Proxies for AI and LLM Training Data Collection

The scale of AI data collection

Training and evaluating machine learning models, especially large language models, requires enormous amounts of text, code, images and structured data. Some of it comes from licensed datasets and public datasets like Common Crawl. Some is collected directly from the web for specific domains, freshness or quality. That collection runs into the same obstacles as any large crawl: rate limits, IP bans, bot protection and geography.

Proxies are part of the infrastructure, but AI data collection also carries particular legal and ethical expectations. This guide covers both.

Respect opt-outs first

A growing number of sites signal whether they're willing to be used for AI training:

• robots.txt rules for specific AI crawler user agents.
• Meta tags and headers some sites use to express AI-use preferences.
• Terms of service clauses about AI training.
• Licensing signals on content.

Responsible collectors identify their crawler honestly with a user agent, publish information about it, and honour these signals. Using proxies to disguise a crawler in order to evade opt-outs is the kind of behaviour that leads to legal disputes and reputational damage. See ethical web scraping.

Legal considerations

• Copyright: training on copyrighted works is the subject of active litigation and evolving law in many jurisdictions.
• Personal data: GDPR, UK GDPR and similar laws apply to personal data in training sets.
• Contracts: site terms may restrict automated access.

Get legal advice for your situation. See are proxies legal?.

Proxy architecture for large crawls

For a polite, identified crawler, datacenter proxies or your own IP space are usually appropriate. Residential IPs are best reserved for legitimately region-specific content.

Politeness at scale

• Per-domain concurrency limits and crawl delays.
• Honour Retry-After and back off on errors.
• Spread crawling over time.
• Use conditional requests to avoid re-downloading unchanged pages.

See rate limiting and proxies.

Data quality

• Remove boilerplate, navigation and duplicate content.
• Detect and discard block pages and soft errors.
• Track provenance (URL, fetch time, licence signals) for every document.

Cost control

At AI scale, bandwidth costs dominate. Prefer datacenter or own infrastructure for bulk crawling; residential per-GB pricing becomes very expensive at terabyte scale. See proxy pricing guide.

Example crawler identification

A transparent AI crawler typically:

• Uses a descriptive user agent such as ExampleAIBot/1.0 (+https://example.com/bot).
• Publishes a page explaining purpose, opt-out instructions and contact details.
• Honours robots.txt rules for its user agent.
• Publishes its IP ranges so site owners can verify it.

Publishing IP ranges is another reason identified crawlers usually run on datacenter or owned infrastructure rather than residential proxies.

Dataset documentation

Keep, for each collected dataset:

• Sources and collection dates.
• robots.txt and opt-out status at collection time.
• Licence signals observed.
• Filtering applied (personal data removal, deduplication, quality filters).
• Retention and deletion policies.

Good documentation helps with legal review, model cards and responding to removal requests.

Removal requests

Have a process to handle site owners' and individuals' requests to remove their content or data from datasets, and honour them in future collections.

Where proxies still help

• Regional content available only in certain countries.
• Load distribution across your own IP ranges.
• Resilience when some ranges are temporarily rate-limited.

See web scraping proxies and datacenter proxies.

Respecting AI-specific opt-outs

Website owners now have several ways to say "not for AI training", and a responsible crawler checks all of them:

• robots.txt rules for named crawlers. Many sites disallow specific AI crawler user agents, such as OpenAI's GPTBot, Anthropic's ClaudeBot or Common Crawl's CCBot, while allowing search crawlers. The robots.txt standard is RFC 9309.
• Product tokens that separate uses. Google, for example, documents a Google-Extended token that lets sites opt out of some AI uses without leaving Search; see its crawler documentation.
• Text and data mining reservations. In the EU, rights holders can reserve TDM rights in machine-readable form, and the AI Act requires providers of general-purpose AI models to have a policy for honouring those reservations.
• Infrastructure defaults. In July 2025, Cloudflare moved to block AI crawlers by default unless they compensate site owners, so many sites now opt out without editing anything themselves.

For your crawler, that means: use a distinct, documented user agent; evaluate robots.txt for that agent and for any tokens you honour; record what each site said at crawl time; and never route around an opt-out with proxies. Proxies help with scale and geography, not with permission. See ethical web scraping and are proxies legal.

Key takeaways

• Responsible AI crawlers identify themselves, publish contact and opt-out information, and honour robots.txt and other opt-out signals.
• Copyright, personal data and contract law all apply to training data collection; get legal advice.
• Datacenter proxies or your own IP ranges suit identified, polite crawling; residential IPs are mainly for region-specific content.
• Per-domain rate limits, caching and conditional requests keep load on sites reasonable.
• Document provenance, licence signals and filtering for every dataset, and have a removal process. See ethical web scraping.

FAQ

Do AI crawlers need residential proxies?

Usually not for polite crawling of open sites. Residential IPs are mainly for region-specific content.

Should AI crawlers hide their identity?

Responsible practice is to identify your crawler and honour opt-outs.

Is Common Crawl enough?

For many purposes it's a strong starting point; specialised domains often need targeted collection.