Mobile Proxy AI Training Data

AI Training Data Requirements

It is worth being blunt at the start, because most pages on this subject are not. If you are running a declared AI crawler, a proxy does not get you past the controls that now block it. Those controls key on the crawler identity you publish, not on the address it arrives from. Changing the exit IP while continuing to declare the same crawler changes nothing except how hard the refusal is to attribute.

What a proxy legitimately solves for dataset work is geography. A very large share of the web is served per region — local news, regional catalogues, market-specific documentation, country editions of large publications — and a great deal of it is unavailable, abridged or differently written outside its market. Collecting a corpus from one country produces a corpus that over-represents that country’s framing of every subject. That is a data quality problem long before it is anything else, and it is the problem distributed collection actually addresses.

Scraping Datasets at Scale

The economics of dataset collection changed materially between 2025 and 2026, and understanding what changed is more useful than any technique. Cloudflare moved from leaving the decision to individual sites to blocking AI crawlers by default, asking every new domain up front whether AI crawlers are permitted. From September 2026 that default extends further, covering mixed-use crawlers — those blending search, agent and training behaviour — on any page carrying advertising.

Alongside the blocking, a payment channel appeared. Pay Per Crawl answered a request with a 402 Payment Required, letting publishers set a rate and crawlers decide whether to pay. Its successor shifted the trigger: publishers are paid when their content is actually used in an answer rather than when a bot fetches the page. Either way, the assumption that public means free to ingest is no longer how the infrastructure behaves.

The scale of the shift is visible in the traffic. By June 2026 training crawlers made up 50.6 per cent of AI bot traffic on Cloudflare’s network while search bots had fallen to 10.7 per cent, and more than 2.5 million sites had already chosen to fully disallow AI training as far back as August 2025. Publishers stopped treating AI crawling as a rounding error on their search traffic and started pricing it as its own thing.

source_url        …
fetched_at        2026-08-24T09:14:02Z
robots_directive  …          # what was in force at fetch time, not today
stated_licence    …          # as published on the page
market            FR         # the exit country you fetched from
language          fr
content_hash      …          # for deduplication across markets

Data Diversity and Quality

Diversity in a training corpus is usually discussed as a fairness property. It is also, more prosaically, an accuracy property. A model trained on material collected entirely from one country inherits that country’s defaults on everything from measurement units and date formats to which side of a debate counts as the neutral one. None of that is deliberate; it is simply what the sample contained.

• Sample the locale, not just the language — Spanish collected only from Spain is not Spanish. Regional editions differ in vocabulary, subject matter and what gets published at all.
• Deduplicate across markets, not within them — Country editions of the same publication share a great deal of text. Content hashing across the whole corpus stops one syndicated article appearing forty times.
• Record the market as a field — Without it you cannot measure your own coverage, rebalance a corpus, or answer a question about representation after the fact.
• Watch for the generic fallback — Requesting a regional edition from outside the region often returns an international default. Collecting that at scale produces the appearance of coverage without the substance.
• Prefer breadth over depth — Another thousand documents from a source you already have adds far less than the first hundred from a market you do not.

Avoiding Blocks During Collection

For the collection that is open to you, the discipline is the same as any other large crawl and it is covered properly in the scraping guides. Keep per-host request rates at a level that does not impose real cost on the publisher. Honour Retry-After rather than treating a 429 as something to route around. Cache aggressively and use conditional requests so a re-crawl costs the origin almost nothing.

The distinction worth holding onto is between a block and a refusal. A rate limit is a request to slow down, and slowing down is the correct response. A robots directive or a licensing gate is a decision about permission, and the correct response there is to respect it or to negotiate access — not to change addresses until the answer changes. Conflating the two is how dataset programmes acquire legal problems that outlive the model they were built for.

For the mechanics of running a large crawl politely, see web scraping best practices, and for reading a response correctly before reacting to it, common blocks and how to read them.

Collect Each Locale From Inside It

Live PXM2 locations — add a vantage point for every language and market your corpus is meant to represent:

Frequently Asked Questions

Related Mobile Proxy Guides

Dataset work borrows its collection discipline from the scraping cluster and its sampling logic from market research.

Business use cases

Core mobile proxy guides