Darty Scraper
We do not have a dedicated Darty data API. What we do have is an AI scraper that reads a darty.com page you point it at, turns it into the columns you asked for and hands them back by REST, CSV or Excel — on a schedule, priced per page.
- darty.com
How to scrape Darty
Darty publishes no read API for its catalogue. There is no key to apply for and no endpoint to call, which is why the page is the interface. Darty's contract de confiance commits it to matching competitors, which makes its price a derivative of the market rather than an input to it. The way to get the data is to read the page the way a customer sees it: point the AI scraper at a darty.com URL, declare the columns you want in plain English, and schedule it. Electronics is the category where price changes fastest and where a promotion, a bundle or an open-box price can undercut the shelf price without the shelf price moving at all.
What the AI scraper does with a darty.com link
The same three steps as any other page you point it at. Nothing about Darty is pre-built, which is exactly why it works on pages nobody wrote a connector for.
Paste the link
Give it a darty.com product or listing URL. Market, currency and assortment all come from the page itself, so there is no region setting to get wrong — the URL decides.
Declare the columns
Name each field, give it a type and write a line telling the AI what to look for. “product_title” and “brand” are the two most people start with. The schema is yours and you can change it between runs.
Schedule it and collect
Run it hourly, daily or on demand. Every run returns the same columns in the same order with a timestamp on each row, so the history builds itself.
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. Everything else — market, currency, which products are on the page — comes from the page, so there is nothing to configure twice.
- Link to a Darty pagerequired
Open the product or category page in your browser and copy the address. Any Darty country domain works the same way.
https://www.darty.com/ - Your column listrequired
Field name, type and one line of plain English per column. Written once, reused on every run.
product_title, brand, sku, mpn - How often it runsoptional
Hourly, daily, weekly or on demand through the API. Daily is what most price monitoring uses.
Daily at 06:00 UTC
One row per product on the page, 13 columns each
- product_title
- brand
- sku
- mpn
- price
- was_price
- currency
- availability
- delivery_estimate
- rating
- review_count
- product_url
- +1 more, see all columns
REST API, CSV or Excel. The column set stays the same between runs, so downstream jobs do not re-map fields.
The columns a Darty run comes back with
We are not going to print a table of invented electronics prices and call it real output. Darty answers an ordinary request with Akamai rather than a product page, so we have no run of our own to quote from. So what is published here is the column list you declare before the run, which is the same list every run comes back with.
product_titleProduct title as the site writes itbrandBrand as the site labels itskuSite's own article numbermpnManufacturer part number, where the page prints onepricePrice the site is charging todaywas_priceStrike-through price, only where the page shows onecurrencyCurrency the page quotedavailabilityStock state as the page states itdelivery_estimateDelivery window shown on the pageratingAverage rating, where shownreview_countHow many reviews back that ratingproduct_urlLink back to the page the row came fromchecked_atWhen this run collected the page (UTC)Every column here is one you name yourself in the schema. If a field is not on the page on the day of the run, it comes back empty rather than guessed.
What a Darty page costs
Pay-as-you-go from your wallet. No plan, no monthly fee, no seat count.
Measured on 3 October 2026, not assumed. A plain request for a darty.com page is refused with HTTP 403 by Akamai, and so is the bare homepage, which rules out a bad path as the explanation. Clearing that needs both a real browser and a residential exit.
500 Darty pages checked once a day for a month is 15,000 pages.
about EUR 56.25 for the month
You are charged for what a run actually used, not what 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 Darty endpoint
There is no Darty API in our catalogue and no pre-built Darty schema. The AI scraper reads the page you point it at — that is the whole mechanism, and it is why it works on pages nobody built a connector for.
Nothing behind a login
It reads what a visitor can see. Account pricing, contract pricing and anything behind a sign-in are out of scope.
No field the page does not show
If the page does not print it, the scraper cannot return it. Identifiers like EAN or GTIN only come back where the retailer publishes them.
A price is a point in time
Every row carries the timestamp of the run that collected it. A price without one is not evidence, which is why the column is in the default schema.
Darty has one specific trap
Prices differ between the Fnac and Darty storefronts despite the shared group.
Not a bulk dump of the catalogue
You give it the URLs you care about. It does not crawl darty.com end to end, and a schedule that tried to would cost more than the answer is worth.
Typical fields the AI scraper extracts from a Darty page
Treat this as a starting point rather than a fixed schema. What comes back is whatever the page actually shows on the day of the run.
Price and what is hiding behind it
In electronics the headline figure is frequently not the price a buyer pays, and the difference is printed on the page.
- Current price
- Strike-through or was-price, where the page shows one
- Promotion, bundle or voucher wording
- Open-box or refurbished price, where listed separately
- Currency and market the price belongs to
Identity you can join on
Electronics is the one category where a manufacturer part number is usually on the page, which makes cross-retailer matching tractable.
- Manufacturer part number, where printed
- EAN or GTIN, where published
- The site's own article number
- Brand and model name
- Category breadcrumb
Stock and delivery
A competitor who is cheaper and out of stock is not actually cheaper, and electronics stock turns over daily.
- Stock state as the page states it
- Delivery window shown
- Click-and-collect availability, where the page shows it
- Backorder or pre-order wording
- Rating and review count
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 run, not to us. How Custom Schema works →
What it cannot give you If a field is not visible on the page, the scraper cannot invent it. Identifiers such as EAN or GTIN only come back when Darty publishes them.
What people use Darty data for
Daily competitor price tracking
The reason most people land here. A list of product URLs, a daily schedule, and a table you can diff against yesterday rather than a tab you check by hand.
Promotion and bundle detection
The shelf price can sit still while a voucher, a bundle or an open-box listing does the actual undercutting. Capturing the was-price and the promotion wording is what makes that visible.
Cross-retailer matching on part numbers
Electronics pages usually publish a manufacturer part number, which means one schema across several retailers produces a table that genuinely lines up rather than one that needs fuzzy matching.
Stock-out monitoring
Knowing a competitor is out of stock on a hero SKU is worth as much as knowing their price, and it is the same scrape.
Reseller price-floor checks
If you manufacture, this is how you see what your own products are actually listed at across the retailers that carry them, with the date attached.
Feeding your own repricing rules
The export is a normal REST API or CSV, so it drops into whatever repricing logic you already run.
What makes Darty harder than an ordinary storefront
Here is what the scraper is actually up against on darty.com, measured rather than assumed.
Access
- Plain HTTP requests are refused at the edge — a naive fetch returns HTTP 403, not a page
- Named protection on the response: Akamai
- No usable sitemap directive in robots.txt, so the URL list comes from your own category pages
The price is rarely one number
- Strike-through prices, vouchers and bundles move the real price without moving the shelf price
- Open-box and refurbished listings are separate pages with separate prices
- Stock state changes faster than price and changes what a price means
- Category pages are paginated and lazily loaded, so a category sweep is a list of URLs rather than one request
What the page publishes
- No product-level structured data is published, so every field is read off the rendered page
- Prices differ between the Fnac and Darty storefronts despite the shared group.
- Price is one of the last things the page settles on, so a read taken too early records the placeholder rather than the number
Keeping a history
- A single read is a snapshot; the value is in the series, which means the run has to be scheduled and the rows kept
- Every row carries the timestamp of the run that produced it, so two days can be compared without guesswork
- Columns stay stable between runs, so a dashboard written once does not break when the site redesigns
Darty scraping — questions
What people ask before pointing the scraper at darty.com.
Darty publishes no read API for its catalogue. There is no key to apply for and no endpoint to call, which is why the page is the interface.
Ready to pull Darty data into your stack?
Start free — or talk to our team about your exact fields, refresh cadence and volume.