Exploring our IPinfo IP to Residential proxy data

We launched our IP to residential proxy data at the beginning of this year. Although it is a new database, we have been working on it internally for the entire last year. We have gained a ton of insights into residential proxies, how they operate, and interesting patterns.

I will take this opportunity to explore the database and gather customer feedback on it.

Delivery methods

The residential proxy detection database can be downloaded in the following formats:

  • IP data downloads
    • CSV
    • JSON (ND JSON)
    • MMDB
    • Parquet
  • Platform delivery
    • Snowflake
    • GCP / BigQuery
  • Data push to storage buckets like AWS S3, GCP GCS, Azure Storage Blob, etc.

Most of the queries run here will use our data on Snowflake Marketplace. You can make a request to obtain the data here: Snowflake

Documentation and Schema

The documentation for the Residential proxy database is available here: Documentation for IP to Residential Proxy Database - IPinfo.io

Database schema:

Field Name Example Data Type Description
ip 38.222.31.85 TEXT IPv4 or IPv6 address associated with a residential proxy.
service lightningproxies TEXT Name of the residential proxy service. Carrier/mobile services are suffixed with _mobile (e.g., soax_mobile).
last_seen 2024-09-07 TEXT Last recorded date when the residential proxy IP was active, formatted as YYYY-MM-DD (ISO-8601). Timezone is UTC.
percent_days_seen 2 TEXT Integer indicating the percentage of days the IP was active in the last 90-day period, reflecting its activity and frequency of IP within a residential proxy pool.

Column overview

IP

Unlike most IP address databases, we are using single IP addresses as the index column. We used to have IP ranges, and moved to a CIDR-based database this year. However, for this database, the index column consists of single IPs.

IP SERVICE LAST_SEEN PERCENT_DAYS_SEEN
187.19.XXX.XXX s*******y 2025-01-21 12
2001:ee0:XXXX:XXXX:XXXX:XXXX:XXXX:XXXX y*************y 2024-12-06 2
86.8.XXX.XXX p*****d 2025-02-12 22
2600:1700:XXXX:XXXX:XXXX:XXXX:XXXX:XXXX p*********s 2025-01-15 1
125.163.XXX.XXX h*******y 2025-01-12 5
95.7.XXX.XXX d******y 2024-12-05 1
177.41.XXX.XXX s*******y 2025-02-18 4
73.171.XXX.XXX y*************y 2025-02-18 3
27.23.XXX.XXX p*****d 2024-11-25 1
2602:fe43:XXXX:XXXX:XXXX:XXXX:XXXX:XXXX t*********y 2025-01-25 1

The reason is that residential proxies do not operate in ranges; they operate individually with the own frequency metadata (last_seen and percent_days_seen). Clustering them in CIDRs would result in a large number of /32s and /128s, bloating the database.

Therefore, we opted to use individual IPs instead.

Service

With the residential proxy database, one interesting thing you debated for a while was the service details.

Even though we can detect hundreds (nearly 200) of different proxy service providers, because the proxy pools are shared between many different vendors, resellers, and rebranded organizations, we decided to stick with one prominent service provider per IP address.

Even though we have the data internally where our ‘service’ field name is an array, for simplicity, we return a string with a single provider’s name for our commercial database.

Top residential proxy provider by IP count

SERVICE IPV4_COUNT SERVICE IPV6_COUNT SERVICE TOTAL_COUNT
s*******y 2383233 d******y 674906 s********y 2383282
p*****d 1973789 t*********y 617726 d*******y 2123475
l*********s 1639183 p********e 582494 p******d 1975587
d******y 1448569 p*******o 537826 l**********s 1639246
o****s 1251672 o*******y 511711 t**********y 1357831
p****y 1195433 l******y 487886 p*****y 1308598
s*********s 942863 t***********y 449923 o*****s 1251702
b*******a 823159 s********e 447886 p*********e 1109730
y*************y 773270 k******y 355613 s**********s 1041452
t*********y 740105 p*********s 320813 p********o 932769

Another aspect of the service is that we categorize the mobile/carrier residential proxies providers by adding the suffix “_mobile”.

To carrier/mobile proxy provider by IP count

SERVICE COUNT
s**x_mobile 833355
i*************s_mobile 484634
g*****y_mobile 365095
r************s_mobile 322612
d*********e_mobile 232359
r*****g_mobile 223690
p*********p_mobile 223237
p*********e_mobile 218792
o*****s_mobile 115751
d*****y_mobile 94941

Number of residential/ISP and mobile/carrier proxies

Total 38743724
Mobile Proxy Count 3212201
ISP Proxy Count 35531523

last_seen

This is proxy frequency metadata. The last date the residential proxy IP was active was YYYY-MM-DD in the UTC timezone. The 90-day window applies here, which means that the oldest last-seen IP value is always going to be 3 months old.

We have the absolute historic data of IPs we have seen over the last year or so since we started building the product, but for our customer database, the timeframe window is limited to 90 days.

Dataset sorted by last_seen on Feb 20, 2025

IP SERVICE LAST_SEEN PERCENT_DAYS_SEEN
105.161.XXX.XXX i********************e 2024-11-21 1
178.222.XXX.XXX p******d 2024-11-21 1
178.45.XXX.XXX p******d 2024-11-21 1
38.137.XXX.XXX t**********y 2025-02-20 4
38.137.XXX.XXX y**************y 2025-02-20 2
38.137.XXX.XXX l*******y 2025-02-20 3

percent_days_seen

Another proxy frequency metadata. The percent_days_seen column describes, on a percentage basis (1-100), the integer value of how many times an IP address was seen over the last 90 days. The percentage value is returned as an integer rounded.

Due to how residential proxy data is used/churned, the most frequent value for percent_days_seen is 1 within the 90 day timeframe.

Random queries

I was curious about how residential proxies are being used, how they are distributed, and what the most popular ASNs are. However, to keep things simple, I will run these queries on the IPv4 residential proxy IPs.

CREATE TEMP TABLE ip_proxy_residential_ipv4 AS
SELECT *
FROM ip_proxy_residential
WHERE ip LIKE '%.%';

Population of residential proxy countries

Powered by our IP location database.

# COUNTRY COUNT_IP
1 Brazil 4373872
2 United States 3073021
3 Russia 1932029
4 India 1713312
5 Vietnam 1478303
6 Great Britain 902341
7 Morocco 764127
8 South Africa 691660
9 Mexico 654287
10 Turkey 624651

How many residential proxies operate in the largest ASNs

Powered by our ASN database.

# ASN NAME COUNTRY COUNT_IP_ASN RES_IP_COUNT ASN_RES_PERCENT
1 AS4134 CHINANET-BACKBONE CN 103919104 213032 0.205%
2 AS7018 AT&T Services, Inc. US 90563584 325457 0.359%
3 AS7922 Comcast Cable Communications, LLC US 69625600 604119 0.868%
4 AS4837 CHINA UNICOM China169 Backbone CN 57441536 71333 0.124%
5 AS4766 Korea Telecom KR 46453504 49833 0.107%
6 AS701 Verizon Business US 40575744 174185 0.429%
7 AS17676 SoftBank Corp. JP 36434176 18915 0.052%
8 AS3320 Deutsche Telekom AG DE 34050560 131419 0.386%
9 AS4713 NTT Communications Corporation JP 28691456 13611 0.047%
10 AS3356 Level 3 Parent, LLC US 28098304 1763 0.006%

If you have any queries or questions you want me to explore, please let me know. Thanks!

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