rules and data cleaning in-house.
Frequently asked questions
What actually breaks when you build an LLM scraper in-house?
Blocking, silent layout changes, boilerplate, duplicates, rising model costs and missing provenance, more than the scraping code itself.
Is it cheaper to build or buy a scraper?
Building looks cheaper at first; including proxies, browsers, monitoring and engineering time, buying the collection layer is often cheaper.
Should I use an LLM to extract every page?
Usually not. Use the model to work out the extraction once, then run cheaper rules, and call the model only when a page changes.
How do I know my scraper is still working?
Track coverage: the share of target pages that returned valid data each run. A sudden drop means blocking or a layout change.