Data cluster

Thailand Flight Price APIs.

Fare, availability, NDC, GDS, and booking layers used to compare routes, cabins, ancillaries, and live airline offers.

4 sources Updated 2026-05-11 Download JSON

Best use for travel intelligence

For travel decisions, fare APIs matter when a route exists on paper but the real question is what is for sale now, under which bag and cabin rules, and whether the quote can still be booked.

What this changes for travelers

  • Fare-shopping APIs: Whether a route is actually bookable and what it costs today.
  • Ancillary-aware retail APIs: What baggage, seat, and branded-fare rules really attach to the offer.
  • Offer-expiry logic: Why a page should never pretend an old quote is still valid.
  • Route comparison: Useful when airport choice, fare family, and bag rules change the real cheapest option.

Best sources to start with

  • Best shopping and availability source: Amadeus Flight APIs for flight offers and route-search workflows.
  • Best live metasearch-style source: Skyscanner Flights Live Prices API for current price discovery and itinerary retrieval.
  • Best modern airline-retail source: Duffel Flights API for offers, ancillaries, seats, baggage, and order management.
  • Best agency or GDS source: Sabre Flight APIs for flight search, refresh, offer, order, and servicing workflows.
  • Best developer starting point: Store offer expiry, baggage rules, ancillaries, and shopping context separately from schedule or airport-board data.
Official / agency sources

2 of 4 sources look official or agency-backed.

API/feed candidates

3 sources expose API, feed, CKAN/DataStore, JSON, XML, or similar machine-readable access.

Near-real-time signals

0 sources have live, hourly, event-driven, warning, or frequent update language.

Developer note

Treat schedules and prices as separate systems, reprice before booking, and store offer expiry, baggage, ancillaries, and shopping context with every fare result.

Last checked and source confidence

Last checked: 2026-05-11.

Source confidence: This cluster is strongest when several sources describe the same traveler problem from different angles: official context, machine-readable feeds, and slower fallback documentation.