Solution
Residential proxies for LLM training data
Training a competitive model starts with a clean, diverse crawl — millions of pages across every region and language. IPStation gives your crawlers residential IPs at crawl scale, so your dataset reflects the real web, not the version sites serve to bots.
Why AI data pipelines need residential proxies
Scale: millions of pages, thousands of domains
LLM pretraining eats billions of tokens. A serious crawl spans millions of pages across news, forums, docs and blogs in every language — no single IP or datacenter range survives that volume.
Deduplication starts at collection
The same URL served to different geos or A/B cohorts returns different content. Collect with consistent geo targeting and content-hash dedup, and your pipeline wastes no GPU hours on near-duplicates.
Compliance is infrastructure, not an afterthought
Responsible crawlers honor robots.txt, crawl-delay and terms of service. We provide the network layer; your stack enforces the policy — Crawl4AI's check_robots_txt flag does it in one line.
Bot-served content poisons datasets
Sites serve stripped-down or fake pages to detected crawlers. If your training data comes from bot-walled content, your model learns a distorted web. Residential IPs collect what humans actually see.
Recommended proxies
The plans our customers use most for this workload.
Up and running in 60 seconds
Activate a plan, copy your credentials from the dashboard, and drop them into your stack:
# Cost math for 5M pages (avg 80 KB HTML each):
# 5,000,000 x 80 KB = ~400 GB
# 400 GB x $1.60/GB = ~$640 on the 500 GB plan
# Text-only extraction (trafilatura) keeps it even leaner.import asyncio
from crawl4ai import AsyncWebCrawler, BrowserConfig, CrawlerRunConfig
async def main():
browser_config = BrowserConfig(
proxy_config={
"server": "http://gate.kookeey.info:1000",
"username": "48210-jdoe88-US-a1b2c3d4",
"password": "P4ssw0rd",
}
)
async with AsyncWebCrawler(config=browser_config) as crawler:
# check_robots_txt=True: honor robots.txt automatically
result = await crawler.arun(
url="https://example.com/article",
config=CrawlerRunConfig(check_robots_txt=True),
)
print(result.markdown[:500])
asyncio.run(main())
# --- Scrapy equivalent (per-request rotation) ---
# yield scrapy.Request(url, meta={
# "proxy": "http://48210-jdoe88:[email protected]:1000"
# })Common questions
Rule of thumb: 5 million pages at ~80 KB of HTML each is ~400 GB of traffic, which costs about $640 on our 500 GB plan ($1.60/GB). Text-only extraction pipelines typically use far less. Start with the $4.99 / 2 GB plan to benchmark your real per-page cost before scaling.
Residential for anything where content quality matters — news, forums, e-commerce, social. Datacenter IPv6 works for high-volume, low-trust-barrier sources (docs, sitemaps, open datasets) at a fraction of the cost. Most serious pipelines use both.
Dedup by content hash (SimHash/MinHash) after collection, and reduce duplicates at the source: consistent geo targeting per domain so you don't collect the US and DE variants of the same page as two documents.
We provide compliant infrastructure: the network never interferes with your crawler's policy layer. Honor robots.txt and crawl-delay in your stack — Crawl4AI does it with check_robots_txt=True, Scrapy with ROBOTSTXT_OBEY=True — and identify your bot with a contactable User-Agent.
The pool holds 90M+ IPs with unlimited concurrent sessions on dynamic plans. Throughput is bounded by your own fetcher fleet and how politely you pace per-domain requests, not by us.
HTTP for plain fetchers (Scrapy, httpx), SOCKS5 for browser-based crawling (Playwright, Crawl4AI). Every plan speaks both — switch without changing plans.
Keep exploring
Feed your model the real web.
200 MB free on signup. Benchmark your per-page proxy cost on a real crawl today — then scale to millions of pages with USDT payments, no card needed.