AI Training Data Collection
with Proxies

Every major large language model—GPT-4, Claude, Gemini, Llama, Mistral—is trained on data crawled from the open web. The scale is staggering: GPT-3 was trained on roughly 300 billion tokens drawn from approximately 45 terabytes of text. GPT-4 and its successors use datasets orders of magnitude larger. The Common Crawl project, which many AI labs use as a starting corpus, contains over 250 billion web pages accumulated over more than a decade.

But raw volume is only part of the story. AI companies need fresh data. Models trained on stale corpora produce outdated answers, miss recent events, and fail to understand evolving language patterns. This is why AI labs do not simply download Common Crawl once and call it done. They run continuous crawling operations that fetch new and updated pages daily, feeding this fresh data into ongoing pre-training and fine-tuning runs.

The result is that AI companies have become the largest new consumers of web crawling infrastructure. According to industry analyses, AI-related crawling now accounts for a significant and growing share of all bot traffic on the internet. Cloudflare, Akamai, and other CDN providers have reported measurable increases in crawler traffic attributable to AI data collection operations.

This demand is not slowing down. As models grow larger and multimodal capabilities expand, the appetite for diverse web data—text, images, code, structured data, multilingual content—continues to accelerate. For the infrastructure that supports this crawling, particularly proxy networks, the AI training data market represents the fastest-growing demand segment.

Training an LLM is not as simple as pointing a crawler at the internet and feeding the results into a model. The data goes through a multi-stage pipeline where each step transforms raw web content into something suitable for training. Understanding where proxies fit in this pipeline explains why they are so critical to the entire operation.

  AI Training Data Pipeline
  ==========================

  +------------------+     +------------------+     +------------------+
  |                  |     |                  |     |                  |
  |    CRAWLING      |---->|    CLEANING      |---->|  DEDUPLICATION   |
  |                  |     |                  |     |                  |
  |  Fetch raw HTML  |     |  Strip boiler-   |     |  Remove exact &  |
  |  from millions   |     |  plate, ads,     |     |  near-duplicate  |
  |  of web pages    |     |  navigation      |     |  documents       |
  |                  |     |                  |     |                  |
  +--------+---------+     +------------------+     +--------+---------+
           |                                                 |
    [PROXIES HERE]                                           v
    Mobile IPs for                                  +------------------+
    high success rate                               |                  |
    Geo-distributed                                 |    FILTERING     |
    for diverse data                                |                  |
                                                    |  Quality scores  |
           +----------------------------------------|  Toxicity filter |
           |                                        |  PII removal     |
           v                                        |                  |
  +------------------+                              +--------+---------+
  |                  |                                       |
  |    TRAINING      |<--------------------------------------+
  |                  |
  |  Tokenize and    |
  |  train on GPU    |
  |  clusters        |
  |                  |
  +------------------+

The key takeaway is that proxies are the foundation of the entire pipeline. If the crawling step fails—because IPs get blocked, requests get rate-limited, or geographic coverage is insufficient—every downstream step suffers. Poor crawling yields incomplete data, which means the model trains on a biased or sparse view of the web. This is why AI companies invest heavily in proxy infrastructure, and why the quality of that infrastructure directly impacts model quality.

The tension between AI companies and website owners is one of the defining conflicts of the current AI era. Website publishers invest in creating content, and AI companies crawl that content to train models that may reduce the need for users to visit those sites. This has led to an arms race where websites increasingly block AI crawlers, and AI companies increasingly need sophisticated proxy infrastructure to maintain data access.

It is important to note that using proxies does not bypass robots.txt or legal restrictions. Proxies solve the technical problem of IP-based blocking and rate limiting. Responsible AI data collection still requires checking and respecting robots.txt, honoring crawl-delay directives, and complying with applicable laws.

Not all proxy infrastructure is created equal, and AI data collection places specific demands that differ from general web scraping. The scale, diversity, and continuity requirements of training data pipelines mean that proxy networks must meet a higher bar across multiple dimensions.

Among all proxy types—datacenter, residential, ISP, and mobile—mobile proxies offer the strongest combination of advantages for AI training data collection. The reasons are rooted in how mobile networks work and how anti-bot systems evaluate traffic.

The economics are clear when you factor in success rates. While mobile proxies have a higher per-GB sticker price than datacenter proxies, they waste far less bandwidth on blocked requests. For AI data collection where every page of training data matters, the higher success rate means mobile proxies often deliver the best cost-per-useful-page ratio, especially on protected sites.

For teams running continuous AI crawling pipelines, the volume pricing at Proxies.sx (down to $4/GB at 501-1000GB) makes mobile proxies particularly competitive for large-scale data collection operations where reliability and data quality are paramount.

A growing ecosystem of crawling tools has emerged specifically designed for AI data collection. These tools focus on converting web pages into clean, structured formats that LLMs can consume directly—markdown, JSON, or plain text stripped of HTML boilerplate. Here are the leading frameworks and how to integrate mobile proxies with each.

# Example: Crawl4AI with Proxies.sx mobile proxies
from crawl4ai import AsyncWebCrawler

async def crawl_for_training_data():
    proxy_url = "http://USER:PASS@proxy.proxies.sx:PORT"

    async with AsyncWebCrawler(
        proxy=proxy_url,
        headless=True,
    ) as crawler:
        result = await crawler.arun(
            url="https://example.com/article",
            # Output clean markdown for LLM training
            word_count_threshold=50,
            remove_overlay_elements=True,
        )

        # result.markdown contains clean LLM-ready text
        training_text = result.markdown
        print(f"Extracted {len(training_text)} chars")

All of these tools work with mobile proxies from Proxies.sx. The integration is typically a one-line configuration change: pass your proxy credentials to the crawler, and all requests are automatically routed through mobile IPs with rotation. Check our documentation for detailed setup instructions for each framework.

AI training data collection operates in a rapidly evolving legal and ethical landscape. The question of who can crawl what, for what purpose, and under what conditions is being actively litigated in courts and debated in standards bodies. Teams building AI data pipelines must stay informed about these developments.