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Use Case 3 min read

How Travel Startups Aggregate Fares Across Airlines

Fare comparison engines need to collect prices from dozens of protected airline sites simultaneously. Here's what the infrastructure actually looks like.

traveluse-caseproxygeo-targeting

Travel fare comparison is one of the most technically demanding use cases in web data collection. Airlines and online travel agencies (OTAs) serve different prices based on location, browser, time of day, and request history. Building a reliable fare aggregator means solving all of these challenges at once.

The Technical Challenge

Airline pricing pages are some of the most heavily protected on the web:

  • Aggressive anti-bot systems. Most major airlines use Akamai, DataDome, or PerimeterX.
  • Geographic price variation. A flight from London to New York shows different prices depending on whether you search from the UK, US, or India.
  • Dynamic rendering. Fare results load asynchronously after multiple API calls within the page.
  • Session tracking. Price changes between page loads (the infamous "your searched fare is no longer available").

How a Fare Aggregator Works

Step 1: Search Request

The aggregator receives a search query (origin, destination, dates, passengers) and fans it out to multiple airline and OTA targets.

Step 2: Parallel Data Collection

Each target requires its own approach:

tasks = [
    # Static API endpoint, fast single request
    {"url": "https://api.airline-a.com/fares?from=LHR&to=JFK&date=2026-04-15", "type": "single"},
    # JavaScript-heavy SPA, needs browser rendering
    {"url": "https://airline-b.com/search?o=LHR&d=JFK&dt=20260415", "type": "browser", 
     "options": {"waitFor": ".fare-results"}},
    # Geo-restricted pricing, needs US proxy
    {"url": "https://ota-site.com/flights/LHR-JFK", "type": "proxy",
     "options": {"proxyCountry": "US"}},
]

Step 3: Parse and Normalize

Each site returns data in a different format. The aggregator normalizes everything into a common schema: airline, flight number, departure, arrival, price, currency, cabin class.

Step 4: Deduplicate and Rank

The same flight appears on multiple sites at different prices. The aggregator deduplicates by flight number and presents the cheapest option for each route.

Why Data Collection APIs Matter Here

Without a service like FourA, a travel startup would need to:

  • Maintain a pool of residential proxies across multiple countries
  • Run headless browsers at scale with anti-detection patches
  • Build retry logic for every anti-bot system encountered
  • Handle IP bans and rotate through proxy pools manually

That infrastructure alone can cost more than the rest of the application combined. A data collection API abstracts all of this behind a single endpoint.

Key Considerations

  • Geo-targeting is essential. Airlines serve different prices by region. Use the proxyCountry option to collect prices from the traveler's perspective.
  • Speed matters. Travel searches are time-sensitive. Users expect results in seconds. Use single tasks for API endpoints and browser only when necessary.
  • Compliance is critical. Respect rate limits and terms of service. Some airlines offer affiliate APIs that provide authorized access to fare data.

So Where Do You Start?

If you're building a travel product that needs fare data, the FourA API documentation and the guide to choosing task types cover the technical details.

But the bigger question is architectural. The startups that get fare aggregation right don't just pick the right API. They design their search fanout, caching, and normalization layers around the reality that every airline site behaves differently. The proxy task type with geo-targeting handles the hardest part of that puzzle.