H&M Data API: Extract Structured JSON in 2026

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

Why use H&M data?

You may need H&M product data for several practical purposes. AI models can train on up‑to‑date pricing trends. Analytics pipelines can track inventory changes across regions. Competitive intelligence tools can compare listings without manual copy‑paste. All of these use cases rely on clean, typed data that a data API can deliver.

What data can you extract?

Publicly listed product pages expose a predictable set of fields. Typical extraction targets include:

• title – the product name displayed on the page
• price – the listed price string
• currency – the three‑letter currency code
• sku – the stock‑keeping unit identifier
• availability – in‑stock, out‑of‑stock or limited status
• rating – average customer rating when shown

These fields are consistent across H&M’s storefront layout, making them suitable for downstream processing.

The extraction approach

Scraping raw HTML with libraries like BeautifulSoup works for simple pages but quickly breaks when H&M updates its markup. A data API removes that fragility. It fetches the page, runs the extraction, and returns validated JSON. You no longer need to maintain CSS selectors or handle layout drift. The service also handles IP rotation, CAPTCHA solving and request throttling behind the scenes.

Quick start with AlterLab Extract API

Getting started requires only a few steps. Install the SDK, define a schema and call the extract endpoint. Full installation instructions are in the Getting started guide.

import alterlab

client = alterlab.Client("YOUR_API_KEY")

schema = {
  "type": "object",
  "properties": {
    "title": {"type": "string", "description": "The title field"},
    "price": {"type": "string", "description": "The price field"},
    "currency": {"type": "string", "description": "The currency field"},
    "sku": {"type": "string", "description": "The sku field"},
    "availability": {"type": "string", "description": "The availability field"},
    "rating": {"type": "string", "description": "The rating field"}
  }
}

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

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

The endpoint returns an estimated cost before execution. Cost is clamped to a minimum of $0.001 and a maximum of $0.50. Pricing details are on the AlterLab pricing page. When a BYOK key is registered, the orchestration fee is 300 µ¢; otherwise the platform rate of 1000 µ¢ applies.

Define your schema

The schema parameter tells the API which fields to return and how to type them. AlterLab validates the output against the schema and rejects malformed responses. This guarantees that downstream code can safely access result.data["price"] without additional parsing.

Handle pagination and scale

High‑volume pipelines often need to process many product URLs. Use batch requests to send multiple URLs in a single POST. For asynchronous workflows, enqueue jobs and poll the status endpoint until completion. This pattern scales without hitting rate limits and keeps your balance predictable.

Batch and async usage example

import alterlab
import asyncio

client = alterlab.Client("YOUR_API_KEY")

urls = [
    "https://hm.com/product/1",
    "https://hm.com/product/2",
    "https://hm.com/product/3",
]

async def extract_one(url):
    schema = {
      "type": "object",
      "properties": {
        "title": {"type": "string"},
        "price": {"type": "string"},
        "currency": {"type": "string"},
        "sku": {"type": "string"},
        "availability": {"type": "string"}
      }
    }
    resp = client.extract(url=url, schema=schema)
    return resp.data

async def main():
    results = await asyncio.gather(*[extract_one(u) for u in urls])
    print(results)

asyncio.run(main())

Key takeaways

• Public product data on H&M can be retrieved as typed