Large language models are built out of the web. The clearest illustration is the model that started the current era: a filtered version of Common Crawl made up 60% of GPT-3's weighted training mix, 410 billion tokens of scraped web text, with the rest coming from books, Wikipedia and a curated web subset. Every model since has the same appetite. Whether you are assembling a training corpus, building a retrieval-augmented generation (RAG) index, or pulling fresh pages to feed a model at query time, you are doing web collection at scale, and proxies for LLM scraping are what keep that collection from being blocked before it finishes.
We run the network this collection rides on, so we see it from the supply side: the teams building domain-specific corpora, the RAG pipelines refreshing their sources, the researchers sampling the web to study it. This is the honest guide to gathering web data for models: why the open commons that fed the first LLMs is closing, which proxy type fits which source, how to keep the collected data clean enough to train on, and where the legal and ethical lines sit. If you want the collection mechanics first, our web scraping guide is the groundwork this builds on.
Why does LLM scraping need proxies?
Because the good data sits on sites that increasingly refuse automated AI collection. Bulk scraping from one IP gets rate-limited and blocked. Declared AI crawlers get turned away on a rising share of the web. And source material is geo-gated, so a corpus pulled from one location is skewed toward that slice of the web. Proxies spread the load across many IPs to stay unblocked, and let you collect across regions so the data is not lopsided.
The open commons is closing
For years the deal was simple: the web was open, Common Crawl packaged it, and models trained on the result. Common Crawl has been collecting petabytes of web data since 2008, reaching 3.1 billion web pages by April 2023, and it has trained more than GPT-3, including Google DeepMind's Gemini. That pre-packaged corpus is still a real starting point for a general model.
What changed is that the web started saying no. The Data Provenance Initiative ran the first large-scale longitudinal audit of consent signals across the domains behind the major training corpora, and the finding is stark: in a single year, 5% or more of all tokens in the widely used C4 corpus, and 28% or more of its most actively maintained and critical sources, became fully restricted to AI crawlers through robots.txt, with terms-of-service restrictions covering far more. The infrastructure layer moved the same direction. Cloudflare found AI bots reaching roughly 39% of the top one million websites it protects, and in July 2025 it changed its default to block AI crawlers unless they pay for the content.
The practical consequence for anyone gathering LLM data today: the easy, open, pre-packaged path is narrowing, precisely for the highest-quality sources, and a declared crawler coming from a datacenter is the first thing that gets refused. Collecting current, specialized or high-quality data increasingly means doing it yourself, through IPs that read as ordinary users rather than as an announced AI bot.
Two jobs, both collection
"LLM scraping" covers two related jobs, and proxies serve both.
The first is corpus building: gathering a large body of text to train or fine-tune a model, or to build a specialized dataset for a vertical (legal, medical, code, a specific language or region). This is bulk, mostly stateless collection where volume and coverage are everything.
The second is retrieval: pulling web pages to feed a model at query time, the fetch layer behind a RAG system or a research assistant. This is lighter and more continuous, but it hits the same walls, because a RAG pipeline that fetches forty live sources per query from one server IP gets rate-limited exactly like a scraper. Our companion guide on proxies for AI agents covers the interactive, task-driven end of that retrieval work.
Both are web collection, and both meet the standard defenses: rate limits, IP blocks, bot walls and geo-gating. Proxies spread the requests and place them in the right region; they do not, by themselves, defeat the browser-level fingerprinting on the hardest targets, which is the honest caveat that runs through all of this.
Which proxy type fits which source
The economical instinct is the right one: start on the cheapest tier a source will accept, and step up only when block rates leave no choice.
Datacenter proxies are the fastest and cheapest, and correct for the friendly half of LLM collection: open data repositories, public datasets, government and reference sites, and any API that does not scrutinize IP reputation. A great deal of raw corpus material lives on sources like these, and paying for residential to collect them is wasted money. This is the tier to start on wherever the source tolerates it.
Rotating residential proxies draw each request from a pool of real home connections and read as ordinary consumers, so they clear reputation checks that reject datacenter ranges. They are what you need for the defended sources that hold the high-value modern data: news, forums, marketplaces, active communities, the very sources the Data Provenance audit found closing fastest to declared crawlers. They can be pinned to a country, which is also how you build a geo-balanced corpus rather than a lopsided one. Billed per gigabyte, and slower than datacenter.
Static residential and ISP proxies are the pick for the minority of sources that need a persistent session or a login to reach the content, where mid-session rotation would break the flow.
Mobile proxies are the reserve tier for the few sources that block everything else, at the highest price.
| LLM data source | Proxy type | Why |
|---|---|---|
| Open repositories, public datasets, friendly APIs | Datacenter | No bot team, cheapest and fastest per request |
| News, forums, marketplaces, active communities | Rotating residential | These block datacenter and declared AI crawlers |
| Geo-specific or multilingual sources | Residential, geo-pinned | A local exit reaches the local version of the web |
| Sources behind a login or session | Static residential / ISP | Session must persist, so rotation would break it |
| The most bot-hostile sources | Mobile | Carrier IPs shared by many users, rarely hard-blocked |
Bad data is worse than no data
There is a failure mode specific to collecting training data that is more dangerous than an outright block, because a block at least announces itself. It is the soft block: instead of refusing you, a defended site serves a CAPTCHA page, an empty result, a truncated snippet or a generic placeholder, and your collector stores it as if it were genuine content. No error is logged. The junk enters the corpus silently.
For an LLM this is a real problem, because a model is a mirror of its training data. Feed it thousands of CAPTCHA pages, boilerplate error screens and half-loaded articles and you have taught it to produce exactly that. Clean IPs that do not trigger soft blocks in the first place are the first line of defense, and they are the part a good proxy actually provides. The second is validation: confirm that a page parsed to the expected structure, that its length and language look right, that you got an article and not an interstitial, and log your block and anomaly rate so a rising number warns you before the poison spreads through the dataset. Our full checklist for staying clean is in avoiding IP bans while scraping.
Rotation and geography
Most corpus collection is stateless: independent pages, no login, no session that has to carry cookies. Draw a fresh IP per request for that, so no single address accumulates the steady footprint that flags a bulk collector. Reserve sticky sessions for the sources that need a login or a multi-step flow to reach the content, and hold one exit for the length of that flow.
Geography is not just an access concern for LLM data, it is a quality one. A corpus scraped entirely through one country's IPs over-represents that country's version of the web: its language, its outlets, its localized pages. If you want a model that is not lopsided toward one market, collect through exits spread across the regions you care about, which is exactly what country and city targeting on a residential pool is for. Pin one country per session so an identity never jumps continents between requests.
Keeping it legal and ethical
Gathering data to train a model sits in an actively contested legal area, and honesty serves you better than bravado. Collect publicly available data, the pages any visitor can reach without logging in or getting around a paywall. Respect the directives a site publishes, including its robots file, where you have agreed to. Pace your collection so you are not degrading the source. Be especially careful with personal data: names, contact details and anything identifying individuals pull you into laws like GDPR, and training on them raises questions well beyond scraping. Copyright is a live issue for AI training specifically, still being worked out in courts. Proxies are a technical tool, not a legal permission slip, so keep collection to public, non-personal data and get real legal advice before training on collected data commercially.
Where HProxy fits
We built the network for this exact mix of easy and hard sources. Cheap datacenter covers the open repositories and friendly APIs, residential proxies with country and city targeting reach the defended, geo-specific sources that hold the high-value modern data, and ISP handles the ones behind a login. Our pricing is pay-as-you-go at $0.65/GB with a balance that does not expire, which fits corpus collection that runs in bursts and RAG retrieval that runs continuously. Before a large run, point the free proxy checker at your exits to confirm they are alive and leaving from the right country. For the dataset-engineering side of this (scale, deduplication, provenance and the economics of petabyte collection), our companion guide on proxies for AI training data picks up where this one ends.
Sources
- GPT-3 training composition (filtered Common Crawl = 410 billion tokens, 60% of the weighted mix): https://en.wikipedia.org/wiki/GPT-3
- Common Crawl overview (petabytes of data collected since 2008): https://commoncrawl.org/overview and https://en.wikipedia.org/wiki/Common_Crawl (3.1 billion pages by April 2023; used to train Gemini)
- Consent in Crisis: The Rapid Decline of the AI Data Commons, Data Provenance Initiative (in one year, 5%+ of all C4 tokens and 28%+ of its most active sources became robots.txt-restricted): https://arxiv.org/abs/2407.14933
- Cloudflare, Declaring Your AIndependence (AI bots accessed ~39% of the top one million properties Cloudflare protects): https://blog.cloudflare.com/declaring-your-aindependence-block-ai-bots-scrapers-and-crawlers-with-a-single-click/
- Cloudflare, Content Independence Day (July 1, 2025 default change to block AI crawlers unless they pay): https://blog.cloudflare.com/content-independence-day-no-ai-crawl-without-compensation/