Proxies for AI scraping. Feed the model clean data.
Language models are only as good as the data behind them, and that data lives on sites that block bulk collection. Residential IPs and a scraper API gather it at the scale training needs, without the blocks.
What fits
2 products fit, best first
ResidentialRecommended for AI scraping
Real-device IPs in 200+ countries
- Rotating or sticky sessions
- State, city and ISP targeting on Premium
- Pay per GB, balance never expires
- HTTP and SOCKS5
From
$0.44/ GB
Web Scrapers
Structured JSON from one REST call
- Proxies, retries and parsing handled
- Blocked or errored calls are not billed
- Pay per call from your balance
- No subscription, no monthly minimum
From
$1.49/ 1,000 calls
- Residential proxies
- The broad tool: real home IPs that read as ordinary visitors, so a crawl gathering millions of pages does not trip the defences bulk collection usually does.
- Scraper API
- When you would rather not run the crawler at all: send a URL, get clean structured data back, with the proxy, retries and unblocking handled for you.
Training data is a volume problem, and volume is what gets blocked
Assembling a dataset means fetching pages by the million, and no site distinguishes a research crawl from an abusive one by intent. It counts requests per address, and a single IP pulling that volume is the first thing rate limits and bot defences are built to stop.
Spreading the crawl across a large residential pool is what makes the volume survivable. Each address carries a small share of the total and reads as a genuine visitor, so the crawl completes instead of collapsing into a wall of blocks and CAPTCHAs a few thousand requests in.
Clean input, or clean output
There are two honest ways to do this. Run your own crawler and route it through residential IPs, which gives you full control over what you fetch and how. Or hand the fetching off to the scraper API, which takes a URL and returns parsed data, absorbing the proxy rotation, retries and unblocking so your pipeline only ever sees clean results.
Neither is a dataset you buy off a shelf, and we do not pretend to sell one. What we provide is the collection layer, the IPs and the fetching, that turns the open web into the input your model actually trains on.
How to set up proxies for AI scraping
- 1
Decide who fetches
Run your own crawler on residential lines when you need control over every request. Use the scraper API when the source is one of its platforms and parsed results are what you want back.
- 2
Generate lines for the crawl
Choose a country, or several, and one line per worker. Rotating lines spread the volume, so no single address carries much of it.
- 3
Pace every worker
Keep each worker to the request rate one visitor would produce, and let the number of lines carry the volume instead.
- 4
Keep only real pages
Check every response before it enters the dataset: the status code, the length and a word you expect on a real page. Many block pages answer with status 200.
- 5
Record where each page came from
Store the URL, the date and the exit country with every page, so a page that turns out to be a block or a regional variant can be found and removed later.
AI scraping: what goes wrong, and the fix
- Block pages end up in the dataset
- A challenge page answered with status 200. Test for it before you store a page, and fetch that page again on a fresh residential line.
- The same page arrives many times
- One page lives under several addresses. Normalise the URLs and drop repeats by a hash of the content.
- Regional versions mix in the dataset
- The exit country changed between workers. Fix the country on each line so every page comes from the market you meant.
- Throughput drops after a while
- The site slows down a repeated pattern. Spread the crawl across more lines and vary the order of the requests.
Setup guides and tools
FAQ
AI scraping questions, answered
Can I use these proxies to collect training data for an LLM?
Yes. Residential proxies and the scraper API are the collection layer for exactly this: fetching public web pages at the scale a training corpus needs. What you scrape and how you use it is your call and your responsibility; the proxies are the transport.
Should I use residential proxies or the scraper API?
Use residential proxies when you run your own crawler and want control over the fetch. Use the scraper API when you would rather send a URL and get clean data back, with the blocking handled. Many pipelines use both: the API for hard targets, raw residential for everything else.
Will a datacenter proxy work for AI scraping?
For undefended sources, yes, and it is cheaper. But the sites worth scraping for quality data usually screen datacenter ranges on sight, so at any real scale residential is what keeps the crawl alive.
Do you sell AI datasets?
No. We sell the proxies and the scraper API that let you collect data yourself. We do not run a dataset marketplace or a model, and we will not imply we do.
How do I keep block pages out of the dataset?
Check every response before you keep it: the status code, the length and a word you expect on a real page. Many block pages answer with status 200, so the status code alone does not catch them.
Do I need residential proxies for every source?
No. Sources that do not screen by network work on datacenter addresses, which cost less. Use residential where a source blocks datacenter ranges.
Related: AI agents, Web scraping.