AI and machine learning · Research
Public web data for AI
Model teams need breadth, freshness and a record of where each page came from. The proxy layer should be the least interesting part of that pipeline.
The problem
- Volume is high and targets are many, so per-site tuning does not scale.
- Legal and data-governance teams ask where data came from and under what terms, and expect an answer per source.
- Bandwidth, not request count, dominates cost when pages are heavy.
What to measure
- Success rate per source domain, so one weak domain does not hide behind a good average.
- Bytes per successful page, to decide whether blocking images and scripts pays for itself.
- Share of fetches that honoured the source's robots.txt, if your policy requires it.
The measurement method and the cost tool turn these into numbers you can compare.
Recommended products
Web Scraper API
Per-request cost makes a crawl budget a known number, and retries stay on our side of the meter.
Residential proxies
For crawlers you run yourself across many different domains, where one address per site avoids concentrated rate limiting.
Datacenter proxies
For sources that publish permissive access and rate limits, the cheapest route is the right one.
Be careful about
- Public does not mean free of terms. Record the licence or terms of each source and keep them with the data.
- Personal data in public pages is still personal data under GDPR. Decide your handling before you collect, not after.
Your use must comply with the acceptable use policy. This page is operational guidance, not legal advice.
Questions
Do you provide datasets?
No. ProxyLabs sells access infrastructure: proxies and request-based APIs. You collect, and you own the pipeline and the compliance decisions that go with it.
Can you vouch for the legality of a collection job?
No one can do that in general. Our acceptable-use policy and onboarding questions screen out clearly prohibited use, and your counsel decides the rest.
Test it on your own targets.
Create an account, run the measurement harness against your real workload, and read the numbers before you commit to anything.