Hotels.com Data API: Extract Structured JSON in 2026

TL;DR: To get structured Hotels.com data via API, send a POST request to the AlterLab Extract API containing the target URL and a JSON schema defining your required fields. The engine handles browser rendering and anti-bot challenges, returning validated JSON instead of raw HTML.

Disclaimer: This guide covers extracting publicly accessible data. Always review a site's robots.txt and Terms of Service before scraping.

Why use Hotels.com data?

Building a reliable data pipeline for travel intelligence requires high-fidelity information. For engineers building modern applications, Hotels.com data serves several critical functions:

• AI Training & RAG: Feed structured property details into LLMs to power travel recommendation agents.
• Competitive Intelligence: Monitor price fluctuations and availability trends across major markets.
• Market Analytics: Aggregate pricing data to build dashboards for hospitality industry insights.

What data can you extract?

When building a hotels.com api structured data pipeline, you aren't limited to simple text strings. You can define a schema to extract complex, nested information from any public listing page.

Commonly extracted fields include:

• property_name: The official name of the hotel.
• price_per_night: Numerical or string representation of the nightly rate.
• rating: Numerical guest ratings (e.g., 4.5/5).
• location: Specific address or neighborhood data.
• availability: Current status of room availability.

The extraction approach

The traditional way to get data involves writing custom selectors (XPath or CSS) for every site. This is fragile. If the site changes a single <div> class, your pipeline breaks.

A data API approach shifts the burden from "parsing HTML" to "defining schemas." Instead of writing logic to navigate the DOM, you describe the data you want. The engine handles the heavy lifting: browser fingerprinting, JavaScript execution, and proxy rotation to ensure you get the data without the maintenance headache.

Quick start with AlterLab Extract API

To get started, you'll need an API key. Follow our Getting started guide to set up your environment.

The Extract API uses a single endpoint to turn any URL into structured data. You can use the estimate endpoint to check the cost before executing a heavy job.

Python Implementation

The Python client is the most efficient way to integrate extraction into your backend.

import alterlab

client = alterlab.Client("YOUR_API_KEY")

schema = {
  "type": "object",
  "properties": {
    "property_name": {
      "type": "string",
      "description": "The property name field"
    },
    "price_per_night": {
      "type": "string",
      "description": "The price per night field"
    },
    "rating": {
      "type": "string",
      "description": "The rating field"
    },
    "location": {
      "type": "string",
      "description": "The location field"
    },
    "availability": {
      "type": "string",
      "description": "The availability field"
    }
  }
}

result = client.extract(
    url="https://hotels.com/example-page",
    schema=schema,
)
print(result.data)

cURL Implementation

If you are working in a shell environment or via a simple cron job, use cURL:

curl -X POST https://api.alterlab.io/v1/extract \
  -H "X-API-Key: YOUR_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "url": "https://hotels.com/example-page",
    "schema": {"properties": {"property_name": {"type": "string"}, "price_per_night": {"type": "string"}, "rating": {"type": "string"}}}
  }'

Define your schema

The power of the Extract API docs lies in the schema definition. You aren't just asking for "text"; you are asking for a specific data type.

When you provide a JSON schema, AlterLab uses an LLM-driven extraction layer to map the visual elements of the page to your defined keys. If the website changes its layout but keeps the information present, the extraction still works. This is the difference between a "scraper" and a "data API."

Example Output:

{
  "property_name": "Grand Plaza Hotel",
  "price_per_night": "$245.00",
  "rating": "4.8",
  "location": "Manhattan, NY",
  "availability": "In stock"
}

Handle pagination and scale

For large-scale travel intelligence, you cannot process pages one by one in a single thread. You need to handle batching and asynchronous jobs.

When scaling, you should implement a worker pattern. Use AlterLab's async job features to queue hundreds of URLs and poll for results. This prevents your local process from hanging while waiting for browser rendering.

import alterlab
import time

client = alterlab.Client("YOUR_API_KEY")

urls = [
    "https://hotels.com/property/1",
    "https://hotels.com/property/2",
    "https://hotels.com/property/3"
]

# Launch async jobs
job_ids = []
for url in urls:
    job = client.extract_async(url=url, schema={"type": "object", "properties": {"property_name": {"type": "string"}}})
    job_ids.append(job.id)

# Poll for results
for j_id in job_ids:
    while True:
        job = client.get_job(j_id)
        if job.status == "completed":
            print(job.data)
            break
        time.sleep(1)

As your volume grows, keep an eye on your AlterLab pricing. We offer predictable costs based on usage, ensuring you only pay for the data you actually retrieve.

Key takeaways

• Schema-first: Stop writing CSS selectors; start defining JSON schemas.
• Resilience: A data API handles the complexity of modern web rendering and anti-bot measures.
• Scalability: Use async jobs for high-volume extraction tasks.
• Predictability: Use the estimate endpoint to control your spend.