Fuel Your AI Models
With Clean Data.
Build massive datasets for LLMs, RAG, and Computer Vision. Plan permitted collection of text and image samples with a fixed-price throughput tier.Transferred data is unmetered for the purchased duration.
import requests
from langchain.document_loaders import WebBaseLoader
# Proxy for unlimited scraping
proxies = {
"http": "http://user:pass@<YOUR_GATEWAY_HOST>:7777",
"https": "http://user:pass@<YOUR_GATEWAY_HOST>:7777"
}
def fetch_training_data(url):
# Rotate IP automatically to avoid 403 Forbidden
response = requests.get(url, proxies=proxies)
if response.status_code == 200:
# Feed clean HTML to your vector DB
return process_for_rag(response.text)
# Scrape massive datasets without bandwidth limits
urls = ["https://wiki-source.com/ai", "https://news.com/tech"]
for url in urls:
data = fetch_training_data(url)
print(f"Ingested {len(data)} tokens.")Why AI Projects Fail with Standard Proxies
Large data-collection workloads can make per-GB proxy costs hard to forecast. Compare your expected traffic with a fixed-price plan, while accounting for the selected speed tier and target conditions.
Volume & Velocity
Size parallel workers against the selected aggregate throughput tier. Request latency, retries, payload size, and exit availability affect observed results.
Access Considerations
Residential exits provide a different network perspective, but do not bypass site permissions or guarantee access through challenges.
Flat-Fee Pricing
Choose a fixed price for 1, 7, or 30 days of unmetered traffic at the selected throughput tier.
Essential for Modern AI Workflows
Whether you are fine-tuning Llama 3 or building a real-time RAG application, you need external data.
LLM Training & Fine-Tuning
Collect diverse text data from forums, news sites, and specialized wikis to train your models on niche domains (Medical, Legal, Coding).
- ✓ Scrape Common Crawl alternatives
- ✓ Multi-language data extraction
RAG (Retrieval-Augmented Generation)
Feed a vector database with permitted source material on a schedule you control. Freshness depends on source updates, collection success, and your indexing pipeline.
- ✓ High-frequency scraping
- ✓ Low latency response