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Proxies for AI Training Data

Unmetered US ISP proxies for LLM training data pipelines: pre-training crawls, post-training and RLHF collection, RAG refresh. Flat per-IP pricing, no per-GB fees.

Proxies for AI training data collection

Unmetered US ISP proxies for LLM data pipelines. Pre-training crawls, post-training collection, and retrieval refresh at flat per-IP pricing.

Common Crawl gets you a starting point, not a corpus. The tokens that separate one model from another are the ones you crawl yourself, and at that point you are a first-party crawler being scored on your own addresses. Static US ISP addressing and unmetered bandwidth mean a corpus run costs what the IPs cost, not what the terabytes cost.

Typical volume: 1M-10M requests/day

The open corpus is the floor, not the dataset

FineWeb, DCLM-Baseline, Dolma, RedPajama and Nemotron-CC all start from the same Common Crawl snapshots and publish their recipes. What is left to compete on is the delta you crawl yourself, and that is a first-party crawl scored on your own addresses.

  • Common Crawl is a sampled archive with a fixed cutoff, so the long tail and anything recent has to be fetched directly
  • Corpus runs are sustained for weeks, which is the exact pattern a shared rotating pool cannot hold reputation through
  • Post-training collection returns to the same high-signal hosts every round, so an address burned once stays burned
  • Per-GB bandwidth pricing puts the cost of a terabyte-scale crawl in the bandwidth line rather than the compute
  • Declared AI training crawlers are now the most-blocked entries in robots.txt, and Cloudflare defaults new sites to blocking them
  • A rotating exit makes a failed fetch impossible to reproduce, because the address that failed is gone before you read the log

Static US addressing, unmetered, at corpus scale

100 Gbps of transit out of Ashburn, next door to the cloud region most training clusters and the Common Crawl archive already sit in, and address blocks that belong to one pipeline. A crawl runs flat out for weeks without sharing a pool and without a bandwidth meter running underneath it.

  • Unmetered bandwidth on every plan, so a 50TB run costs the same per IP as a 50GB one
  • 100Gbps transit for pre-training crawls that have to finish inside a training schedule
  • Static residential ISP addressing from US Tier 1 carriers, dedicated to one workload
  • Subnet-diverse US space, or a private /24 to /22 that no other tenant touches
  • A failed fetch is reproducible, because tomorrow's retry leaves from the same address
  • Bearer-token API to add capacity mid-crawl or replace a single burned IP without pausing the pipeline
  • Flat per-IP pricing, never per gigabyte
  • Ashburn transit, adjacent to us-east-1 where the archives live
  • 99.994% network uptime, with a named account manager on enterprise

How it works

  1. Scope the delta: Work out what the open corpora already cover and what you actually have to crawl: the long tail, the post-cutoff pages, and the hosts Common Crawl only sampled.
  2. Point the fetch layer at us: One proxy setting in Scrapy, Crawl4AI, Crawlee, Firecrawl or Playwright. Extraction and dedup sit downstream of the fetch and do not change.
  3. Pin hosts to addresses: Because the pool is static, you can map each host or shard to a fixed exit. Retries and resumed jobs keep the reputation the first pass built.
  4. Scale and swap from the API: Add capacity when the run grows, and replace any single address that starts picking up friction while the rest of the crawl keeps going.

Key metrics

Bandwidth billed
$0/GB
Transit capacity
100Gbps
Network uptime
99.994%

Recommended products

Private Subnets (recommended)
Pre-training scale. A /24 to /22 of contiguous static residential space that belongs to one pipeline, so reputation is yours to manage rather than inherited from whoever shares the pool.
Static ISP Proxies
Post-training and refresh work. Dedicated addresses bought by the IP, replaceable one at a time from the API when a specific host starts pushing back.

Frequently asked questions

What kind of proxies do I need to collect LLM training data?

It depends on the stage. A pre-training corpus crawl is a sustained, weeks-long run over many hosts, so it wants dedicated addressing you control end to end: our Private Subnets, a /24 to /22 of static residential ISP space allocated to one pipeline. Post-training collection and eval refresh jobs hit a shorter list of protected hosts repeatedly, which is a per-IP problem rather than a block problem, so static ISP proxies with per-IP replacement fit better. All of it is US-based, static, and reached over HTTP or HTTPS with a username and password.

Do I still need to crawl if I already have Common Crawl?

Almost always, yes. Common Crawl is a sampled snapshot with a fixed cutoff, not a mirror, so three things are always missing: pages published since the last snapshot, the long-tail domains the crawl did not reach, and full-fidelity recrawls of hosts it only sampled. Every open corpus that outperforms a plain Common Crawl derivative does it partly on that delta. The moment you fetch it yourself you are a first-party crawler being scored on your own addresses, which is where the proxy layer starts to matter.

How does flat-rate proxy pricing compare to per-GB for a training crawl?

This is the single biggest cost line in AI data collection and it is where per-GB pricing hurts most. Training corpora are measured in terabytes, and residential bandwidth from the large vendors runs roughly $1 to $8 per GB at list in 2026, and higher on some networks. A 50 TB collection run is over 51,000 GB, so even at the low end the bandwidth bill dominates everything else in the pipeline. Our pricing is flat per IP per month with unmetered bandwidth, so the cost of a crawl is the cost of the addresses and does not move when the corpus gets bigger.

Can I use these for post-training data, RLHF and RLVR collection?

Yes, and it is one of the better fits. Post-training collection is narrow and repeated: supervised fine-tuning sets, preference pairs for DPO and RLHF, and the problem banks and test cases behind verifiable-reward training all come from a short list of high-signal hosts that you return to every time the recipe changes. Because each address is dedicated and static, reputation carries between collection rounds instead of resetting, and any single IP that starts picking up friction can be replaced from the API without touching the rest.

Do you offer rotating residential proxies for crawling?

No. Every address on this network is static and dedicated to you, and there is no rotating gateway and no non-US exits. For corpus work that is usually the point, because a static exit makes a failed fetch reproducible and lets reputation accrue across a long crawl. If your pipeline genuinely depends on a large rotating pool, this is the wrong network and we would rather tell you before you buy.

Does Cloudflare blocking AI crawlers affect my pipeline?

It changes what your traffic has to look like. From 15 September 2026 Cloudflare moved new sites and existing free-tier accounts to a default that still allows search crawling but blocks training and agent use on ad-bearing pages, and its Pay Per Crawl work has become a Pay Per Use marketplace where publishers are compensated when content appears in AI answers. Declared training crawlers are also the most-blocked entries in robots.txt across Cloudflare's network, with GPTBot, CCBot and ClaudeBot at the top of the list. A proxy does not create a licence and does not change what a site's terms permit. What it changes is the network path: US residential ISP addressing behaves like ordinary consumer traffic where a datacenter range is pre-scored before your crawler reads a byte.

Can I provision and replace IPs programmatically?

Yes. The management API at /api/v2/order takes a Bearer token and covers creating, listing, updating and cancelling orders, pulling usage, and exchanging or replacing individual IPs. That is the piece that matters mid-crawl: you can add capacity when a corpus run grows and swap a single burned address without pausing the pipeline. Note this is the account and order API. The proxy connection itself is always username and password, never a token or an IP whitelist.

Is collecting public web data for AI training allowed?

That is a question for your counsel, not for a proxy vendor, and the answer turns on the specific sources you collect. What we can say is what responsible teams actually do: stay on publicly accessible, non-personal content, read and honour the terms of the sites you depend on, keep robots.txt handling deliberate rather than accidental, pace requests so you are not the reason a small site falls over, and keep crawl logs so provenance is auditable later. Several large platforms specifically prohibit scraping for AI training in their terms, and a proxy does not change that.