👗 Zara Scraper

🚀 Export Zara's catalogue in seconds. Pick a category, get 34 fields per product back, including every single size with its own stock status, the retail SKU behind it, and the exact fabric composition.

Zara publishes a lot on a product page and lets you export none of it. This Actor reads Zara's own public catalogue data directly and returns each colourway as one flat row: the price, the description, the fabric breakdown, the release date, and a size-by-size stock table with the real SKU for each size. No login, no API key, no cookie to paste.

Every run returns 34 fields per product. One category call brings back the whole category in a single request, so a 531-product listing such as Women's Dresses is enumerated in about two seconds, and 40 fully detailed products land in roughly 25 seconds. The category picker carries 1,291 live Zara categories read from Zara itself, spanning Woman, Man, Kids, Zara Home, Beauty, Massimo Dutti and Pre-Owned, and the same run works across 22 country stores with the local currency, local names and local assortment.

🎯 Target Audience	💡 Primary Use Cases
Retail and pricing analysts	Track Zara price points and discount depth per category, per country, over time.
Competitive intelligence teams	Benchmark assortment size, colour range and fabric mix against your own line.
Merchandisers and buyers	Spot which sizes are selling out first and which colourways are flagged as running out.
Trend researchers and forecasters	Follow release dates and new-arrival tags to date the drop of every garment.
Dropshippers and marketplace sellers	Pull structured product feeds with SKUs, images and composition ready to import.

📋 What the Zara Scraper does

💡 Why it matters: a price on its own tells you almost nothing about how a garment is performing. What tells you is which sizes are already gone. This Actor is one of the few that returns Zara's per-size stock table, so sizesInStock: 4 against totalSizes: 6 says the piece is selling, and DEMAND_RUNNING_OUT on size 11-12 says exactly where.

• 📊 Size-by-size stock, not one flag. Every row carries a sizes array where each entry has its own availability (in_stock, low_on_stock, out_of_stock), the retail sku, the size reference and Zara's own demand signal. Most Zara actors give you a single availability field for the whole product.
• 🧵 Real fabric composition. Parsed from Zara's structured composition data, not scraped prose: OUTER SHELL: 69% cotton, 16% modal, 15% lyocell, with the exceptions note (EXCLUSIVE OF ELASTIC) kept in its own column.
• 📅 Release dates. releaseDate is Zara's own firstVisibleDate for the garment, so you can date a drop precisely instead of guessing from a "NEW" badge.
• 🎨 One row per colourway. Zara sells the same garment in several colours with different stock and different photos. Each colourway is its own row, deduplicated on Zara's colour reference, so nothing is double counted and nothing is collapsed away.
• 🗂 1,291 categories, read live. The picker is built from Zara's own category tree, not a hand-written list, and shows the full path (WOMAN > COLLECTION > DRESSES) so you know exactly what you are selecting.
• 🌍 22 country stores. Switch to es/es, jp/ja, uk/en, kr/ko and 18 more. Prices come back in the correct currency with the correct decimals, which matters for JPY and KRW where there are none.
• 🔻 Discounts as data. oldPrice, discountPercentage and isOnSale populate on sale items, so markdown depth is a column you can sort on rather than something to compute.
• 🔗 Paste any Zara URL. Category URLs and individual product URLs both work, and a URL from a non-US store is resolved against that store's own category tree.
• 📦 One fixed schema. Every product lands with the same 34 columns, so your spreadsheet or database mapping never breaks.

🎬 Full Demo (🚧 Coming soon)

A short walkthrough video is in production and will land here.

📊 Output

Each product colourway becomes one flat row. These are the fields:

Field	Type	Description
🖼 imageUrl	string	Primary product photo at 1024px.
👗 name	string	Product name as Zara publishes it in the selected language.
📦 availability	string	in_stock, low_on_stock, out_of_stock or coming_soon.
🔗 url	string	Canonical Zara product URL for this colourway.
🆔 productId	string	Zara's internal product ID for the colourway.
🏷 reference	string	Zara's colour reference, unique per colourway.
🔖 displayReference	string	The reference shown on the product page, such as 3777/600.
💲 price	number	Current price in the store's currency, decimals already applied.
💱 currency	string	ISO currency code read from Zara, such as USD or JPY.
💰 oldPrice	number or string	Pre-discount price, or N/A when the item is not reduced.
🔻 discountPercentage	string	Markdown depth such as 40%, or N/A.
🛍 isOnSale	string	Yes or No.
🎨 colorName	string	Colour name for this row, such as Oyster-white.
🖌 colorHex	string	Hex swatch Zara uses for the colour.
🚻 section	string	WOMAN, MAN, KID, HOME or BEAUTY.
📚 family	string	Garment family in English, such as DRESS or TROUSERS.
📖 subfamily	string	Zara's finer classification, such as GRL DENIM TRS.
🗂 categoryPath	string	Full breadcrumb of the category it was found in.
📝 description	string	Zara's own product description.
🧵 composition	string	Fabric breakdown per part with percentages.
🪡 compositionNote	string	Composition exceptions, or N/A when there are none.
📅 releaseDate	string	ISO date the garment first went live on Zara.
✨ isNew	string	Yes when Zara tags it as new.
🌈 totalColors	number	Colourways of this garment on Zara.
📏 totalSizes	number	Sizes Zara lists for this colourway.
✅ sizesInStock	number	How many of those sizes you can actually buy right now.
⚠️ hasLowStock	string	Yes when any size is low or flagged as running out.
🎨 availableColors	array	Every colour name this garment comes in.
📐 sizesAvailable	array	Just the size labels currently buyable.
🏷 tags	array	Zara's own badges, such as NEW or FEW ITEMS LEFT.
📊 sizes	array	Per size: label, availability, SKU, reference and demand signal.
🖼 imageUrls	array	Full photo gallery for this colourway at 1024px.
🕒 scrapedAt	string	ISO timestamp of the run.
❌ error	string	null on success, the reason on a failed row.

Sample records

A full-price dress from WOMAN > COLLECTION > DRESSES, all six sizes in stock:

{
  "imageUrl": "https://static.zara.net/assets/public/ddee/e3ec/1da145698979/77ba8e7ef965/04772272933-p/04772272933-p.jpg?ts=1787843696948&w=1024",
  "name": "SATIN MIDI DRESS WITH FROG FASTENINGS",
  "availability": "in_stock",
  "url": "https://www.zara.com/us/en/midi-jacquard-frog-fastening-dress-p04772373.html?v1=558566649",
  "productId": "558566649",
  "reference": "C04772373933000-I2026",
  "displayReference": "4772/373",
  "price": 79.9,
  "currency": "USD",
  "oldPrice": "N/A",
  "discountPercentage": "N/A",
  "isOnSale": "No",
  "colorName": "Green",
  "colorHex": "#2ec176",
  "section": "WOMAN",
  "family": "DRESS",
  "subfamily": "T-DRESS",
  "categoryPath": "WOMAN > COLLECTION > DRESSES",
  "description": "Midi halter neck dress. Contrasting piping detail. Tonal short interior lining. Chest closure with frog fastenings and back hidden in-seam zip and tie closure.",
  "composition": "OUTER SHELL: 100% polyester",
  "compositionNote": "EXCEPT FOR TRIMMINGS",
  "releaseDate": "2026-08-30T00:00:00Z",
  "isNew": "Yes",
  "totalColors": 2,
  "totalSizes": 6,
  "sizesInStock": 6,
  "hasLowStock": "No",
  "availableColors": ["Green", "Oyster-white"],
  "sizesAvailable": ["XS", "S", "M", "L", "XL", "XXL"],
  "tags": ["NEW"],
  "sizes": [
    { "size": "XS", "availability": "in_stock", "sku": "558541997", "reference": "0477227293301-I2026", "demand": "DEMAND_REGULAR" },
    { "size": "S", "availability": "in_stock", "sku": "558541998", "reference": "0477227293302-I2026", "demand": "DEMAND_REGULAR" }
  ],
  "scrapedAt": "2026-08-31T19:22:22.303Z",
  "error": null
}

A reduced item where the size table is doing the talking: two sizes gone, one running out.

{
  "name": "WIDE LEG JEANS WITH DRAWSTRINGS",
  "url": "https://www.zara.com/us/en/wide-leg-jeans-with-drawstrings-p03777600.html?v1=577969328",
  "reference": "C03777600400000-I2026",
  "price": 21.54,
  "currency": "USD",
  "oldPrice": 35.9,
  "discountPercentage": "40%",
  "isOnSale": "Yes",
  "colorName": "Blue",
  "section": "KID",
  "family": "TROUSERS",
  "categoryPath": "KIDS > GIRL > COLLECTION > SPECIAL PRICES",
  "composition": "OUTER SHELL: 69% cotton, 16% modal, 15% lyocell",
  "compositionNote": "EXCLUSIVE OF ELASTIC",
  "releaseDate": "2025-11-29T00:00:00Z",
  "totalSizes": 6,
  "sizesInStock": 4,
  "hasLowStock": "Yes",
  "tags": ["FEW ITEMS LEFT"],
  "sizes": [
    { "size": "6-7 years (48,0 INCHES)", "availability": "in_stock", "sku": "554921542", "demand": "DEMAND_REGULAR" },
    { "size": "8-9 years (51,6 INCHES)", "availability": "out_of_stock", "sku": "554921532", "demand": "DEMAND_REGULAR" },
    { "size": "11-12 years (58,3 INCHES)", "availability": "low_on_stock", "sku": "554921549", "demand": "DEMAND_RUNNING_OUT" },
    { "size": "13-14 years (64,6 INCHES)", "availability": "out_of_stock", "sku": "554921527", "demand": "DEMAND_REGULAR" }
  ],
  "error": null
}

A Japanese store row, showing that yen comes back whole rather than divided by a hundred:

{
  "name": "コントラストストラップ＆スタッズ ワンピース - THE ITEM",
  "price": 29990,
  "currency": "JPY",
  "colorName": "エクリュ",
  "section": "WOMAN",
  "family": "DRESS",
  "sizesAvailable": ["XS", "S", "M", "L", "XL"],
  "error": null
}

✨ Why choose this Actor

• 🔬 The size table is the product. Per-size availability, SKU and demand signal is the field set that turns a Zara export into stock intelligence, and it is the field set most Zara actors leave out.
• 🧾 No always-empty columns. Fields Zara does not publish were dropped during research rather than shipped as permanently blank headers. Where a value can genuinely be withheld you get a labelled sentinel, never a silent null.
• 🌐 22 stores, correct money. Currency exponents are read from Zara per store, so JPY and KRW come back as whole units instead of being wrongly divided.
• 🗂 A category picker that is actually Zara's. 1,291 entries pulled from the live tree with full breadcrumbs, covering Zara Home, Beauty, Massimo Dutti and Pre-Owned as well as apparel.
• ⚡ One request per category. Zara returns an entire category in a single call, so listing 531 dresses takes about two seconds before detail fetching even begins.
• 🎯 Filters that run before the expensive work. Colour, price range, sale and stock filters are applied to the listing first, so you only pay the detail request for products you actually want.
• 🧯 Honest failure. A category that does not exist in your chosen store returns an error row telling you exactly what to do instead, rather than an empty dataset.

📈 How it compares to alternatives

	This Actor	Typical Zara actors
Per-size stock status	✅ Every size with its own in_stock / low_on_stock / out_of_stock	⚠️ Usually one availability flag for the whole product
Retail SKU per size	✅ Yes	❌ Rarely
Demand signal	✅ Zara's DEMAND_RUNNING_OUT flag surfaced	❌ No
Fabric composition	✅ Structured, per garment part, with percentages	⚠️ Sometimes, as raw prose
Release date	✅ Zara's own firstVisibleDate	❌ No
Category coverage	✅ 1,291 live categories including Zara Home, Beauty, Massimo Dutti	⚠️ Often apparel URLs only
Country stores	✅ 22, with correct currency decimals	⚠️ Often one store
Row grain	✅ One row per colourway, deduped on Zara's colour reference	⚠️ Often one row per product, losing colour-level stock
Search by keyword	❌ Not offered	⚠️ Some offer it

What this Actor does not do, stated plainly. Zara's keyword search runs through a third-party engine that is not publicly reachable, so there is no search input here: you browse by category or paste URLs. Zara's bulk product-detail endpoint is closed at its edge, so full detail costs one request per garment, about half a second; turn fetchProductDetails off for a much faster listing-only pass. Zara also publishes no ratings, no reviews and no numeric stock counts anywhere, so no such columns are invented. Finally, the category picker lists the US catalogue, which is the largest; smaller stores number a few categories differently, and for those you paste a category URL from that store instead.

🚀 How to use

1. Create a free Apify account. Sign up here and you start with $5 of free credit, no card required.
2. Open the Actor and go to the Input tab.
3. Pick a category from the picker, or paste Zara category and product URLs into Zara URLs. URLs win when you provide both.
4. Choose your store if you want something other than the United States.
5. Set Max Items. Start small while you check the shape of the data.
6. Add filters if you want them. Colour words, size labels, a price range, sale only, in stock only.
7. Click Start and watch the log.
8. Download the results as CSV, Excel, JSON or XML from the Dataset tab, or pull them from the API.

A minimal input looks like this:

{
  "category": "2420895",
  "store": "us/en",
  "maxItems": 100,
  "fetchProductDetails": true
}

💼 Business use cases

🏷 Competitive pricing and markdown tracking

Run the same category daily and diff price, oldPrice and discountPercentage. Because rows are keyed on Zara's colour reference, a colourway is comparable across runs even when Zara reshuffles the grid. You can see the exact day a line goes on markdown and how deep the first cut is, per country store.

📦 Sell-through and stock intelligence

sizesInStock against totalSizes is a sell-through proxy you can compute on day one. Watch which sizes disappear first across a category to infer size curve demand, and use hasLowStock and the DEMAND_RUNNING_OUT demand signal to catch a line selling out before the price ever moves.

🧵 Assortment and material benchmarking

Aggregate composition across a category to see the real fibre mix behind a competitor's range, then compare it to your own. family, subfamily and totalColors let you measure assortment width and colour depth per garment type without opening a single product page.

🛒 Product feed and catalogue building

Every row already carries the SKU per size, the full image gallery, the description and the composition, which is most of what a marketplace listing needs. Filter to in-stock items, export to CSV, and map straight into Shopify, a PIM or a dropshipping feed.

🔌 Automating Zara Scraper

• Make and Zapier: trigger a run on a schedule and push new rows into Sheets, Airtable or Notion.
• Slack: post an alert when discountPercentage crosses a threshold or sizesInStock hits zero on a product you watch.
• Airbyte: sync the dataset into Snowflake, BigQuery or Postgres for historical price series.
• GitHub Actions: call the Actor from a workflow and commit the CSV so your price history is version controlled.
• Google Drive: drop each run's export into a shared folder for the merchandising team.
• Apify Scheduler: run it every morning and let the dataset accumulate on its own.

🌟 Beyond business use cases

• Research. Fast fashion assortment turnover is hard to measure from the outside. releaseDate plus a daily run gives a real dataset on how quickly a global retailer cycles its range.
• Personal. Watch a specific garment in your size and get told when it goes on sale or when your size comes back.
• Non-profit. Sustainability groups can quantify synthetic versus natural fibre share across a mass-market range using the composition field, at catalogue scale.
• Experimentation. The image galleries and structured attributes make a clean, consistent corpus for training or evaluating product classification and visual search models.

🤖 Ask an AI assistant about this scraper

Paste this into ChatGPT, Claude or any assistant to have it design a workflow for you:

I am using the ParseForge Zara Scraper on Apify. It returns one row per Zara product colourway with 34 fields, including price, oldPrice, discountPercentage, fabric composition, release date, and a sizes array where every size has its own availability, SKU and demand signal. It covers 1,291 Zara categories across 22 country stores. Help me design a daily pipeline that tracks markdown depth and size sell-through for a category I choose.

❓ Frequently Asked Questions

🔑 Do I need a Zara account or an API key?

No. The Actor reads Zara's publicly available catalogue data. There is nothing to log into and nothing to paste.

🌍 Which countries are supported?

22 country stores, including the United States, United Kingdom, Spain, France, Germany, Italy, Portugal, Netherlands, Poland, Greece, Sweden, Turkey, Russia, Mexico, Canada, Brazil, Australia, India, United Arab Emirates, Japan and South Korea. Prices come back in the local currency.

🔎 Can I search Zara by keyword?

No, and that is deliberate. Zara's keyword search is served by a third-party engine that is not publicly reachable, so a search input would be unreliable. Browse by category or paste product URLs instead.

📏 Why is per-size stock a big deal?

Because a single "in stock" flag hides the story. A dress with six sizes where four are gone is behaving completely differently from one where all six are available, and only the size table tells you which.

⚡ How fast is it?

A category listing is one request, so 531 products are enumerated in about two seconds. With full details on, roughly 40 products land in 25 seconds. Turn fetchProductDetails off and a listing-only pass is far quicker.

🧵 Where does the composition come from?

From Zara's own structured composition data, split by garment part, so you get OUTER SHELL: 100% polyester and LINING: ... separately rather than one blob of text. Exceptions such as EXCEPT FOR TRIMMINGS land in their own column.

🎨 Why do I get several rows for what looks like one product?

Because Zara sells it in several colours, and each colour has its own photos, its own stock and sometimes its own price. Each colourway is one row, deduplicated on Zara's colour reference so nothing is counted twice.

🛡 Do I need a proxy?

A residential proxy is on by default and is required when running on Apify, because Zara's edge blocks datacenter traffic. The exit country follows the store you selected unless you set one yourself.

🚫 A category returned an error row. What happened?

The category picker lists the US catalogue, which is the largest. A few smaller stores number some categories differently. When that happens the Actor tells you in the row, and the fix is to paste a category URL copied from that country's own Zara site into Zara URLs.

📉 Does it capture sale prices?

Yes. On reduced items oldPrice, discountPercentage and isOnSale all populate, so markdown depth is a sortable column. On full-price items those fields read N/A rather than being left blank.

📊 Are ratings or reviews included?

No. Zara does not publish ratings or reviews on its product pages anywhere, so there is nothing to collect and no such column is invented.

💾 What export formats are available?

CSV, Excel, JSON and XML from the Dataset tab, plus the Apify API and integrations for everything else.

🔌 Integrate with any app

The dataset is available over the Apify API the moment a run finishes, so anything that speaks HTTP can consume it. Ready-made integrations cover Make, Zapier, Airbyte, Slack, Google Drive, GitHub, Google Sheets and Amazon S3, and webhooks can push each finished run straight into your own service.

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💡 Pro Tip: browse the complete ParseForge collection.

🆘 Need Help? Open our contact form

⚠️ Disclaimer: this is an independent tool and is not affiliated with, endorsed by or connected to Zara or Inditex. It collects only publicly available data.