Residential proxies
RecommendedRotating household exits per point-of-sale country return the local fare and pass booking-engine defences at volume.
Travel fare aggregation · Residential recommended
Airlines and OTAs price by point of sale and defend their booking engines harder than almost any other industry: hosting ranges are blocked, repeated searches from one address are throttled, and some carriers quote higher fares to traffic they distrust. Accurate aggregation needs a fresh local visitor for every search.
01 — Which network
A recommendation is only useful with its trade-offs. The cards below say what each network does well for travel fare aggregation, and where it stops being worth the money.
Rotating household exits per point-of-sale country return the local fare and pass booking-engine defences at volume.
App-only fares and mobile promotions exist; carrier exits reveal them.
A few static IPs per market for low-volume monitoring of specific routes.
Blocked or served inflated fares by most booking engines.
02 — The recipe
The settings that matter for travel fare aggregation, and a working example on the right. Replace USER and PASS with the credentials issued at signup.
// Search the same route from three points of sale
for (const pos of ["gb", "de", "jp"]) {
const dispatcher = new ProxyAgent(`http://USER-cc-${pos}:[email protected]:9000`);
const res = await fetch(searchUrl(route, date), { dispatcher, headers: { "Accept-Language": lang[pos] } });
fares.push({ pos, ...(await res.json()) });
}
03 — Budget
Per-GB pricing is predictable once you know your page size. Enter your monthly volume; the average size is pre-filled with a realistic figure for this job and can be changed.
average results page (HTML/JSON, heavy engines)
Residential rate ladder, identical in every country. Buying the month's traffic in one purchase reaches the tier shown; smaller top-ups are priced at their own tier. Full ladder.
04 — Best practices
The pool is rarely the problem. These four habits decide the success rate and the bill.
A GB fare comes from a GB household; label every result with it.
Aggregation traffic should never reach payment; keep flows read-only.
Booking engines ship megabytes of scripts; fetch the fare endpoint, not the whole app, whenever possible.
Compare against a known-good sample; distrusted traffic sometimes gets inflated prices rather than a block.
05 — Questions
Point of sale, currency and trust level all change the quote. Residential exits in the target country return the fare that country is offered.
Results pages are heavy, often 0.5 to 2 MB. One hundred fifty thousand searches at 900 KB is about 135 GB, around $208 on the residential ladder; fetching fare endpoints directly can cut that by half.
Yes: add -sid- and -ttl-10m so results, fare rules and seat maps come from one household.
Collecting publicly displayed fares is a common, legitimate use. Automated booking, inventory hoarding or bypassing access controls is not.
Ready when you are
Create your account, top up $10 and try the recipe above on your real target. What you do not use stays on your balance.