#multiotacomparisonapp

[travelscrape]

Travel Scraping API Case Study: App Live in Days | TravelScrape

How a startup built a price-comparison app on the Travel Scrape travel scraping API — from zero to live across 12 OTAs in days, not months.

[travelscrape]

Travel Scraping API Case Study: App Live in Days | TravelScrape

How a startup built a price-comparison app on the Travel Scrape travel scraping API — from zero to live across 12 OTAs in days, not months.

[travelscrape]

Travel Scraping API Case Study: App Live in Days | TravelScrape

Case study summary. A two-person startup built and launched a hotel-and-flight price comparison app on the Travel Scrape travel scraping API — going from zero to live across 12 OTAs in days, with no scrapers to build or maintain. By consuming one normalised API, the founders shipped a working product before they’d have finished a single in-house scraper.

This travel scraping API case study shows how a tiny team punched far above its weight by buying data infrastructure instead of building it. Values are illustrative.

The client: two founders, one big idea, zero data

The client was a two-person travel startup building a consumer price-comparison app for hotels and flights across India and Southeast Asia. They had design and mobile skills but no data engineering and no time to acquire it — a classic early-stage constraint. Their product was only as good as the live travel data behind it, and they had none.

The challenge: a comparison app needs data on day one

A price-comparison app is, fundamentally, a data product. Without live rates from the OTAs travellers use, there is nothing to compare and nothing to launch. Building that data layer in-house meant months of web scraping work the two-person team simply couldn’t take on without abandoning the product itself.

No data engineers to build or run scrapers.

No time — runway favoured shipping, not infrastructure.

Needed many sources — a useful comparison app must cover the OTAs users actually book on.

Why the startup chose the Travel Scrape travel scraping API

The founders chose Travel Scrape’s travel scraping API to get a single, normalised feed of hotel and flight data across 12 OTAs. The deciding factors:

One API, many sources — 12 OTAs through a single normalised endpoint, no per-source code.

Clean JSON — consistent schema meant the mobile app could consume it directly.

Days to integrate — a REST API with simple key auth, live in any language fast.

Zero maintenance — Travel Scrape handled proxies, anti-block and site changes.

The solution: consume one feed, ship the product

Travel Scrape exposed hotel and flight data for the startup’s target markets through its travel scraping API. The team integrated it directly into their app backend.

GET /v1/hotels?city=bali&checkin=2026-08-10&sources=booking,agoda,airbnb

GET /v1/flights?route=DEL-BOM&date=2026-08-15&sources=mmt,skyscanner

// → normalised JSON, same schema across all sources

Sample of a unified comparison response the app rendered directly:

{

  "query": "hotels/bali/2026-08-10",

  "results": [

    { "source": "booking.com", "name": "Ubud Villa", "price": 5200, "currency": "INR" },

    { "source": "agoda",       "name": "Ubud Villa", "price": 4980, "currency": "INR" },

    { "source": "airbnb",      "name": "Ubud Villa", "price": 5100, "currency": "INR" }

  ],

  "cheapest": "agoda"

}

The results: live in days, scaling on data they don’t maintain

Time to Launch

In-House Plan: Several months of development and integration.

With Travel Scrape API: Go live within days.

Outcome: Faster product launch and quicker market entry.

OTA Coverage at Launch

In-House Plan: Typically 1–2 OTA integrations initially.

With Travel Scrape API: Access to 12 OTA sources from day one.

Outcome: Comprehensive travel price comparison from launch.

Maintenance Effort

In-House Plan: Ongoing engineering work for API changes, scraping updates, and monitoring.

With Travel Scrape API: No infrastructure or maintenance burden.

Outcome: Product team remains focused on user experience and growth.

Team Size Required

In-House Plan: Additional 2–3 engineers needed.

With Travel Scrape API: Managed by a small founding team.

Outcome: No additional hiring costs or recruitment delays.

The startup launched a credible, multi-OTA comparison app with a two-person team — something that would have been impossible while also building scrapers. Engineering time went entirely into UX and growth, and the data simply arrived, clean and current, through the travel scraping API.

“We’re two people. There’s no way we could have built scrapers for 12 OTAs and a product. The Travel Scrape API gave us the data on day one — we just built the app.”

— Co-founder, travel-tech startup client

Key takeaways

Small teams can ship big products by buying the data layer.

Normalised API = no integration tax — one schema across all sources.

Maintenance avoided is velocity gained — stay on the product, not the plumbing.

Frequently asked questions

What is a travel scraping API?

A travel scraping API returns live travel data (hotel rates, flight fares, availability) from OTAs as clean JSON. Travel Scrape’s API covers 50+ sources through one normalised endpoint.

How fast can I build an app on a travel scraping API?

Often in days. In this case study a two-person team launched a 12-OTA comparison app without building any scrapers.

Do I need a big team to use it?

No. The whole point is that a small team can ship a data-rich product by consuming the API instead of building scraping infrastructure.

Is the data normalised across OTAs?

Yes. Travel Scrape returns one consistent schema across all sources, so no per-source mapping is needed.

Source : https://www.travelscrape.com/travel-scraping-api-case-study.php

Originally published at https://www.travelscrape.com.