Uber Eats Scraper

🍔 Uber Eats Scraper

Scrape Uber Eats restaurants, grocery and convenience stores near any address, by keyword or store type, or straight from store URLs. Every store comes with its full menu (sections, items, prices, discounts, item ratings and, if you want them, all item options), plus rating, delivery fee, ETA, opening hours, phone, address and reviews. No login, no browser, and 10 stores with full menus take only a few seconds.

✨ Why use Uber Eats Scraper?

• ⚡ Fast — reads Uber Eats' own JSON data instead of rendering pages, so a store with a 100-item menu takes well under a second.
• 🍕 Full menus — every section and item with price, original price before a discount, description, image, calories, "% liked" rating and promotion tags.
• 🧩 Item options — optional sizes, toppings and add-ons with their prices for every item that has them.
• 💸 Fees and timing — delivery fee text and amount, ETA range, distance, opening hours per day, open/closed state.
• 🌍 Any Uber Eats country — US, UK, Canada, Australia, France, Mexico, Japan and more; the right currency and locale are picked from the address or URL.
• 🔓 No login needed — only public store pages are used.

🎯 Use cases

• 📈 Menu price monitoring — track what restaurants charge on Uber Eats and spot discounts over time.
• 🧭 Market research — map every restaurant, grocery or pharmacy that delivers to an address, with cuisines, price level and ratings.
• 🤝 Lead generation — build lists of restaurants with phone numbers, addresses and chain information.
• 🏷️ Competitor analysis — compare menus, delivery fees and promotions of competing restaurants in a neighbourhood.
• 🍽️ Food data projects — collect dishes, descriptions, images and option trees for apps and datasets.

🚀 How to use

1. Enter a delivery address (or a city or postcode).
2. Optionally add search keywords such as pizza, sushi or a store name. Leave it empty to list all stores Uber Eats shows for the address, or pick a store type like Grocery or Pharmacy.
3. Or paste store URLs to scrape specific stores.
4. Set Max stores per keyword, then click Start.
5. Download the results as JSON, CSV, Excel or HTML, or read them through the API.

📥 Input

{
  "address": "1600 Pennsylvania Avenue, Washington DC",
  "searchQueries": ["Pizza"],
  "maxStores": 10,
  "startUrls": [{ "url": "https://www.ubereats.com/store/dc-pizza/7jqs2jNeR1OT-9k70p3B2w" }],
  "includeMenuCustomizations": false
}

📤 Output

One row per store.

{
  "uuid": "ee3aacda-335e-4753-93fb-d93bd29dc1db",
  "title": "DC Pizza",
  "url": "https://www.ubereats.com/store/dc-pizza/7jqs2jNeR1OT-9k70p3B2w",
  "phoneNumber": "+12023311800",
  "ratingValue": 4.8,
  "ratingCount": "2,000+",
  "address": "1103 19th St Nw, Washington, DC 20036",
  "location": { "city": "Washington", "region": "DC", "postalCode": "20036", "country": "US", "latitude": 38.9040182, "longitude": -77.0432506 },
  "priceBucket": "$$",
  "cuisineList": ["Vegetarian", "Italian", "Pizza", "Salads", "Group Friendly"],
  "isOpen": true,
  "workingHoursTagline": "Open until 10:00 PM",
  "etaRange": "Delivered in 14 to 30 min",
  "etaMinutes": { "min": 14, "max": 30 },
  "deliveryFee": { "amount": 0.0, "currencyCode": "USD", "text": "$0 delivery fee (new users)" },
  "distance": "0.6 mi",
  "currencyCode": "USD",
  "hours": [{ "dayRange": "Sunday", "sectionHours": [{ "startTime": 615, "endTime": 1260, "sectionTitle": "Online Ordering" }] }],
  "menuItemCount": 41,
  "minItemPrice": 1.55,
  "averageItemPrice": 45.21,
  "menu": [{
    "title": "Wings (Oven Roasted)",
    "itemCount": 3,
    "items": [{
      "title": "10 pc Wings (Oven Roasted)",
      "description": "Dressing is always on the side. MUST Choose 2",
      "price": 16.54,
      "originalPrice": null,
      "priceText": "$16.54",
      "imageUrl": "https://tb-static.uber.com/prod/image-proc/processed_images/74abe0a444ccb2d6bae42679b0335681/c67fc65e9b4e16a553eb7574fba090f1.jpeg",
      "isSoldOut": false,
      "hasCustomizations": true,
      "rating": "100%",
      "numRatings": 7
    }]
  }],
  "storeReviews": [{ "text": "Best pizza I've ever had.", "eaterName": "Jamie R.", "createdAt": "2021-09-05T00:00:00Z" }],
  "searchQuery": "Pizza",
  "position": 4,
  "scrapedAt": "2026-10-15T20:49:19.729Z"
}

💰 Pricing

You pay $4.00 per 1,000 stores ($0.004 per store), each with its full menu, plus a little platform usage (compute and proxy traffic). A typical run of 1,000 stores with menus costs about $4.10 in total. Item options are included at no extra event cost; they only add a bit of proxy traffic.

❓ FAQ

Is it legal to scrape Uber Eats? The actor only reads public store pages that anyone can open without an account. Check the site's terms and your local laws for your use case, and handle any personal data (such as reviewer first names) responsibly.

Do I need an Uber Eats account or my own proxy? No. No login is used, and proxies are included. Datacenter addresses are the default; if they get blocked the run switches to residential addresses in the address's country by itself.

How many stores can I get? As many as Uber Eats lists for the address and keyword. Busy city addresses usually list several hundred stores; the run stops cleanly when the list ends and says how many there were.

Why is a field empty? Uber Eats does not show every field for every store, for example the delivery fee for closed stores or calories for most non-US items. Empty values are null.

Can I scrape stores outside the US? Yes. Enter an address in any Uber Eats country or paste store URLs with any country prefix; prices come in the local currency.

Can I schedule it or use the API? Yes. Schedule runs in the console, or start them over the API and fetch the dataset as JSON or CSV.

⚠️ Limits

• Store types other than restaurants are best effort: Uber Eats' public search has no strict store-type filter, so the actor searches by the type's name and some unrelated stores can appear.
• Very large grocery catalogs that Uber Eats paginates include only the first catalog page (the row has a menuNote when that happens).
• Item option trees are included two levels deep (groups, options and their direct sub-options).
• Individual reviews show the text, reviewer first name and date; Uber Eats does not publish a star rating per review.
• Fees and promotions are the ones Uber Eats shows to a new, logged-out visitor.