Uber Eats Scraper — Restaurants, Menus, Prices & Grocery

Scrape Uber Eats restaurants and stores for any address, postal code, neighbourhood or city — or straight from store links — and get complete menus with prices, opening hours, phone numbers, full addresses with coordinates, ratings, customer reviews, delivery fees, ETAs and promotions.

It goes beyond restaurants: turn on the aisle crawl to collect the full product catalogue and shelf prices of grocery, pharmacy, convenience, alcohol and retail stores on Uber Eats (Walmart, Shoppers Drug Mart, T&T, 7-Eleven, Rexall, Dollarama, local grocers…).

Works in Canada, the United States, the United Kingdom and other Uber Eats markets, with prices in local currency. No Uber Eats account needed.

What you get

For every store	For every menu item / product
Name, store link, chain	Name, description, photo
Store type (restaurant, grocery, pharmacy, convenience, alcohol, retail…)	Price (number + formatted), currency
Rating, exact rating, number of ratings	Regular price when on sale + deal tags ("52% off", "2 for $7", "BOGO")
Cuisines, price range ($–$$$$)	Category / aisle, pack size (groceries)
Phone number	Calories
Street, city, region, postal code, country, neighbourhood	Sold-out / available flags
Latitude / longitude	Has options (sizes, add-ons)
Opening hours (per menu, e.g. breakfast / dinner)	Optional: every option group with extra prices, nutrition
Open now, ETA, distance, delivery fee text
Promotions ("Buy 1, get 1"…), badges ("Great value", new, exclusive)
Menu categories with item counts
Recent reviews: text, date, reviewer first name + initial

How to use

All stores near an address or postal code — the simplest run:

{ "locations": ["M5V 3L9"], "maxStoresPerSearch": 100 }

Keyword search in several cities:

{ "locations": ["Toronto, ON", "Vancouver, BC", "Brooklyn, NY"], "searchTerms": ["sushi", "ramen"], "maxStoresPerSearch": 80 }

Every store in a city — a single address shows at most ~500 stores, so city-wide coverage searches a grid of points (about 3 km apart) and merges the results without duplicates:

{ "locations": ["Toronto City Hall"], "coverage": "cityWide", "radiusKm": 10, "maxStoresPerSearch": 0 }

Specific stores by link:

{ "storeUrls": ["https://www.ubereats.com/ca/store/papa-johns-pizza-200-dundas-st-e/AWUBSjLNT6yLE-wVBC63qA"] }

Grocery / pharmacy shelf prices as a flat price list:

{
  "locations": ["M5V 3L9"],
  "storeTypes": ["grocery", "pharmacy", "convenience"],
  "includeGroceryAisles": true,
  "maxItemsPerStore": 2000,
  "outputFormat": "menuItems"
}

Input options

• Locations — address, postal/ZIP code, neighbourhood, city, landmark or "lat,lng". Use Country hint for ambiguous inputs (bare 5-digit codes are treated as US ZIP codes; Canadian and UK postcodes are recognised automatically).
• Search terms — optional keywords; empty = every store delivering to the location.
• Store links — scrape specific stores directly. Uber Eats city pages (e.g. ubereats.com/ca/city/toronto-on) are accepted too and searched as a location.
• Coverage — nearby (one point) or cityWide (grid within radiusKm).
• Store types — keep only restaurants, grocery, pharmacy, convenience, alcohol, retail, specialty food, pet supply or florists.
• Dining mode — delivery or pickup.
• Include menu / reviews / item options, Crawl grocery aisles with a per-store product cap.
• Output format — one row per store (menu nested inside) or one row per menu item (store name, address and coordinates on every row — ready for spreadsheets).
• Max results, Max concurrency, Proxy (datacenter by default; switches to residential automatically if needed).

Output example (one row per store, shortened)

{
  "dataType": "store",
  "storeUuid": "3c718ac9-75de-4477-9a4e-188d98a0823d",
  "name": "McDonald's (Queen & Spadina)",
  "url": "https://www.ubereats.com/ca/store/mcdonalds-queen-%26-spadina/PHGKyXXeRHeaThiNmKCCPQ",
  "storeType": "restaurant",
  "chain": { "uuid": "d8fb9d71-d641-43e1-901c-5f36a182c2d9", "name": "McDonald's" },
  "cuisines": ["Burgers", "Fast Food", "Chicken", "Canadian"],
  "priceRange": "$",
  "rating": 4.5,
  "ratingExact": 4.4852,
  "reviewCount": 20000,
  "reviewCountText": "20,000+",
  "isOpen": true,
  "hoursText": "Open until 3:49 a.m.",
  "hours": [{ "days": "Every Day", "periods": [
    { "open": "04:00", "close": "10:49", "label": "Breakfast Menu" },
    { "open": "11:00", "close": "23:49", "label": "Lunch & Dinner Menu" }
  ] }],
  "phone": "+14167037401",
  "address": { "street": "160 Spadina Ave", "city": "Toronto", "region": "ON", "postalCode": "M5T", "country": "CA", "full": "160 Spadina Ave, Toronto, ON M5T" },
  "neighborhood": "Fashion District",
  "latitude": 43.6487706,
  "longitude": -79.3966784,
  "currency": "CAD",
  "etaText": "12–24 Min",
  "etaMinMinutes": 12,
  "etaMaxMinutes": 24,
  "distanceText": "1 km",
  "deliveryFeeText": "$0 delivery fee (new users)",
  "promotions": [],
  "menuItemCount": 169,
  "menuCategories": [{ "name": "Featured items", "itemCount": 9 }, { "name": "Most Popular", "itemCount": 9 }],
  "menu": [
    {
      "itemUuid": "27f17e03-8e76-5a0f-bec2-7f1add570cbd",
      "name": "Big Mac [560.0 Cals]",
      "description": null,
      "price": 11.19,
      "priceCents": 1119,
      "priceText": "$11.19",
      "calories": "560",
      "imageUrl": "https://tb-static.uber.com/prod/image-proc/processed_images/47a2af73e7fc83efe6c3b394bfea438d/a19bb09692310dfd41e49a96c424b3a6.jpeg",
      "isSoldOut": false,
      "hasOptions": true,
      "category": "Individual Items",
      "categories": ["Featured items", "Individual Items"]
    }
  ],
  "reviews": [
    { "text": "Hamburgers typically have 2 buns (1 on the top and one on the bottom). Would be great to have both next time!", "author": "Daniel K.", "date": "2026-08-16", "timeAgo": "1 month ago", "isFeatured": true }
  ],
  "searchLocation": { "query": "M5V 3L9", "latitude": 43.6425558, "longitude": -79.3871029 },
  "scrapedAt": "2026-09-24T05:36:05.078Z"
}

With one row per menu item, each row is an item (name, price, originalPrice, dealTags, currency, category, aisle, size, calories, description, isSoldOut, imageUrl) plus storeName, storeUrl, storeType, storeAddress, storeCity, storeLatitude, storeLongitude and storeRating.

Use cases

• Menu & price monitoring — track competitors' menu prices, new items and promotions by city or chain.
• Grocery & CPG price intelligence — shelf prices and pack sizes of the same product across stores and banners; see which stores carry a product.
• Lead generation — restaurants and local shops with phone number, full address, cuisine, rating and popularity.
• Market research — restaurant density, cuisine mix, price ranges and ratings by neighbourhood; delivery coverage maps from coordinates.
• Review analysis — recent customer reviews for sentiment and quality tracking.
• Menu data for apps — nutrition, calories, options and photos.

Tips

• One location returns up to ~500 stores (the most Uber Eats lists for a single address). For more, use city-wide coverage.
• ETA, distance and delivery fee are relative to the location you search from. Store-link runs without a location leave them empty.
• Grocery and retail storefronts only show a first page of products; enable Crawl grocery aisles for the full catalogue.
• Reviews: Uber Eats shows up to ~180 recent reviews per store; reviewer names are first name + initial as shown on the site.

FAQ

Do I need an Uber Eats account or API key? No.

Which countries work? Canada, the US and the UK are tested; other Uber Eats markets (e.g. Australia, Mexico, Japan) work the same way with local currency.

How fresh is the data? Everything is fetched live when you run the scraper.

Why is a field sometimes empty? Not every store publishes everything (e.g. some have no phone number or price range). Fees and ETAs depend on the location and time of day.

Do I need residential proxies? No. Apify's datacenter proxies, which every plan includes, work most of the time. When Uber Eats starts blocking datacenter IPs, the actor switches to residential proxies by itself, if your plan has them. If it doesn't, the run keeps going on datacenter IPs, and when many requests were blocked the status message starts its hint with [NO_RESIDENTIAL]: results may be partial, and enabling residential proxies in Apify Console → Proxy fixes it.

How fast is it? About 5 stores per second with full menus and reviews at default concurrency — e.g. 360 stores across Toronto, Vancouver and New York in about 75 seconds.

How much does it cost? Pay only for results — see the Pricing tab. You pay per store saved (its menu, hours and reviews are included); flat menu-item rows, extra grocery products from the aisle crawl and item options are optional add-ons. Stores that fail to load are never charged, and a run that finds nothing costs nothing.

Is it legal to scrape Uber Eats? This scraper collects only publicly available data — the same store listings, menus, prices, hours and reviews that anyone can see on ubereats.com without logging in. Reviews show the reviewer's first name and last initial, which can count as personal data in some places, so if you store or use it, have a legitimate reason and follow data protection laws such as GDPR, PIPEDA and CCPA, as well as Uber Eats' terms. If you're unsure about your use case, check with a lawyer. More background: Is web scraping legal?

Does it access any private data? No. It never logs in and uses no Uber Eats account, cookies or personal session. It only reads what the public Uber Eats website shows every visitor for an address or store link: stores, menus, prices, hours, business phone numbers and public reviews (first name + initial, as displayed). No orders, customer accounts, email addresses or payment details are ever accessed. Turn off Include reviews if you don't need reviewer names at all.

Location lookup uses OpenStreetMap data © OpenStreetMap contributors.