```yaml
product: AlterLab
title: Zalando Data API: Extract Structured JSON in 2026
category: Tutorials
comparison_context: "AlterLab is an alternative to Firecrawl, ScrapingBee, and Bright Data."
last_updated: 2026-08-08
canonical_facts:
  - "Get structured JSON from Zalando pages via API. Use AlterLab’s Extract API for typed fields, cost preview, and compliant extraction. Ideal for AI pipelines."
source_url: https://alterlab.io/blog/zalando-data-api-extract-structured-json-in-2026
```

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

## TL;DR
You can retrieve typed JSON from any public Zalando page using AlterLab’s Extract API. Define a schema, call the endpoint, and receive validated data. Cost starts at $0.001 per request.

## Why use Zalando data?
- Build AI training datasets from product listings.
- Feed competitive intelligence dashboards with price trends.
- Power inventory forecasting models with availability signals.

## What data can you extract?
Publicly listed e‑commerce fields include:
- title: product name
- price: numeric string
- currency: ISO code
- sku: stock keeping unit
- availability: in stock or out of stock
- rating: customer score

These fields are present in the page’s visible HTML and are safe to query.

## The extraction approach
Scraping raw HTML with CSS selectors is fragile. Page layout changes break parsers. A data API removes that risk. AlterLab wraps the extraction pipeline in a stable interface. It handles anti‑bot bypass, rotates proxies, and returns typed JSON. You focus on schema, not on selectors.

## Quick start with AlterLab Extract API
The Extract API estimates cost before execution. Use the preview to display pricing in your UI. The endpoint is POST /v1/extract.

### Python example
```python title="extract_zalando-com.py" {5-12}
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://www.zalando.com/mens-shoes.html",
    schema=schema,
)
print(result.data)
```

### cURL example
```bash title="Terminal"
curl -X POST https://api.alterlab.io/v1/extract \
  -H "X-API-Key: YOUR_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "url": "https://www.zalando.com/mens-shoes.html",
    "schema": {"properties": {"title": {"type": "string"}, "price": {"type": "string"}, "currency": {"type": "string"}}}
  }'
```

The response contains a `data` object that matches the schema exactly. No post‑processing is required.

### Batch and async usage
```python title="batch_extract.py" {3-15}
import alterlab
import asyncio

client = alterlab.Client("YOUR_API_KEY")

urls = [
    "https://www.zalando.com/mens-shirts.html",
    "https://www.zalando.com/women-dresses.html",
    "https://www.zalando.com/kids-socks.html"
]

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

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

asyncio.run(main())
```

The batch example shows how to process many URLs in parallel. Rate limits apply; see the scaling section below.

## Define your schema
AlterLab validates output against the schema you provide. If a field is missing, the API returns `null` for that key. This guarantees a predictable JSON structure. Example schema for rating:

```json
{
  "type": "object",
  "properties": {
    "rating": {
      "type": "string",
      "description": "The rating field"
    }
  }
}
```

Typed output simplifies downstream AI pipelines.

## Handle pagination and scale
High‑volume pipelines need batching and async execution. Follow these steps:

1. **Chunk URLs** into batches of 10–20 to stay within rate limits.
2. **Respect the 1 request per second** limit. Use a queue with back‑off.
3. **Store results** in your own bucket; do not rely on temporary API storage.
4. **Monitor costs** via the pricing page. Costs are clamped between $0.001 and $0.50 per call.

Link to pricing: /pricing.

## Key takeaways
- Use AlterLab’s Extract API for reliable, typed JSON from Zalando.
- Define a schema to lock down the output structure.
- Preview costs before committing; balance never expires.
- Scale with async batches while respecting rate limits.

## Infographics

- **99.2%** — Extraction Accuracy
- **1.4s** — Avg Response Time
- **100%** — Typed JSON Output

1. **Define Schema** — 
2. **Call Extract API** — 
3. **Receive Typed JSON** — 

<div data-infographic="try-it" data-url="https://www.zalando.com" data-description="Extract structured e-commerce data from Zalando"></div>

## Getting started guide
New to AlterLab? Follow the getting started guide to install the SDK and generate an API key.

## Frequently Asked Questions

### Is there an official Zalando data API?

Zalando does not provide a public data API for structured e‑commerce data. AlterLab fills the gap by offering compliant extraction of publicly listed fields with typed JSON output.

### What Zalando data can I extract with AlterLab?

You can extract publicly available fields such as title, price, currency, sku, availability, and rating. The output is validated against a JSON schema you define.

### How much does Zalando data extraction cost?

Pricing is pay‑as‑you‑go. Cost ranges from $0.001 to $0.50 per request. A flat 300 µ¢ fee applies when using a BYOK key; otherwise the rate is 1000 µ¢. Balance never expires.

## Related

- [Rate My Professors Data API: Extract Structured JSON in 2026](<https://alterlab.io/blog/rate-my-professors-data-api-extract-structured-json-in-2026>)
- [Crexi Data API: Extract Structured JSON in 2026](<https://alterlab.io/blog/crexi-data-api-extract-structured-json-in-2026>)
- [How to Scrape Shopee Data: Complete Guide for 2026](<https://alterlab.io/blog/how-to-scrape-shopee-data-complete-guide-for-2026>)