```yaml product: AlterLab title: "How to Scrape H&M Data: Complete Guide for 2026" category: Tutorials comparison_context: "AlterLab is an alternative to Firecrawl, ScrapingBee, and Bright Data." last_updated: 2026-07-21 canonical_facts: - "Learn to scrape H&M product data with Python and Node.js using AlterLab’s API. Covers anti-bot handling, structured extraction, pricing, and responsible scraping practices." source_url: https://alterlab.io/blog/how-to-scrape-h-m-data-complete-guide-for-2026 ``` # How to Scrape H&M Data: Complete Guide for 2026 This guide covers extracting publicly accessible data. Always review a site's robots.txt and Terms of Service before scraping. ## TL;DR To scrape H&M product pages, send a request to AlterLab’s API with the target URL, parse the returned HTML with CSS selectors, and optionally use Cortex for typed JSON output. Start at tier T1 and let the API promote automatically if anti‑bot measures require more advanced handling. ## Why collect e-commerce data from H&M? - **Price monitoring**: Track changes in product pricing across categories to inform competitive pricing strategies. - **Market research**: Analyze product availability, description updates, and new arrivals to spot trends. - **Data analysis**: Build datasets for machine learning models that predict fashion demand or style popularity. ## Technical challenges H&M’s public pages include typical e‑commerce protections: rate limiting per IP, header validation (User‑Agent, Accept), and occasional JavaScript‑based checks that block simple HTTP clients. Raw requests often receive HTTP 429 or challenge pages. AlterLab’s Smart Rendering API manages proxy rotation, header generation, and headless browser fallback, letting you focus on data extraction rather than bypass mechanics. ## Quick start with AlterLab API First, install the SDK and make a basic scrape request. The examples below show Python, Node.js, and cURL. For setup details, see the [Getting started guide](/docs/quickstart/installation). ```python title="scrape_hm-com.py" {3-5} import alterlab client = alterlab.Client("YOUR_API_KEY") response = client.scrape("https://www2.hm.com/en_us/productpage.1234567890.html") print(response.text[:500]) ``` ```javascript title="scrape_hm-com.js" {3-5} import { AlterLab } from "alterlab"; const client = new AlterLab({ apiKey: "YOUR_API_KEY" }); const response = await client.scrape("https://www2.hm.com/en_us/productpage.1234567890.html"); console.log(response.text.slice(0, 500)); ``` ```bash title="Terminal" curl -X POST https://api.alterlab.io/v1/scrape \ -H "X-API-Key: YOUR_KEY" \ -d '{"url": "https://www2.hm.com/en_us/productpage.1234567890.html"}' ``` The response contains the raw HTML of the product page. You can now parse it with libraries like BeautifulSoup (Python) or cheerio (Node.js). ## Extracting structured data Once you have the HTML, target visible elements with CSS selectors. On an H&M product page, common data points include: - Product title: `h1.product-item-headline` - Price: `span.price-value` - Rating: `div.rating-stars` (data‑rating attribute) - Description: `div.pdp-description-inner` ### Python example with BeautifulSoup ```python title="parse_hm-com.py" from bs4 import BeautifulSoup import alterlab client = alterlab.Client("YOUR_API_KEY") html = client.scrape("https://www2.hm.com/en_us/productpage.1234567890.html").text soup = BeautifulSoup(html, "html.parser") title = soup.select_one("h1.product-item-headline").get_text(strip=True) price = soup.select_one("span.price-value").get_text(strip=True) rating_elem = soup.select_one("div.rating-stars") rating = rating_elem["data-rating"] if rating_elem else None description = soup.select_one("div.pdp-description-inner").get_text(strip=True) print({"title": title, "price": price, "rating": rating, "description": description}) ``` ### Node.js example with cheerio ```javascript title="parse_hm-com.js" import { AlterLab } from "alterlab"; import cheerio from "cheerio"; const client = new AlterLab({ apiKey: "YOUR_API_KEY" }); const html = await client.scrape("https://www2.hm.com/en_us/productpage.1234567890.html"); const $ = cheerio.load(html); const title = $("h1.product-item-headline").text().trim(); const price = $("span.price-value").text().trim(); const rating = $("div.rating-stars").attr("data-rating"); const description = $(".pdp-description-inner").text().trim(); console.log({ title, price, rating, description }); ``` ## Structured JSON extraction with Cortex For a more direct approach, use AlterLab’s Cortex extraction API to receive typed JSON without writing selectors. Define a JSON Schema that matches the fields you need. ```python title="extract_hm-com_structured.py" import alterlab client = alterlab.Client("YOUR_API_KEY") result = client.extract( url="https://www2.hm.com/en_us/productpage.1234567890.html", schema={ "type": "object", "properties": { "title": {"type": "string"}, "price": {"type": "number"}, "rating": {"type": "number"}, "description": {"type": "string"} }, "required": ["title", "price"] } ) print(result.data) # Typed JSON output ``` Cortex returns validated JSON, reducing post‑processing work and handling page variations automatically. ## Cost breakdown AlterLab’s pricing is usage‑based. The table below shows the cost per request and per 1,000 requests for each tier. For H&M, start at T1; the API will promote to T2‑T4 if anti‑bot measures require more advanced handling, and you only pay for the tier that succeeds. | Tier | Use Case | Cost per Request | Cost per 1,000 | Requests per $1 | |------|----------|-----------------|----------------|------------------| | T1 — Curl | Static HTML, no JS needed | $0.0002 | $0.20 | 5,000 | | T2 — HTTP | Standard pages with headers | $0.0003 | $0.30 | 3,333 | | T3 — Stealth | Protected pages, anti-bot active | $0.002 | $2.00 | 500 | | T4 — Browser | Full JS rendering required | $0.004 | $4.00 | 250 | | T5 — CAPTCHA | CAPTCHA solving + JS rendering | $0.02 | $20.00 | 50 | See the full [AlterLab pricing](/pricing) page for volume discounts and enterprise options.