Uber Eats Scraper — Restaurants, Menus & Prices

Scrape Uber Eats the way a customer sees it at a given address: which restaurants, grocery and convenience stores deliver there, with rating, rating count, ETA, delivery fee, offers and availability, plus each store's cuisines, price level, address and coordinates, opening hours, phone and review snippets. In menus mode it outputs every menu item of a store with its price, was-price, offer, availability, popular flag and (optionally) all option groups with their extra prices. Turn on Only new or changed items and schedule it to track menu prices over time.

No login, no browser, no API key. Works in every Uber Eats country (US, Canada, UK, France, Australia, Mexico, Japan, ...).

What data does Uber Eats Scraper extract?

Stores (one row per store, type: "store"):

Field	Example
name, url, storeUuid	Art of Pizza, https://www.ubereats.com/store/art-of-pizza-state-%26-polk/gtJCq6f1Rhi0KtNllYN0BQ
rating, ratingCount, ratingCountText	4.8, 1500, "1,500+"
etaMinMinutes, etaMaxMinutes, etaText	23, 41, "23 min"
deliveryFee, deliveryFeeText, currency	2.99, "$2.99 Delivery Fee", "USD" (when the site shows it for the address)
promotion, promotions, badges	"$5 off $25+", ["$5 off $25+"], ["Only on Uber Eats"]
availability, isOrderable, isOpen	"ACCEPTING_ORDERS", true
priceLevel, cuisines *	"$$", ["Pizza", "Italian"]
address, street, city, region, postalCode, country, latitude, longitude *	727 S State St, Chicago, IL 60605 ...
phone, hours, hoursTagline *	"+13128775335", [{"days": "Every Day", "open": "11:00", "close": "22:00"}]
reviews *	[{"text": "Best deep dish...", "reviewerName": "Tina F.", "date": "2025-11-05"}]
menuCount, menuItemCount, chainName, distanceText, diningModes *	1, 128, null, "0.9 mi", ["DELIVERY", "PICKUP"]
imageUrl, rank, searchTerm, deliveryAddress, scrapedAt	position in the site's list for the search

* with Include store details (default on).

Menu items (one row per item, type: "menuItem"): id (store UUID + item UUID), name, description, price, originalPrice (struck-through was-price on discounted items), priceText, currency, promotion ("BOGO", "16% off", "3 for $17.70"), isAvailable, isSoldOut, isPopular and popularLabel ("#1 most liked", "Popular", "Featured"), hasOptions, category ("Sides"), menu ("Lunch/Dinner") and menuHours, imageUrl, position, store fields (storeName, storeUrl, storeRating, storeAddress, storeCity) and, with Include item options, optionGroups: [{"name": "Sauce", "required": false, "minSelections": 0, "maxSelections": 1, "options": [{"name": "Ketchup", "price": 1.0, "isSoldOut": false}]}].

How to scrape Uber Eats restaurants near an address

1. Mode Restaurants / stores delivering to an address.
2. Delivery address: a street address, ZIP/postcode or city. Add the city and, outside the US, the country (10 Downing Street, London) so the right place is chosen.
3. Optional Search terms: a cuisine (pizza, sushi), dish or brand. Empty = every store in the address's delivery feed (often 300-1,500 stores in big cities).
4. Optional Sort (recommended, highest rating, fastest delivery) and the site's filters: Minimum rating, Price levels, Only stores with offers.

Delivery fee, ETA and the list of stores are the ones Uber Eats shows for that address at the time of the run (fees are often hidden while a store is closed).

How to scrape an Uber Eats menu with prices

Mode Menus with item prices, paste one or more Store URLs (any country). Every item of every menu (breakfast, lunch, ...) is output once, in menu order, with its own category; items repeated in "Featured items" / "Most popular" rows are flagged isPopular. Turn on Include item options to add sizes, toppings and add-ons with their prices (one extra request per item that has options). Without Store URLs, menus mode reads the first Max stores stores found for the address and search terms.

Grocery and convenience stores (supermarkets, pharmacies) return their storefront rows (best sellers, deals, featured aisles) rather than the whole catalogue.

Input example

{
  "mode": "stores",
  "address": "233 S Wacker Dr, Chicago, IL 60606",
  "searchTerms": ["pizza"],
  "sort": "rating",
  "minRating": "4.5",
  "maxItems": 100,
  "includeDetails": true
}

{
  "mode": "menus",
  "storeUrls": ["https://www.ubereats.com/store/sizl-3517-n-spaulding-ave/uFeEbuTxU9eP38C1XBqcvg"],
  "includeOptions": true,
  "maxItems": 500
}

Output example

{
  "type": "store",
  "storeUuid": "71db3bee-8aa9-587a-bbc1-d4acedd05adf",
  "name": "Nonnina",
  "url": "https://www.ubereats.com/store/nonnina-new/cds77oqpWHq7wdSs7dBa3w",
  "rating": 4.9,
  "ratingCount": 32,
  "ratingCountText": "32",
  "priceLevel": null,
  "cuisines": ["Italian", "Pasta", "Comfort Food", "Pizza"],
  "etaMinMinutes": 14,
  "etaMaxMinutes": 31,
  "etaText": "14 min",
  "deliveryFee": null,
  "currency": "USD",
  "promotion": null,
  "availability": "NOT_ACCEPTING_ORDERS",
  "distanceText": "0.7 mi",
  "address": "340 North Clark Street, Chicago, IL 60654",
  "city": "Chicago",
  "latitude": 41.8887,
  "longitude": -87.6312,
  "phone": "+13128220077",
  "hours": [{"days": "Monday - Saturday", "open": "14:45", "close": "22:30"}],
  "menuItemCount": 44,
  "searchTerm": "pizza",
  "rank": 1,
  "deliveryAddress": "233 S Wacker Dr, Chicago, IL 60606-6300, USA",
  "scrapedAt": "2026-10-10T06:00:53+00:00"
}

{
  "type": "menuItem",
  "id": "b857846e-e4f1-53d7-8fdf-c0b55c1a9cbe:5fd8e592-40b1-5b0b-9948-cca9a57eff75",
  "name": "French Fries",
  "description": "Crispy golden French fries, hot and salty, perfect for a savory snack or a tasty side.",
  "price": 3.99,
  "originalPrice": null,
  "priceText": "$3.99",
  "currency": "USD",
  "promotion": null,
  "isAvailable": true,
  "isPopular": true,
  "popularLabel": "Featured",
  "hasOptions": true,
  "category": "Sides",
  "menu": "Sizl Menu",
  "menuHours": "4:00 PM – 11:45 PM",
  "storeName": "Sizl (3517 N Spaulding Ave)",
  "storeUrl": "https://www.ubereats.com/store/sizl-3517-n-spaulding-ave/uFeEbuTxU9eP38C1XBqcvg",
  "optionGroups": [{"name": "Sauce", "required": false, "minSelections": 0, "maxSelections": 1,
                    "options": [{"name": "Ketchup", "price": 1.0}, {"name": "Garlic Sauce", "price": 2.0}]}]
}

Monitor changes on a schedule

Turn on Only new or changed items and the Actor remembers what earlier runs of the same input returned. In menus mode each run outputs only menu items that are new or whose price, was-price, offer or availability changed (a price increase, an item selling out, a new BOGO deal); in stores mode only new stores or stores whose rating, delivery fee or offer changed. Unchanged rows are dropped before they are saved (and before option groups are fetched), so they are not charged. Save the input as a task (for example your competitors' store URLs with Max results large enough to cover their menus), then add a Schedule in Apify Console (Schedules → Create new, e.g. daily). Output rows carry changeType (new or changed), and changed ones carry previousValues, e.g. {"price": 14.99} for an item whose price went from $14.99 to its new price. Use Monitoring key to keep several schedules on the same input separate.

Does it work without login or a browser?

Yes. It reads the same JSON the Uber Eats website loads, with plain HTTP requests: no account, no browser, no Uber API key. Each worker keeps its own cookies on one proxy IP, like a single visitor.

Proxy note

The default Apify Proxy (datacenter) works and is the cheapest option. Each run makes one request per 80 stores of a search, one per store page (details or menu) and one per item with options. If a run reports bot-defense errors, switch the proxy to residential.

How much does it cost to scrape Uber Eats?

Pay per result: $0.002 per store, $0.0015 extra per store page opened (store details), $0.0007 per menu item. 100 stores with details cost $0.35; a 120-item menu costs $0.084. Platform usage (compute, proxy) is billed separately and is small (a few cents per thousand rows). With monitoring on, only new or changed rows are charged.

Is it legal to scrape Uber Eats?

The Actor collects only what Uber Eats shows publicly to every visitor without logging in: store listings, menus and prices, business contact details (phone, address) and the public review snippets shown on store pages with the reviewer's first name and initial. Nothing behind a login, no customer accounts, no orders. Review texts and reviewer names can be personal data in some jurisdictions (GDPR, CCPA): have a legitimate purpose, keep only what you need, and check Uber's terms and your local law before reusing the data. If you are unsure, consult a lawyer.

Support

Missing a field or found a store that does not parse? Open an issue on the Actor page with the store URL and the run ID.