Zalando Scraper - Products, Prices & Discounts

Zalando Scraper — Products, Prices, Discounts, Sizes & Availability 👗👟

Extract comprehensive product catalog data, live pricing, markdown percentages, and real-time size availability from Zalando, Europe's leading online fashion and lifestyle marketplace. This high-efficiency Zalando scraper traverses brand showcases, category trees, and keyword search results across all European domains (.de, .co.uk, .fr, .it, .es, .nl, etc.) without headless browsers or expensive infrastructure. No login required, no rate limit blocks, and 1,000+ deep pagination unlocked.

Export structured e-commerce datasets directly to JSON, CSV, Excel, or consume via the Apify REST API.

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⚡ At a glance

Feature	Description
🎯 Target Platform	Zalando European fashion e-commerce networks
🌍 Supported Domains	.de, .at, .ch, .co.uk, .ie, .fr, .be, .it, .es, .nl, .pl, etc.
🚀 Data Yield	SKU, product name, brand, color, category, department, live price, original price, discount %, sizes
📏 Size Inventory	Full available sizes array (availableSizes) and size count (availableSizesCount)
🔄 Pagination Depth	Continuous catalog traversal via ?p=N (unlocked for 1,000+ products per listing)
⚡ Performance	Ultra-fast HTTP hydration parsing with fast prefill execution (< 60s for standard batches)
🏷️ Deal Filtering	Native flags for discounted-only items (onlyDiscounted) and minimum markdown percentage
📦 Export Formats	Clean JSON, CSV, TSV, XML, Excel (XLSX), or automated webhook streams
💼 Primary Use Cases	Price monitoring, competitor benchmarking, dynamic repricing, MAP tracking, fashion trend analysis

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🔎 Why scrape Zalando?

Zalando is Europe's digital fashion benchmark, featuring hundreds of thousands of active apparel, footwear, accessories, and designer collections from global titans (Nike, Adidas, Levi's, Ralph Lauren) alongside exclusive private labels.

Pricing, clearance markdowns, and size inventories on Zalando shift dynamically based on regional demand, promotional campaigns, and stock replenishment cycles:

• Fashion retailers and marketplace sellers must track competitor price fluctuations and discount depth in real time.
• Brands monitor Authorized Reseller and Minimum Advertised Price (MAP) compliance across European borders.
• Dropshippers and arbitrage resellers identify discounted inventory and in-stock size distributions.

Manually collecting product data across dozens of international Zalando domains is impossible at scale. This Zalando data extractor reads server-rendered catalog listings directly, parsing structured product models, stock sizes, and pricing attributes into standardized records ready for analytics or automated repricing workflows.

💼 Primary business applications

• 🏷️ Dynamic pricing & competitor monitoring: Track rival brand listings, promotional discount depths, and clearance campaigns to adjust retail pricing dynamically.
• 📦 Stock availability & size intelligence: Monitor in-stock shoe and clothing sizes over time to detect stockouts or fast-moving sizes.
• 🛡️ Brand protection & MAP compliance: Audit whether third-party sellers or regional distributors violate suggested retail price boundaries.
• 👗 Fashion trend & assortment research: Aggregate color distributions, silhouette classifications, and brand representations across European markets.
• 🛍️ Dropshipping & deal aggregation: Power automated deal-alert channels, affiliate portals, or price comparison search engines.
• 📊 Catalog feed enrichment: Ingest high-resolution product imagery, structured SKUs, and standardized category tags into internal PIM systems.

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✨ Key features

• 🌐 Multi-country domain support: Seamlessly extract listings from Germany, the UK, France, Italy, Spain, the Netherlands, Poland, and more with matching local currencies (EUR, GBP, PLN, CHF).
• 🔍 Flexible input targeting: Provide direct category, brand, or filtered catalog URLs, or simply enter a search keyword with country and gender options.
• 💰 Comprehensive pricing breakdown: Captures live selling price (currentPrice), list price (originalPrice), discount percentage (discountPercent), and savings amount (discountAmount).
• 👟 Detailed size stock extraction: Parses every in-stock size (e.g., ["38", "39", "40.5", "42"] or ["S", "M", "L", "XL"]) to verify real inventory availability.
• 🎨 Visual & style attributes: Extracts dominant color tags, silhouette categories (e.g., SNEAKER, DRESS, JACKET), and department targets (MEN, WOMEN, KIDS).
• 🏷️ Built-in sale filters: Restrict extractions to discounted products or enforce minimum markdown thresholds (e.g. 30%+ off).
• 🔄 Deep 1,000+ pagination: Automatically traverses multi-page catalogs to collect thousands of unique items per listing.
• ⚡ Lightweight pure-HTTP engine: Highly optimized HTTP extraction eliminates browser overhead, lowering computing costs and accelerating throughput.

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🌍 Country portals & currency reference

Zalando maintains localized storefronts across Europe. When using keyword searches or direct URLs, the scraper automatically detects and normalizes the target country context:

Portal Code	Country	Domain	Currency	Default Language
de	Germany	www.zalando.de	EUR (€)	German
co.uk	United Kingdom	www.zalando.co.uk	GBP (£)	English
fr	France	www.zalando.fr	EUR (€)	French
it	Italy	www.zalando.it	EUR (€)	Italian
es	Spain	www.zalando.es	EUR (€)	Spanish
nl	Netherlands	www.zalando.nl	EUR (€)	Dutch
pl	Poland	www.zalando.pl	PLN (zł)	Polish
at	Austria	www.zalando.at	EUR (€)	German
ch	Switzerland	www.zalando.ch	CHF (Fr.)	German / French
be	Belgium	www.zalando.be	EUR (€)	Dutch / French
ie	Ireland	www.zalando.ie	EUR (€)	English

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🎯 In-depth use cases & industry workflows

1. Fashion brand price monitoring & MAP compliance

Global apparel and footwear brands distribute across multiple European Zalando portals. Market managers utilize this actor to monitor price integrity:

• Enforce MAP policies: Verify whether European wholesale distributors discount seasonal collections prematurely.
• Currency arbitrage detection: Compare UK sterling (GBP) price conversions against continental Euro (EUR) retail prices.
• Seasonal clearance tracking: Track when end-of-season clearance markdowns commence across European markets.

2. E-commerce competitor intelligence & assortment benchmarking

Retailers competing with Zalando analyze catalog breadth and product density within specific niches (e.g. sustainable denim, outdoor trail footwear):

• Benchmark brand representation: Quantify how many SKUs each brand maintains in a given category.
• Measure markdown velocity: Track how quickly newly launched styles transition to discounted status.
• Monitor size curves: Analyze whether standard sizes (M, L / 41, 42) sell out faster in specific territories.

3. Automated fashion deal curation & affiliate engines

Publishers and deal curation platforms scan Zalando for significant discounts on premium apparel:

• Filter high-discount items: Configure onlyDiscounted: true and minDiscountPercent: 50 to uncover flash clearance bargains.
• Stream data to webhooks: Feed discounted listings directly into Telegram, Discord, or newsletter automation pipelines.
• Verify inventory before posting: Check availableSizesCount to guarantee featured items have actual stock available for readers.

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4. Assortment gap analysis & seasonal benchmarking

Fashion retail merchandising teams identify catalog voids and white-space opportunities:

• Category mix comparison: Measure the proportion of sneakers versus casual shoes across competing fashion platforms.
• Color palette trends: Monitor the shift in seasonal color trends (e.g. pastels in spring vs earth tones in autumn).
• Sustainable product share: Analyze what percentage of items carry the "Sustainable" or "Eco" flag over time.

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📥 Input parameters

The scraper accepts flexible inputs including URLs and keyword searches. All fields are optional with intelligent defaults.

Field	Type	Description	Default / Prefill
startUrls	array	Zalando catalog URLs (category, brand, search, or filtered). Any country domain (.de, .co.uk, .fr, .it, etc.).	Prefilled with sneaker category
search	string	Optional search keyword (e.g. nike air max) combined with country and gender.	""
country	string	Target country portal for keyword searches (de, co.uk, fr, it, es, nl, pl, at, ch).	"de"
gender	string	Target department for keyword searches (women, men, kids).	"women"
onlyDiscounted	boolean	Set true to collect only products currently on sale.	false
minDiscountPercent	integer	Keep only products discounted by at least this percentage (e.g. 30 for 30%+ off). Set 0 to disable.	0
maxItems	integer	Maximum products to collect per URL / search. Set to 0 for unlimited catalog extraction (supports 1,000+ products).	0 (prefill: 50)
proxyConfiguration	object	Proxy settings. Defaults to Apify Proxy for high-reliability IP rotation.	{"useApifyProxy": true}

Example input configuration

{
  "startUrls": [
    { "url": "https://www.zalando.de/herrenschuhe-sneaker/" }
  ],
  "search": "",
  "country": "de",
  "gender": "men",
  "onlyDiscounted": false,
  "minDiscountPercent": 0,
  "maxItems": 0,
  "proxyConfiguration": {
    "useApifyProxy": true
  }
}

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🚀 Step-by-step extraction guide

1. Find your target catalog: Open Zalando in your browser and navigate to any category, brand, or search results page.
2. Apply your desired filters: Narrow your search by size, color, brand, or price range directly on Zalando.
3. Copy the filtered URL: Copy the full URL from your browser address bar.
4. Configure the actor:
   • Paste the URL into startUrls.
   • Set maxItems to your target volume (or 0 for complete catalog ingestion).
   • Optionally toggle onlyDiscounted if tracking sale events.
5. Start the extraction: Click Start in the Apify Console.
6. Export your dataset: Download structured data in CSV, Excel, or JSON format.

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📤 Output dataset & field coverage

Each extracted product produces a structured item record containing product identity, pricing, markdown analytics, stock sizes, and imagery.

🛍️ Product identity & classification

Field	Type	Coverage	Description
sku	String	🟢 High	Unique Zalando product SKU identifier
name	String	🟢 High	Full commercial product name
brand	String	🟢 High	Brand or manufacturer label
color	String	🟢 High	Product colorway extracted from product title
category	String	🟢 High	Silhouette or product type classification (e.g., SNEAKER, BOOT)
targetGroup	String	🟢 High	Target demographic department (MEN, WOMEN, KIDS)
url	String	🟢 High	Canonical product page URL on Zalando
flags	Array	🟡 Conditional	Badges and tags (e.g., ["Sustainable", "Exclusive", "Sale"])
image	String	🟢 High	Clean high-resolution product image URL
scrapedAt	String	🟢 High	ISO 8601 extraction timestamp

💰 Pricing, discounts & size inventory

Field	Type	Coverage	Description
currency	String	🟢 High	Currency code (EUR, GBP, PLN, CHF)
currentPrice	Float	🟢 High	Active live selling price
originalPrice	Float	🟢 High	Original manufacturer retail list price
onSale	Boolean	🟢 High	true if currentPrice is discounted below originalPrice
discountPercent	Integer	🟢 High	Markdown percentage (0 to 90+)
discountAmount	Float	🟢 High	Absolute monetary discount amount
availableSizes	Array	🟢 High	Array of all currently in-stock size strings
availableSizesCount	Integer	🟢 High	Total count of unique available sizes

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📋 Sample output record

{
  "sku": "AD112B10B-A11",
  "name": "Adidas Originals - SAMBA OG - Trainers - cloud white/core black",
  "brand": "adidas Originals",
  "color": "cloud white/core black",
  "category": "SNEAKER",
  "targetGroup": "MEN",
  "url": "https://www.zalando.de/adidas-originals-samba-og-sneaker-cloud-whitecore-black-ad112b10b-a11.html",
  "currency": "EUR",
  "currentPrice": 89.95,
  "originalPrice": 119.95,
  "onSale": true,
  "discountPercent": 25,
  "discountAmount": 30.00,
  "availableSizes": ["39 1/3", "40", "41 1/3", "42", "43 1/3", "44", "45 1/3"],
  "availableSizesCount": 7,
  "flags": ["Sustainable", "Sale"],
  "image": "https://img01.ztat.net/article/spp-media-p1/ad112b10b-a11/ad112b10b-a11@1.1.jpg",
  "scrapedAt": "2026-11-10T14:22:15.890Z"
}

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💻 Developer integration & API examples

Trigger runs and retrieve catalog datasets programmatically using Python, Node.js, or cURL.

Python integration

from apify_client import ApifyClient

# Initialize client with your Apify API token
client = ApifyClient("YOUR_APIFY_TOKEN")

# Run the Zalando Scraper actor
run = client.actor("haketa/zalando-scraper").call(
    run_input={
        "startUrls": [{"url": "https://www.zalando.de/herrenschuhe-sneaker/"}],
        "onlyDiscounted": True,
        "minDiscountPercent": 20,
        "maxItems": 100,
    }
)

# Fetch dataset items
products = client.dataset(run["defaultDatasetId"]).list_items().items
for p in products:
    print(f"[{p['brand']}] {p['name']} - {p['currentPrice']} {p['currency']} (-{p['discountPercent']}%) | Sizes: {p['availableSizesCount']}")

Node.js integration

import { ApifyClient } from 'apify-client';

const client = new ApifyClient({ token: 'YOUR_APIFY_TOKEN' });

const run = await client.actor('haketa/zalando-scraper').call({
    startUrls: [{ url: 'https://www.zalando.co.uk/mens-shoes-trainers/' }],
    maxItems: 100,
});

const { items } = await client.dataset(run.defaultDatasetId).listItems();
items.forEach((p) => {
    console.log(`${p.brand} ${p.name}: £${p.currentPrice} (Available sizes: ${p.availableSizes.join(', ')})`);
});

cURL API execution

curl "https://api.apify.com/v2/acts/haketa~zalando-scraper/runs?token=YOUR_APIFY_TOKEN" \
  -X POST \
  -H "Content-Type: application/json" \
  -d '{
    "startUrls": [{"url": "https://www.zalando.de/herrenschuhe-sneaker/"}],
    "maxItems": 50
  }'

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💡 Best practices for optimal results

• Filter directly on Zalando: Use Zalando's native website filters for precise brands, shoe sizes, or price brackets before pasting the URL into startUrls. The actor preserves all URL query parameters.
• Deep catalog ingestion: Set maxItems: 0 to enable the full pagination loop across multi-thousand product listings.
• Fast prefill testing: For quick data structure checks, leave maxItems set to 50 to complete runs in under 30 seconds.
• De-duplication: The scraper de-duplicates products by unique SKU in memory, ensuring that overlapping multi-page listings never produce duplicate rows.
• Deal hunting automation: Combine onlyDiscounted: true with minDiscountPercent: 30 to extract only heavily discounted merchandise.
• Multi-URL batching: You can supply multiple category and brand URLs simultaneously in startUrls. The actor processes each one in sequence while tracking global SKU de-duplication.
• Currency matching: When targeting specific regional currencies, ensure your startUrls or country codes match the target jurisdiction.
• Handling rate limits gracefully: The built-in exponential backoff automatically retries requests if high-velocity queries trigger temporary throttling.

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❓ Frequently Asked Questions

Do I need a Zalando account, login, or API key?

No. The scraper operates without user accounts, developer credentials, or API keys, extracting publicly accessible catalog listings directly.

Can I scrape Zalando stores from other countries?

Yes. The scraper supports all European Zalando storefronts including Germany (.de), United Kingdom (.co.uk), France (.fr), Italy (.it), Spain (.es), Netherlands (.nl), Poland (.pl), Austria (.at), and Switzerland (.ch).

How many products can be extracted per run?

The pagination engine supports 1,000+ products per listing without artificial barriers. Setting maxItems: 0 instructs the engine to paginate until the end of the catalog.

Why are availableSizes sometimes empty for certain items?

Standard apparel and footwear expose full in-stock size arrays. However, one-size merchandise such as accessories, home textiles, and cosmetics do not have size variants on Zalando, so availableSizes remains empty.

Can I run this scraper on a recurring schedule?

Yes. Use Apify's built-in scheduler to trigger recurring daily or weekly extractions, allowing you to build comprehensive historical price tracking databases.

Does this actor download full-size product images?

The actor extracts clean, direct image URLs without query resizing artifacts. You can download or hotlink images directly using the URLs provided in the image field.

How does the scraper avoid duplicate items?

Each product has a globally unique Zalando SKU. The actor tracks discovered SKUs in an in-memory session set, discarding any duplicate entries before pushing data to the dataset.

What export formats are available for Zalando data?

You can download your datasets in JSON, CSV, TSV, XML, or Excel (XLSX) format. You can also connect the Apify API directly to PostgreSQL, Snowflake, BigQuery, or Google Sheets.

Can I monitor specific fashion brands exclusively?

Yes. Navigate to your brand page on Zalando (e.g. https://www.zalando.de/nike/), apply any desired filters, and provide that URL in startUrls. The scraper will restrict extraction exclusively to that brand.

How frequently should I schedule this scraper for price tracking?

For active repricing and dynamic deal tracking, a daily run at off-peak hours is recommended. For macro assortment and seasonal trend research, a weekly or bi-weekly schedule is typically ideal.

Does the scraper work with sale and outlet sections?

Yes. You can supply URLs from Zalando's dedicated Sale or Lounge outlet sections directly. The actor automatically identifies promotional tags, markdown percentages, and crossed-out prices.

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🔗 Related e-commerce scrapers

Expand your retail intelligence pipeline with other high-performance scrapers in the portfolio:

• 🛍️ Tokopedia Product Scraper — Indonesian e-commerce marketplace catalog & pricing.
• 👗 Wildberries Catalog Scraper — Eastern European e-commerce product extraction.
• 📦 AliExpress Product Scraper — Global direct-to-consumer marketplace pricing and reviews.
• 🏷️ Flipp Scraper — Retail circulars, promotional flyers, and grocery discounts.
• 🇲🇾 Lelong.my Scraper — Malaysian consumer electronics and lifestyle products.

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📋 Changelog

Version 1.2 — November 2026

• Unlocked 1,000+ deep pagination: Removed restrictive item limits to enable complete category catalog indexing for large brands.
• Optimized prefill defaults: Lowered prefill to 50 items for rapid platform test runs (< 60s).
• Enhanced size extraction: Expanded media and size extraction logic across all apparel and accessories types.
• Enhanced schema compliance: Validated all schema types and refreshed field coverage documentation.

Version 1.1 — October 2026

• Added keyword search helper (search, country, gender).
• Added color extraction from product titles.
• Added discount filtering flags (onlyDiscounted, minDiscountPercent).

Version 1.0 — August 2026

• Initial release with support for Zalando category and brand catalog extraction, live pricing, and available sizes.