Lieferando & Takeaway Scraper — Restaurants, Ratings, Menus
Pricing
from $2.00 / 1,000 result scrapeds
Lieferando & Takeaway Scraper — Restaurants, Ratings, Menus
Scrape restaurants from Lieferando.de, Thuisbezorgd.nl, Pyszne.pl and Takeaway.com (Belgium): names, cuisines, ratings, review counts, delivery fees and times, minimum order values, plus full menus with item prices. Search by city or postcode. No login, no cookies.
Pricing
from $2.00 / 1,000 result scrapeds
Rating
0.0
(0)
Developer
Studio Amba
Maintained by CommunityActor stats
0
Bookmarked
2
Total users
1
Monthly active users
7 days ago
Last modified
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Scrape restaurant data from five continental Just Eat Takeaway brands with one actor: Lieferando.de (Germany), Lieferando.at (Austria), Thuisbezorgd.nl (Netherlands), Pyszne.pl (Poland) and Takeaway.com (Belgium). Get every restaurant delivering to a city or postcode — names, cuisines, ratings, review counts, delivery fees, delivery times and minimum order values — and optionally the full menu of every restaurant with item prices.
No login, no cookies, no session tokens. Pick a country, type a city, run.
Why use this actor?
Food delivery market data is sold by at least five commercial data services, usually as expensive subscriptions. This actor gives you the same raw data on demand: competitive pricing analysis for restaurants, delivery-fee benchmarking, market-entry research, menu price monitoring, or lead lists of restaurants per city and cuisine. One run on a big city returns 900–1,600 restaurants in under a minute.
What it covers
| Brand | Country | Input value | Currency |
|---|---|---|---|
| Lieferando.de | Germany | de | EUR |
| Lieferando.at | Austria | at | EUR |
| Thuisbezorgd.nl | Netherlands | nl | EUR |
| Pyszne.pl | Poland | pl | PLN |
| Takeaway.com | Belgium | be | EUR |
All five brands run on the same Just Eat Takeaway platform, so the data model is identical across countries. The same run works the same way in Berlin, Vienna, Amsterdam, Warsaw or Brussels.
Input
| Field | Type | Required | Description |
|---|---|---|---|
country | String | No | de (Lieferando.de), nl (Thuisbezorgd), pl (Pyszne), be (Takeaway.com) or at (Lieferando.at). Default: de |
location | String | No | City name (Berlin, München, Warszawa), postcode (10115) or literal coordinates (52.52,13.40). Default: Berlin |
serviceType | String | No | delivery (default) or collection |
maxResults | Integer | No | Maximum restaurants to return (default: 100) |
scrapeMenus | Boolean | No | Also fetch each restaurant's full menu — one row per menu item (default: false) |
proxyConfiguration | Object | No | Proxy settings; the automatic default works fine |
City names are resolved offline against a bundled table of the 50 biggest cities per country (native names and exonyms both work: München/Munich, Den Haag/The Hague, Warszawa/Warsaw, Brussel/Bruxelles/Brussels). Postcodes and smaller towns fall back to OpenStreetMap geocoding automatically.
Output
One row per restaurant. With scrapeMenus enabled, one row per menu item with the restaurant fields repeated on every row — flat rows that load straight into a spreadsheet or database.
| Field | Type | Example |
|---|---|---|
restaurantId | String | "10015773" |
name | String | "Burger Vision Schönhauser Allee" |
brand | String | "lieferando" |
cuisines | Array | ["Burgers", "100% Halal"] |
rating | Number | 4.3 (0–5 scale) |
ratingCount | Integer | 2822 |
deliveryFee | Number | 1.49 |
deliveryTimeMinutes | Integer | 45 |
minimumOrderValue | Number | 10 |
currency | String | "EUR" |
address | String | "Schönhauser Allee 186a" |
city | String | "Berlin" |
postcode | String | "10119" |
latitude / longitude | Number | 52.529 / 13.409 |
isOpen | Boolean | true |
url | String | Public menu page on the brand site |
logoUrl | String | Restaurant logo image |
country | String | "de" |
scrapedAt | String | ISO timestamp |
Menu rows add: menuCategory, itemId, itemName, itemDescription, itemPrice, itemVariations (size/option prices when an item has several) and itemImageUrl.
Example output
{"restaurantId": "10015773","name": "Burger Vision Schönhauser Allee","brand": "lieferando","cuisines": ["Burgers", "100% Halal"],"rating": 4.3,"ratingCount": 2822,"deliveryFee": 1.49,"deliveryTimeMinutes": 45,"minimumOrderValue": 0,"currency": "EUR","address": "Schönhauser Allee 186a","city": "Berlin","postcode": "10119","latitude": 52.52937,"longitude": 13.409462,"isOpen": true,"url": "https://www.lieferando.de/speisekarte/vb-schnhauser-allee","country": "de","serviceType": "delivery","scrapedAt": "2026-07-11T19:30:48.335Z","menuCategory": "Smash Burger Menü","itemName": "Smash Beyond Cheese Menü","itemDescription": "Beyond Veggie Patty - Vision Potato Buns, mit Käse...","itemPrice": 15.5}
How to scrape Lieferando data
Lieferando.de is Germany's largest food delivery platform. To scrape it:
- Set
countrytode. - Set
locationto any German city (Berlin,Hamburg,München,Köln,Frankfurt) or a postcode like10115. - Run. A single Berlin run returns 1,600+ restaurants with ratings, delivery fees and minimum order values.
- Enable
scrapeMenusto also get every dish and its price — a typical restaurant has 50–200 menu items.
The same steps work for the other three brands — only the country value changes.
How to scrape Thuisbezorgd.nl data
Set country to nl and location to a Dutch city (Amsterdam, Rotterdam, Den Haag, Utrecht) or postcode. Amsterdam returns roughly 950 restaurants. Prices are in EUR.
How to scrape Pyszne.pl data
Set country to pl and location to a Polish city (Warszawa, Kraków, Wrocław, Gdańsk). Warsaw returns roughly 900 restaurants. Prices are in PLN.
How to scrape Takeaway.com Belgium data
Set country to be and location to a Belgian city (Brussel, Antwerpen, Gent, Liège — Dutch and French spellings both work). Brussels returns roughly 900 restaurants. Prices are in EUR.
How to scrape Lieferando.at (Austria) data
Set country to at and location to an Austrian city (Wien/Vienna, Graz, Linz, Salzburg, Innsbruck) or postcode. Vienna returns 1,300+ restaurants with full menu support (item prices, categories, size variations) — same data depth as Lieferando.de. Prices are in EUR.
Menu scraping
With scrapeMenus: true every restaurant produces one row per menu item: dish name, description, menu category, price and size variations. maxResults still caps the number of restaurants, so 20 restaurants with menus typically yields 1,000–3,000 rows. Long menu runs are resumable — if the platform migrates the run to another server, it continues where it left off instead of starting over.
Use cases
- Restaurant market analysis — cuisine mix, ratings distribution and delivery pricing per city
- Menu price monitoring — track dish prices across competitors over time
- Delivery fee benchmarking — compare fees and minimum order values across neighbourhoods
- Lead generation — restaurant lists with addresses and cuisines, filtered by city
- Market entry research — measure platform coverage in any German, Dutch, Polish or Belgian city
Tips
- One run covers one delivery point. For full-city coverage of a large metro, run a few postcodes spread across the city and deduplicate on
restaurantId. serviceType: "collection"returns the pickup-oriented view with collection open states.- Coordinates input (
"52.52,13.40") skips geocoding entirely — useful for automated pipelines. - Empty input works: it defaults to Lieferando.de around Berlin with 100 results.
Related scrapers
- Just Eat Scraper — restaurants from Just Eat in Ireland, Italy and Spain
- Wolt Scraper — restaurants and menus from Wolt across Northern and Eastern Europe
- Albert Heijn Scraper — the largest Dutch supermarket
- Tesco Scraper — full UK grocery catalogue with prices
- ASDA Scraper — UK grocery products and prices
- Waitrose Scraper — UK grocery with barcodes
- Picnic Scraper — Dutch online supermarket
Browse all our European scrapers on the Studio AMBA profile.
Legal
This actor scrapes publicly available data only. It does not log in, does not access personal data, and respects the platforms' public interfaces. You are responsible for how you use the data — check the applicable terms and laws for your use case.