V-PROXIES / USE CASES / AI & ML
Collect training data, fine-tuning corpora, and benchmarks from the web at any scale. Geo-targeted IPs for multilingual datasets. No rate limits. Datacenter pricing from $0.40/GB.
99.7%
UPTIME · 30d
148ms
P50 RESPONSE
84,219,553
ACTIVE IPS
12,833
REQ/S · AVG
::01
Training data collection
Scrape text, images, and structured data from any public source for LLM pretraining.
::02
Fine-tuning corpora
Collect domain-specific content — legal, medical, code, e-commerce — with topic-targeted crawls.
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Multilingual datasets
Exit from 120+ countries. Collect native-language content from local sites for each target language.
::04
Benchmark data
Scrape live data for time-sensitive ML benchmarks — pricing, news, product listings — without bans.
01 // POOL RECOMMENDATION
::datacenter · $0.40/GB — recommended for bulk
Unprotected sources
Wikipedia, open datasets, RSS feeds, government data, and most public APIs. At $0.40/GB, a 10 TB crawl costs $4,000 — the cheapest option at scale.
::residential · $2.40/GB
Protected sources
News sites with Cloudflare, social platforms, e-commerce sites with DataDome or Akamai. Residential IPs are required to bypass these without CAPTCHAs.
02 // DATA PIPELINE EXAMPLE
python — async data collection pipeline
import asyncio
import httpx
PROXY = "http://u_a91c2f:p_xk9m2r4n8q1vw3@v-proxies.com:9000"
async def fetch(client, url):
resp = await client.get(url, timeout=30)
return resp.text
async def collect(urls: list[str]):
async with httpx.AsyncClient(proxy=PROXY) as client:
tasks = [fetch(client, url) for url in urls]
return await asyncio.gather(*tasks, return_exceptions=True)
# Collect 1000 pages concurrently
pages = asyncio.run(collect(url_list))03 // RELATED
04 // FAQ
Why do AI and ML teams need proxies?
Large-scale web data collection for training datasets, fine-tuning corpora, and benchmarks requires rotating IPs to avoid rate limits and bans. A single IP scraping millions of pages triggers blocks within hours. Rotating residential proxies distribute requests across thousands of IPs, enabling continuous collection.
What kinds of data can I collect with v-proxies?
Text corpora from news sites, forums, Wikipedia mirrors, and blogs. Image datasets from content platforms. Structured product data from e-commerce. Social content from public profiles. Multilingual datasets using geo-targeted IPs in the target language's country.
How do I build a multilingual dataset?
Use country targeting to exit from the target language's home country. For German content, use -country-de. For Japanese content, use -country-jp. This ensures you receive locally relevant content rather than English-localized versions.
Can I use v-proxies with common ML data pipelines?
Yes. v-proxies works with any HTTP proxy-compatible tool — including Scrapy, Apache Nutch, Common Crawl custom crawlers, Playwright, and custom Python pipelines using requests or httpx.
How much does collecting a 1 TB text corpus cost?
Raw HTML for 1 TB of text content is approximately 3–5 TB of bandwidth (accounting for HTML overhead and failed requests). At $0.40/GB (datacenter pool), that's $1,200–2,000. For protected sources requiring residential IPs, budget at $2.40/GB.