Inditex API — Zara + 6 Brands, One Dataset 🛍️

🎉 One Inditex API across Zara, Zara Home, Bershka, Massimo Dutti, Stradivarius, Oysho and Pull&Bear — 58 identical columns per product, 220 country stores, every price in its own currency

The only place on the Apify Store where all seven brands land in one dataset with one schema. Everyone else sells them as seven separate rentals totalling $84.93 a month.

🔎 What is the Inditex API — and when should you use it?

The Inditex Product Scraper turns seven brand catalogues into one clean table. Pick the brands you want, pick a market, and every row comes back with the same 58 columns and a brand column to sort by. No account, no portal API key, no browser automation to maintain.

Use it when you need: cross-brand comparability. Price, old price, discount and currency, colours, images, taxonomy, descriptions, composition, care, and per-SKU sizes with stock and GTIN barcodes — in one schema across Zara, Zara Home, Bershka, Massimo Dutti, Stradivarius, Oysho and Pull&Bear.

Use something else when: you only ever want one brand. The single-brand Actors are cheaper per row — Zara Product Scraper, Zara Home Product Scraper, Bershka Product Scraper, Massimo Dutti Product Scraper, Stradivarius Product Scraper, Oysho Product Scraper and Pull&Bear Product Scraper.

🤖 Use with AI agents

Already connected to the Apify MCP server? Just ask for this Actor by name: sian.agency/inditex-product-scraper

Otherwise copy this prompt into Claude, ChatGPT, Cursor or any MCP-enabled assistant:

I want product data across several Inditex brands using the Apify Actor `sian.agency/inditex-product-scraper`.

Use it when I need: two or more of Zara, Zara Home, Bershka, Massimo Dutti, Stradivarius, Oysho and Pull&Bear in ONE dataset with one schema — price, old price, discount, currency, colours, images, taxonomy, description, composition, care, and per-SKU sizes with stock and GTIN barcodes.

Don't use it when: I only ever want one brand — the single-brand Actors are cheaper per row, e.g. `zara-product-scraper` or `bershka-product-scraper`.

How to call it: `brands` is the list of brands to run, one after another into the same dataset. `mode` is "overview" (walks each brand's category tree) or "detail" (reads product URLs I paste). Set `country` to the market, keep `allCategories` on, and cap the whole run with `maxItems` — it is a run total across all brands, not a per-brand cap.

Start with this input:
{
  "brands": ["zara", "bershka", "stradivarius"],
  "mode": "overview",
  "country": "de",
  "allCategories": true,
  "maxItems": 300
}

Ask me which brands and which market, then run the Actor and summarise the results as a table grouped by brand.

Things you can ask your agent for:

• Compare average price per family across Zara, Bershka and Stradivarius in the Spanish market.
• Pull 200 products from each of the seven brands in Germany and show me who discounts hardest.
• Build me one table of all Inditex homeware and womenswear in France, with GTIN barcodes where they exist.

Machine-readable API, MCP config and OpenAPI definition for this Actor are published at apify.com/sian.agency/inditex-product-scraper.md.

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📋 Overview

Seven catalogues, two very different upstream APIs, one table. Zara and the six sibling brands publish product data in structurally different shapes — different ids, different price encodings, different bundle semantics. This Actor reads both and emits the same 58 columns either way, so a Bershka row and a Zara row sit side by side and mean the same thing.

What you get:

• 🏬 Seven brands, one schema: Zara, Zara Home, Bershka, Massimo Dutti, Stradivarius, Oysho, Pull&Bear, each row stamped with its brand
• 🌍 220 country stores: the union across the seven brands, each priced in its own currency with the right divisor applied. The six shared-catalogue brands cover ~216 of them; zara.com is open in only 96, so check the Market field's note before pairing Zara with a small market
• 📐 Per-SKU sizes and stock: colour by size, with buyable and back-soon flags
• 🏷️ GTIN barcodes: the join key to every other retail dataset, on five of the six shared-catalogue brands — Massimo Dutti publishes none at all
• 🗓️ Promotion windows: per-SKU price start and end dates, so you know the date a markdown ends, not only that one is running
• 🧾 A bill you can audit: the source column on every row says which call produced it, so your invoice reconciles against the dataset
• 💰 $3.50 per 1,000 products on the category sweep, with no subscription. The only other vendor covering all seven brands charges $84.93 a month before you have scraped anything

✨ Features

• 🏬 Pick your brands: run one, three or all seven into a single dataset, sequentially
• 🗂️ Category sweep: walk each brand's whole tree, or name one category
• 🗺️ Sitemap sweep: pick up catalogue items no category lists, for genuine full-catalogue coverage
• 🔗 Paste your own URLs: URLs are routed to the brand their hostname names, so you can mix brands freely
• 🎚️ Facet filters: colour, size, category and discount shortcuts, or any facet group the site publishes
• 💶 Price bounds: keep only what falls between your floor and ceiling, in the market's major units
• 🧹 Deduplicated: the same product reached twice is counted and charged once, per brand
• 📉 Honest run summary: products pushed, duplicates skipped, ids that no longer exist, and fetches that genuinely failed — reported apart, never merged

🎬 Quick Start

Pick your brands, pick a market, cap the run, press Run.

curl -X POST 'https://api.apify.com/v2/acts/sian.agency~inditex-product-scraper/runs?token=YOUR_TOKEN' \
-H 'Content-Type: application/json' \
-d '{
  "brands": ["zara", "bershka", "stradivarius"],
  "mode": "overview",
  "country": "de",
  "allCategories": true,
  "maxItems": 300
}'

🚀 Getting Started (3 Simple Steps)

Step 1: Choose your brands

Brands defaults to all seven. Brands run one after another into the same dataset, and every row carries a brand column, so a combined export stays sortable and groupable.

Step 2: Choose your market and scope

Set Market to a two-letter country. Leave Sweep All Categories on for the catalogue, or turn it off and name one category. Switch Run Mode to detail to read product URLs you paste instead.

Step 3: Cap it, run it, export it

Max Products is a run total across every brand you selected, not a per-brand cap — set it low for the first run. Export as JSON, CSV or Excel from the dataset tab.

That's it. Within a few minutes you'll have:

• One table covering every brand you picked, in one market
• Price, old price, discount and currency per product
• Per-SKU sizes with stock, and GTIN barcodes on five of the six shared-catalogue brands (not Massimo Dutti)
• A run summary telling you exactly what was and was not fetched

📥 Input Configuration

Field	Type	Required	Description
brands	array	No	Which brands to scrape: zara, zarahome, bershka, massimodutti, stradivarius, oysho, pullandbear. Defaults to all seven
mode	string	No	overview walks the categories, detail reads the URLs you paste. Default overview
country	string	No	Two-letter market, e.g. DE, ES, FR, GB, JP. The list is the union across the seven brands — ~216 markets on the shared-catalogue six, 96 on zara.com
language	string	No	Two-letter language for names and descriptions. Empty takes the market default
allCategories	boolean	No	Sweep every category. On by default, and it wins over Single Category
category	string	No	One category by slug or numeric id. Only used when the sweep is off
fromSitemap	boolean	No	Also crawl the product sitemap for anything the categories missed. Bills at the detail rate
maxItems	integer	No	Stop after this many distinct products — a run total across all brands. 0 means no cap. Default 100
withSizes	boolean	No	Zara only: fetch per-SKU sizes, stock and the description. Charges both events on those rows
productUrls	array	No	Product URLs or bare ids, one per line. URLs route to the brand their hostname names
sort	string	No	Zara and the shared-catalogue brands use different sort ids. default is the only one that works for every brand at once
filters	array	No	Facet filters as GROUP=VALUE, one per line. Same group OR-ed, different groups AND-ed
minPrice	integer	No	Drop products below this price, in the market's major units
maxPrice	integer	No	Drop products above this price, in the market's major units
batchSize	integer	No	Product ids per call on the shared-catalogue brands. Default 100, maximum 200
jitterMs	integer	No	Random pause before each request, in milliseconds. Default 0
proxyConfiguration	object	No	Leave it off. The Actor reaches these APIs directly and a proxy adds cost without adding success

Example — all seven brands, Spanish market:

{
  "brands": ["zara", "zarahome", "bershka", "massimodutti", "stradivarius", "oysho", "pullandbear"],
  "mode": "overview",
  "country": "ES",
  "allCategories": true,
  "maxItems": 1400
}

Example — two brands, discounted items only:

{
  "brands": ["bershka", "stradivarius"],
  "mode": "overview",
  "country": "FR",
  "allCategories": true,
  "maxPrice": 40,
  "maxItems": 500
}

Example — mixed-brand URLs:

{
  "mode": "detail",
  "country": "DE",
  "productUrls": [
    "https://www.bershka.com/de/t-shirt-c0p123456789.html",
    "https://www.oysho.com/de/leggings-c0p987654321.html"
  ]
}

📤 Output

Results are saved to the Apify dataset with 58 columns per product, identical across all seven brands. Export as JSON, CSV or Excel.

Field	Type	Description
brand	string	Which brand the row belongs to: zara, zarahome, bershka, massimodutti, stradivarius, oysho, pullandbear
product_id	integer/string	Catalogue id. The only column that is never null — the join key for everything else
url	string	Canonical product page for the market you scraped
source	string	Which call produced the row: overview, detail, or overview+detail
url_id	integer/string	Numeric URL id on the six shared-catalogue brands. Null on Zara
reference	string	Internal style reference, stable across markets
display_reference	string	The shorter reference printed on the page and the label
seo_product_id	string	Id used in Zara SEO URLs. Null on the shared-catalogue brands
seo_keyword	string	URL slug for the product. Zara only
name	string	Product name in the market language
name_en	string	English product name, when the catalogue carries one. Shared-catalogue brands only
product_type	string	Catalogue type of the record, e.g. Product or Bundle
kind	string	Zara grid classification of the tile. Null on the shared-catalogue brands
country	string	Market the prices and names belong to
language	string	Language the names and descriptions came back in
store_id	integer/string	Internal store id. Shared-catalogue brands only
catalog_id	integer/string	Internal catalogue id. Shared-catalogue brands only
section_name	string	Top level of the catalogue tree, e.g. WOMAN. Zara only
section_name_en	string	English name of the section. Shared-catalogue brands only
family_name	string	Product family in the market language
family_name_en	string	English product family. Shared-catalogue brands only — group on family_name when Zara is in the run
subfamily_name	string	Sub-level of the family in the market language
subfamily_name_en	string	English sub-level of the family. Shared-catalogue brands only
categories	array	Every category the product is filed under, as {id, name}. Shared-catalogue brands only
category_id	integer/string	Id of the category this row was scraped from. Zara only
category_name	string	Name of the category this row was scraped from. Zara only
price	number	Current price in major units, divisor already applied
old_price	number	Pre-discount price. Null when the product is not reduced
currency	string	ISO currency of the market, e.g. EUR, GBP, JPY
discount_pct	number	Percentage off, computed from price and old price
on_special	boolean	Catalogue flag for a promotional product. Shared-catalogue brands only
main_image	string	First image of the shown colour, full resolution
images	array	Every image URL for the shown colour, in catalogue order
description	string	Long product description. Zara grid rows carry none — turn on the size fetch
additional_info	string	Extra copy the catalogue attaches to the product. Shared-catalogue brands only, and measured empty on every product we sampled
keywords	string	Merchandising keywords attached to the product. Shared-catalogue brands only, and measured empty on every product we sampled
assembly_url	string	Link to an assembly or instruction sheet, mostly homeware and furniture. Shared-catalogue brands only
color_name	string	Name of the colour this row represents
colors	array	Colour objects with id, name, reference and, on Zara, price, availability and hex
available_color_names	array	Colour names the grid offers for this style. Zara only
composition	array	Material breakdown per garment part, with the percentage of each fibre
care	array	Washing and care instructions as {id, name, description}. Shared-catalogue brands only — Zara's care text arrives inside description
variants	array	One entry per colour and size — see the table below
size_guide	string	Set to enabled when the product page offers a size guide. Zara detail records only
availability	string	Stock state of the shown colour. Zara only — shared-catalogue brands report stock per SKU
is_buyable	boolean	Whether the product can currently be added to a basket. Shared-catalogue brands only — Zara reports stock in availability
back_soon	boolean	Catalogue flag for a restock that is already scheduled. Shared-catalogue brands only
visibility	string	Catalogue visibility state, e.g. visible or hidden. Shared-catalogue brands only
availability_date	string	Date the product becomes or became available. Shared-catalogue brands only
first_visible_date	string	When the product first appeared in the catalogue. Zara detail records only
is_continuity	boolean	Carryover line versus seasonal drop. Shared-catalogue brands only
is_pinned	boolean	Whether merchandising pinned the tile to a fixed slot. Zara grid only
grid_position	integer	Position of the tile inside the category grid. Zara grid only
join_life	string	Join Life sustainability label text, when the product carries one. Shared-catalogue brands only
sustainability_show	boolean	Whether the product page shows a sustainability badge. Shared-catalogue brands only
sustainability	object	Full sustainability node off the shown colour. Shared-catalogue brands only
traceability	object	Supply-chain traceability node. Shared-catalogue brands only
certified_materials	array	Certified material entries off the shown colour. Shared-catalogue brands only

Inside variants — one entry per colour and size:

Field	Description
sku	Stock-keeping unit id for this colour and size
color / color_id	Colour name and id of the SKU
size / size_id	Size label as the market prints it, plus Zara's size id
partnumber	Shared-catalogue part number
price / old_price	SKU price and pre-discount price in major units
barcode	GTIN barcode of the SKU — shared-catalogue brands
price_start_date / price_end_date	When the current price took effect and when it expires
old_price_start_date / old_price_end_date	The same window for the pre-discount price
is_buyable / back_soon	Whether this SKU can be bought now, and whether a restock is scheduled
dimensions / weight	Named physical axes and gram weight of the SKU
origin	Manufacturing country of the SKU
availability / reference / demand	Zara per-SKU stock state, reference and sell-through signal
equivalent_size_id / twinned_skus	Zara cross-market size id, and the same SKU under sibling style ids

Example row (trimmed):

{
  "brand": "bershka",
  "product_id": 123456789,
  "url": "https://www.bershka.com/de/t-shirt-c0p123456789.html",
  "source": "overview",
  "name": "OVERSIZE T-SHIRT",
  "price": 9.99,
  "old_price": 15.99,
  "currency": "EUR",
  "discount_pct": 37.5,
  "country": "DE",
  "family_name_en": "T-SHIRTS",
  "is_continuity": false,
  "variants": [
    { "sku": 44112233, "size": "M", "barcode": "8445123456789", "price": 9.99, "is_buyable": true,
      "price_start_date": "2026-08-01T00:00:00Z", "price_end_date": "2026-08-31T23:59:59Z", "weight": 180 }
  ]
}

Fields that differ by brand. GTIN barcodes, promotion windows, dimensions, weight, origin, traceability, certified_materials, care, on_special, is_buyable, back_soon, visibility, availability_date, join_life, the sustainability node, categories, additional_info, keywords, assembly_url and every *_en name column come from the six shared-catalogue brands, not from Zara. first_visible_date, grid_position, is_pinned, seo_keyword, section_name, category_id, category_name, size_guide, available_color_names and per-SKU demand come from Zara. Massimo Dutti publishes no GTIN barcodes at all and very few descriptions, which is why its own single-brand Actor is priced lower. traceability and certified_materials are present in the schema but frequently empty — treat them as a bonus, not a guarantee.

💼 Use Cases & Examples

1. Cross-brand price architecture

A pricing team wants to see how the group ladders its brands.

Input: all seven brands, one market, full sweep Output: price, family and subfamily taxonomy, brand on every row Use: average price per family per brand, where the brands overlap and where they do not

2. Group-wide markdown tracking

A retail analyst wants the whole group's discount calendar, not one brand's.

Input: a weekly run across all seven brands Output: old_price, discount_pct, on_special, and per-SKU promotion start and end dates Use: when each brand starts and ends a markdown, and how deep it goes

3. Barcode-keyed catalogue matching

A marketplace needs to match Inditex products against its own inventory.

Input: the six shared-catalogue brands, one market Output: GTIN barcodes per SKU, with price, size and stock Use: joining to any other retail dataset without fuzzy name matching

4. Assortment overlap analysis

A category manager wants to know where two sibling brands compete with each other.

Input: two brands, same market, same categories Output: one table with identical taxonomy columns for both Use: range overlap by family, price gaps at the same product type

5. Multi-market expansion research

A team is choosing which market to enter and wants the group's local pricing.

Input: the same brands run across several country values Output: each market's own prices and currency, with country on every row Use: local price levels, assortment differences, currency-adjusted benchmarks

6. Sustainability and composition reporting

A researcher wants fibre composition across the group's catalogue.

Input: all seven brands, full sweep Output: composition per garment part on every brand; care, join_life and the sustainability node on the six shared-catalogue brands Use: fibre mix by brand and family, synthetic share, care-label analysis

🔗 Integration Examples

JavaScript/Node.js

import { ApifyClient } from 'apify-client';
const client = new ApifyClient({ token: 'YOUR_TOKEN' });

const run = await client.actor('sian.agency/inditex-product-scraper').call({
  brands: ['zara', 'bershka', 'stradivarius'],
  mode: 'overview',
  country: 'ES',
  allCategories: true,
  maxItems: 600,
});

const { items } = await client.dataset(run.defaultDatasetId).listItems();
console.log(`Got ${items.length} products across ${new Set(items.map((i) => i.brand)).size} brands`);

Python

from apify_client import ApifyClient
client = ApifyClient('YOUR_TOKEN')

run = client.actor('sian.agency/inditex-product-scraper').call(
    run_input={
        'brands': ['zarahome', 'oysho'],
        'mode': 'overview',
        'country': 'FR',
        'allCategories': True,
        'maxItems': 400,
    }
)

for item in client.dataset(run['defaultDatasetId']).iterate_items():
    print(item['brand'], item['name'], item['price'], item['currency'])

cURL

curl -X POST 'https://api.apify.com/v2/acts/sian.agency~inditex-product-scraper/runs?token=YOUR_TOKEN' \
-H 'Content-Type: application/json' \
-d '{
  "brands": ["pullandbear"],
  "mode": "overview",
  "country": "GB",
  "allCategories": true,
  "maxItems": 200
}'

Automation Tool Workflows (n8n, Zapier, Make, etc.)

1. Trigger: schedule or webhook
2. HTTP Request: start a run and wait for the dataset
3. Process: group by brand and family_name_en, diff against last week
4. Action: write to your warehouse, or alert on new markdowns across the group

📊 Performance & Pricing

💰 View current pricing

Performance

• ~20 products per second on the category sweep
• Brands run sequentially into one dataset; Max Products is a run total across all of them
• 512 MB memory, no proxy, no browser — direct API reads
• Duplicates removed before anything is charged, so you pay once per distinct product

How you are billed

Three events, and the source column on every row tells you which one applied. Your invoice reconciles against the dataset, brand by brand.

What you ran	source on the row	Event	Price
Run start	—	Actor start	$0.005 once
Category sweep	overview	Scraped product	$0.0035
Pasted URL, or a sitemap row	detail	Scraped product detail	$0.0105
Zara sweep row plus its SKU fetch (withSizes)	overview+detail	both	$0.014

Two switches move a row onto the higher-priced event. Also Crawl the Sitemap cannot use the bulk endpoint — it is one request per product, so those rows bill at $0.0105 instead of $0.0035. Fetch Sizes & SKUs on Zara genuinely costs both calls and charges both, so those rows cost $0.014. The category sweep is the default and the cheap path; use the sitemap when completeness matters more than price.

Cost examples

• 1,000 products across three brands, category sweep: $3.505
• 7,000 products, all seven brands, category sweep: $24.505
• 500 pasted product URLs: $5.255

Prices shown are the BRONZE tier. They step down at SILVER, GOLD, PLATINUM and DIAMOND, so heavier use costs less per row.

What it replaces

The only vendor covering all seven brands sells them as seven monthly rentals: $24.99 for Zara plus six at $9.99, $84.93 a month before you have scraped anything. Here, 7,000 products across all seven brands costs $24.51 and you pay nothing in the months you do not run it.

If you only want one brand, use its own Actor. The single-brand Actors run $0.0025–$0.003 per product, so per row they are cheaper than this one. What the extra buys here is the normalization: seven catalogues land in one dataset with one schema and a brand column, from a single run you schedule once. That is worth paying for when you need to compare the brands against each other. If you do not, run the single-brand Actor instead.

❓ Frequently Asked Questions

Q: Do all seven brands really return the same columns? A: Yes — 58 columns on every row, whichever brand produced it. Columns only one catalogue carries come back null on the others, and the table above says which is which.

Q: Is Max Products per brand or per run? A: Per run. Selecting seven brands with a cap of 100 gives you 100 products in total, not 700. The run splits that total between the brands rather than letting the first one swallow it, so all seven are represented; a brand with less stock than its share passes the remainder on. Set it to 0 for no cap.

Q: Can I mix brands in detail mode? A: Yes, if you paste full product URLs — each is routed to the brand its hostname names. A bare numeric id goes to the first brand in your list, so do not mix bare ids across brands.

Q: Why does Zara come back without barcodes? A: Zara's catalogue does not publish GTIN codes; the six shared-catalogue brands do. Massimo Dutti is the one exception among those six — it publishes no barcodes and very few descriptions.

Q: Can I search by keyword? A: No, and neither can anything else. None of these sites exposes a server-side search endpoint. Name a category or sweep them all and filter the results yourself.

Q: Does it return customer reviews? A: No. No Inditex brand publishes ratings or reviews through these catalogues, so no scraper can return them.

Q: Which markets are supported? A: Not the same number for every brand. The six shared-catalogue brands are open in ~216 markets each; zara.com is open in 96. The Market dropdown lists the union of 220, so a market outside Zara's 96 works for the other six but stops a run that includes Zara — deselect Zara, or pick a market Zara serves. Prices come back in that market's own currency with the correct divisor applied.

Q: What output formats are available? A: JSON, CSV and Excel, exported straight from the dataset. There is also a run summary in the key-value store with the counts for the run.

🐛 Troubleshooting

One brand returned nothing while the others worked

• That brand's category may be a container rather than a product grid — those return no ids
• The run continues and warns rather than failing, so check the log for which brand was skipped

The run summary says complete: false

• Some fetches failed for transport reasons, so the dataset is short. Re-run to fill the gap
• Ids reported as gone are a different thing: those products no longer exist in that market

Sort stopped the run

• Zara and the six shared-catalogue brands use different sort ids. default is the only option that works for every brand at once

The run stopped on the market, not the data

• The Market dropdown is the union across the seven brands. zara.com is open in 96 markets, the other six in ~216, so a market outside Zara's 96 stops a run that includes Zara
• Deselect Zara in Brands, or pick a market Zara serves

A bare product id went to the wrong brand

• Bare ids go to the first brand in your list. Paste full URLs when mixing brands

Sitemap mode is slower and dearer than expected

• That is the trade: it is one request per product instead of one per hundred, and those rows bill at the detail rate. Use the category sweep unless you need the products no category lists

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⚖️ Is it legal to scrape data?

Our actors are ethical and do not extract any private user data, such as email addresses, gender, or location. They only extract what the user has chosen to share publicly. We therefore believe that our actors, when used for ethical purposes by Apify users, are safe.

However, you should be aware that your results could contain personal data. Personal data is protected by the GDPR in the European Union and by other regulations around the world. You should not scrape personal data unless you have a legitimate reason to do so. If you're unsure whether your reason is legitimate, consult your lawyers.

You can also read Apify's blog post on the legality of web scraping.

Trademarks. Zara, Zara Home, Bershka, Massimo Dutti, Stradivarius, Oysho and Pull&Bear are trademarks of Industria de Diseño Textil, S.A. This Actor is an independent tool. It is not affiliated with, endorsed by or sponsored by Inditex or any of its brands, and it reads only publicly available catalogue pages.

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• 🐛 Found a bug? File an issue in the Apify Console Issues tab
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