Zalando Scraper
Zalando is the largest fashion platform in Europe and it runs on markdowns and size runs, not on shelf prices. Its API is closed to anyone who is not an approved partner. The AI scraper reads the public product page instead, turns it into columns you define, and runs on a schedule.
- zalando.de
- zalando.co.uk
- zalando.fr
- zalando.nl
How to scrape Zalando
Zalando is the largest fashion platform in Europe, live in roughly 25 markets, and it does not behave like an electronics retailer. In fashion the shelf price is almost beside the point: what moves the market is when an article goes on markdown, how deep, in which markets, and which sizes are still available when it does. An article at full price with only size 44 left is not competing with you; the same article at minus forty percent with a full size run is. Zalando's programmatic access is a partner API for approved brands and sellers, gated behind a commercial relationship and scoped to your own assortment. It is not something a competitor analyst signs up for. Scrapewise carries no dedicated Zalando endpoint either. What works is the public product page, which Zalando renders to any shopper. Access is the hard part: we measured it on 30 September 2026 and a plain fetch was refused with a 403, including on the homepage, while a real browser was told the site could not be reached. A run therefore has to go through a rendered page from a residential exit. You paste Zalando URLs into the AI scraper, declare the columns you want, and set a daily schedule. Every run comes back over REST, CSV or Excel, and you pay per page from a prepaid wallet.
What the AI scraper does with a Zalando link
Three steps, same as any other site. Nothing about Zalando is pre-built, which is why it reaches a platform nobody ships an endpoint for.
Paste the link
A product page, a brand page, or a category URL with the filters already applied. Zalando encodes filters, sort order and size selections in the address, so a view you built by hand in the browser is a valid input. Each market is its own domain, so a German and a French view are two links.
You declare the columns
A Zalando job usually wants the current price, the original price, the markdown percentage, the sizes still in stock, the colour variant, the brand and Zalando's own article code. You name those fields yourself in a Custom Schema, or start from the ready Product List schema and add to it.
Schedule it and take the data out
Run it once or daily. Pull each run by REST API or download it as CSV or Excel. The columns stay the same between runs, so a Zalando table lines up market by market and season over season.
Set it up once. The data keeps coming.
Save your search once and it runs by itself. Every run lands in your own Scrapewise database, and you pick how to read it.
Set it up once
Add your ASINs, keywords, places or apps to a scraper in the web app. New accounts get 5 free requests.
Runs on your schedule
Pick daily, weekly on the days you choose, every few days, or the first or last day of the month. Scheduled runs start at night, European time. Need it more often? Start runs from your own code.
Saved in your database
Each run adds dated rows, so this week sits next to last week. Rows are kept for 90 days.
Use it your way
Open the table in the web app and download the latest run as an Excel file. Ask your own AI assistant about it. Or pull the rows into your code with an API key.
Ask your AI assistant about your own data
Connect Claude Desktop or Claude Code with a read-only key. The assistant reads the rows you've already collected, at no extra cost on Scrapewise. With a full-access key it can start a run for you too.
- Which of my ASINs lost the Buy Box this week?
- Which competitor cut prices the most since Monday?
- Show the keywords where I dropped out of the top 10.
What you send and what you get
You send a link and a column list. The markdown depth, the size run and the colour variant come from the page itself.
- Link to a Zalando product, brand or category pagerequired
Navigate to the view you want in the browser, set your filters and sort order, then copy the address out of the bar. Zalando's category slugs are localised per market and change, so copy a real one rather than composing it by hand.
https://www.zalando.de/ (navigate, then copy the URL) - Your column listrequired
Start from the ready Product List schema or name your own fields in a Custom Schema. The AI fills the fields you declared.
Custom Schema, 14 fields - How often it runsoptional
- On demand
- Daily
- Weekly
- Monthly
Daily is the fastest cadence available, and in fashion it is the one that matters: a markdown that lands on Thursday and deepens on Sunday is invisible to a weekly check.
One row per article in the listing, 14 columns each
- brand
- product_title
- article_code
- colour
- price
- original_price
- discount_percent
- currency
- market
- sizes_offered
- sizes_in_stock
- is_sold_out
- +2 more, see all columns
REST API, CSV or Excel. The column set stays the same between runs, so a Zalando table lines up across markets and across seasons.
The columns a Zalando run gives you
We are not going to print a table of invented fashion prices and call it real output. Zalando refuses a plain fetch outright and returned an unreachable-site page to an ordinary browser, so a run has to go through the residential tier. This is the column list you declare before the run, and the column list every run comes back with.
brandBrand as Zalando labels itproduct_titleProduct title as Zalando writes itarticle_codeZalando's own article code for this colour variantcolourColour variant the page is showingpricePrice Zalando is charging todayoriginal_pricePre-markdown price, only where the page shows onediscount_percentMarkdown percentage, as the page states itcurrencyCurrency for this marketmarketWhich Zalando domain the row came fromsizes_offeredEvery size listed for this variantsizes_in_stockSizes the page shows as still availableis_sold_outWhether the variant is out of stock entirelyproduct_urlLink back to the page the row came fromchecked_atWhen this run collected the page (UTC)The columns are yours, not ours. Add, rename or drop any of them in a Custom Schema and every future run returns exactly your set.
What a Zalando page costs
Pay-as-you-go from your wallet. No plan, no monthly fee, no seat count.
Measured on 30 September 2026, not assumed. A plain fetch of a zalando.de URL is refused with a 403, and so is the bare homepage, which rules out a bad path as the explanation. Loading the same URL in a real browser returned a Zalando page saying the site could not be reached right now. A browser alone is not enough here, so budget for a rendered page coming out of a residential exit.
400 Zalando article pages checked once a day for a month is 12,000 pages.
about EUR 45.00 for the month at this tier
You are charged for the tier a run actually used, not the tier it was expected to need, and your balance never expires.
See the full price list →What this page is not promising
We would rather say this here than in a support ticket.
No dedicated Zalando endpoint
There is no Zalando data API in our catalogue and no pre-built Zalando schema. The AI scraper reads the page you point it at, and that is the whole mechanism.
This is the expensive tier
Zalando refuses a plain fetch and a plain browser, so every run goes through a rendered page from a residential exit at EUR 3.75 per 1,000 pages. Fashion catalogues are large, so scope the page count before you switch it on rather than after.
Colour variants are extra pages
Each colourway is its own URL with its own article code. Covering a full range means covering more pages, which changes the page count and the cost.
No partner-account data
Anything behind Zalando's partner login, your sales, your stock, your returns figures, is not reachable by a scraper reading public pages, and the partner API is the right tool for that job.
Daily, not real time
Daily is the fastest schedule. There is no real-time feed, no per-minute polling and no markdown alerting.
No price history, no currency conversion, no SKU matching
We store nothing about Zalando prices on our side, so a markdown curve starts the day you switch the scraper on. We do not convert currencies across markets and we do not map Zalando article codes to your own SKUs. We are also not lawyers, so check Zalando's terms before you run anything.
Typical fields the AI scraper extracts from a Zalando page
A starting point, not a fixed schema. What comes back is whatever the page actually shows on the day of the run.
Markdown, not price
In fashion the interesting number is the distance between the original price and today's price, and how fast that distance is growing.
- Current price
- Original price, where the page shows one
- Markdown percentage as stated
- Campaign or sale badge wording
- Currency and market the price belongs to
Size run and availability
The column fashion teams actually ask for, and the one most price monitoring tools do not collect.
- Every size listed for the variant
- Which sizes are still available
- Which sizes are marked as few left, where the page says so
- Whether the variant is sold out entirely
- Delivery window shown for the article
Article identity
Zalando issues its own article code per colour variant, which is the most reliable thing on the page to key a comparison on.
- Zalando article code
- Brand
- Colour variant
- Category breadcrumb
- Material and care text, where the page prints it
- Rating and review count, where shown
- Product image URLs
The column list is one you write: name each field, give it a type and a line telling the AI what to look for. The schema belongs to your account, so one Zalando field set covers every market you point us at, which is what makes a cross-market markdown table possible at all. How Custom Schema works →
What it cannot give you The scraper cannot invent a field the page does not render. Colour variants are separate URLs on Zalando, so covering a full colourway means covering more pages rather than adding more fields. Size availability is read from what the page shows a logged-out visitor, and a size that is hidden rather than marked out of stock will simply be absent. Historical prices are not on the page and are not stored on our side, so a markdown curve starts the day you switch the scraper on.
What people use Zalando data for
Markdown cadence tracking
The question a fashion pricing team actually has is when competitors start discounting, how deep they go and how fast. That is a curve you build by scraping daily, and Zalando is where the curve is most visible.
Size availability as a competitive signal
An article at minus fifty percent with two sizes left is clearing stock, not attacking you. The same discount with a full size run is. Most monitoring tools collect the price and miss the distinction entirely.
Cross-market price checks
The same article is frequently full price in one Zalando market and marked down in another. One schema across several market domains turns that into a table instead of an anecdote.
Brand protection in fashion
If you sell into Zalando through partners, this is how you see what your articles are actually listed at, per market, per colour. That is the evidence a channel conversation needs.
Assortment and range research
Which brands, colours and price points a category actually carries, rather than what a trend report says it carries. A few weeks of scraped category data answers that before you commit to a buy.
Feeding your own repricing rules
The export is a normal REST API or CSV, so it drops into whatever repricing or markdown logic you already run. Scrapewise supplies the observation; your rules decide what to do with it.
What makes Zalando harder than an ordinary storefront
Access is difficult. The fashion data model is what defeats most price monitoring tools.
Access
- A plain fetch is refused with a 403, including on the homepage, so the cheapest tier is not an option
- A real browser was told the site could not be reached rather than shown the listing
- Runs have to go through a rendered page from a residential exit, which is the most expensive tier
- Success is high but not guaranteed on any individual run
Variants are the whole problem
- Every colour variant is a separate URL with its own article code and its own price
- Size availability changes daily and independently of price, and it decides whether an article is really competing
- A size hidden rather than marked out of stock is simply absent from the page
- Category pages are lazily loaded and infinitely scrolled, so a category sweep is a list of URLs, not one request
Markets and currencies
- Around 25 market domains, each with its own assortment, currency and markdown calendar
- The same article can be full price in one market and heavily marked down in another
- Cross-market comparison needs a currency conversion step you control, not one we guess at
- Zalando Lounge is a separate site with separate pricing and needs its own scraper
Scheduling and delivery
- Daily is the fastest cadence, and in fashion a weekly check misses most of the markdown curve
- Every run is retrievable by REST API, or downloadable as CSV or Excel
- Columns stay stable between runs, so downstream jobs do not need to re-map fields
Zalando scraping — questions
What people ask before pointing the scraper at Zalando.
No. Zalando's programmatic access is a partner API for approved brands and sellers, gated behind a commercial relationship and scoped to your own assortment. It is not something you sign up for to read competitor prices, and there is no public read API for Zalando product data.
Ready to pull Zalando data into your stack?
Start free — or talk to our team about your exact fields, refresh cadence and volume.