Every IP on the internet carries a story — who owns it, what infrastructure it sits on, and whether it's trying to hide something. This page is a quick field guide to the categories we classify, and the problems this kind of data actually solves — especially now that so much traffic comes from AI agents, not humans.
Every record in our dataset gets evaluated against these dimensions. An IP can belong to more than one — a VPN exit node can also be a datacenter IP, for example.
An address assigned by an ISP directly to a home or mobile connection — Comcast, Vodafone, Jio, etc.
is_residential, ispAssigned to a cloud provider or hosting company — AWS, Hetzner, DigitalOcean, OVH, etc.
isp = Datacenter, asn, org_nameIP ranges published by commercial VPN providers — used to mask a user's real location and identity.
is_vpnThe final hop in the TOR network before traffic reaches the open internet — the IP a destination server actually sees.
is_torApple's iCloud+ feature that routes Safari traffic through two relays so neither Apple nor the destination sees both your identity and your activity.
is_apple_relayFor proxies specifically, we actively test what a destination server sees when traffic passes through.
anonymity_level, protocolA growing share of requests hitting any given server today aren't from a person clicking a mouse — they're from AI agents, browser automations, scrapers feeding LLM training pipelines, and bots impersonating both. Knowing what kind of IP a request comes from is one of the few reliable signals left.
A sample of the problems IP-type and proxy intelligence data gets used for — across security, AI, and growth teams.
Flag requests from datacenter ranges, known VPNs, or proxy exit nodes to separate genuine human visitors from automated agents and scrapers.
Teams building web-scraping pipelines for LLM training data use proxy IPs to distribute requests — this dataset helps both build and detect such pipelines.
VPN and TOR usage, combined with datacenter origin, are classic signals in fraud-scoring models for signups, logins, and payment flows.
Use verified proxies across regions and ASNs to test how your app, ads, or pricing behave from different network vantage points.
Cross-reference an IP of interest against known TOR exits, VPN ranges, and ASN ownership to quickly narrow down what kind of connection you're looking at.
Build and tune WAF / bot-management rules using a live, classified feed instead of static, slowly-updated blocklists.
Every grade is cumulative — each one ships with everything in the tier below it, plus the additional fields listed. Start lean with Sol, or go all the way to Neptune for the fully enriched dataset.
The core signal set — everything you need for basic anonymity and risk scoring.
ipis_proxyproxy_protocolanonymity_levelis_vpnis_torlast_verifiedscoreAdds ownership data for attribution and ASN-level analysis.
is_apple_relayasnorg_nameAdds datacenter/hosting detection to separate bots and servers from real people.
is_datacenterThe fully enriched dataset — adds residential-proxy detection and country-level geolocation.
is_residential_proxycountryFree during early access — get the latest snapshot with full classification fields.
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