🍴 Uber Eats Scraper [PPR]

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Fast, cost-effective Uber Eats scraper that turns a search query + location — or a list of store URLs — into clean, structured store and menu data. Built for scale, with an automatic anti-block proxy ladder and pay-per-result pricing, so you only pay for stores you actually get.

✨ Why Choose Us?

• 🎯 Two ways to scrape — search by address (+ optional query) or paste store URLs directly.
• 🛡️ Self-healing connection — starts direct (cheapest), and only escalates to datacenter → residential proxies when Uber Eats blocks. No fiddly config.
• 🍕 Deep menu data — full menu sections, items, prices, images, and optional customizations (modifiers / add-ons).
• ⚡ Live results — every store is saved the instant it's collected; a crash never loses progress.
• 💸 Pay per result — billed per store, not per minute.

🔑 Key Features

📥 Input

Choose one mode. If urls is provided, address and query are ignored.

{
"locale":"en-US",
"address":"1600 Pennsylvania Avenue, Washington DC",
"query":"Pizza",
"storeType":"RESTAURANTS",
"maxRows":10,
"urls":[],
"getMenuCustomizations":false,
"proxyConfiguration":{"useApifyProxy":true},
"concurrency":5,
"maxRetries":3
}

📤 Output

Each dataset item is one store:

{
"title":"District Detroit Pizza",
"sanitizedTitle":"District Detroit Pizza",
"phoneNumber":"+12025551234",
"emails":[],
"cuisineList":["Pizza"],
"location":{"address":"…","city":"Washington","country":"US","latitude":38.9,"longitude":-77.0},
"currencyCode":"USD",
"rating":{"ratingValue":4.8,"reviewCount":"260+"},
"storeAvailablityStatus":"AVAILABLE",
"isOpen":true,
"etaRange":"Delivered in 20 to 30 min",
"hours":[ … ],
"categories":["$","Pizza"],
"categoriesLink":[ … ],
"menu":[{"catalogName":"Pizzas","catalogItems":[ … ]}],
"logoImageUrl":"https://…",
"heroImageUrl":"https://…",
"uuid":"…",
"url":"https://ubereats.com/store/…",
"merchantType":"MERCHANT_TYPE_RESTAURANT",
"distance":{"text":"2.2 mi","accessibilityText":"2.2 miles"},
"supportedDiningModes":[ … ],
"storeReviews":[ … ],
"featuredReviews":[ … ]
}

🚀 How to Use (Apify Console)

1. Log in at console.apify.com → Actors.
2. Open 🍴 Uber Eats Scraper [PPR].
3. Either enter an address (and optional query + store type), or paste store URLs.
4. Click Start.
5. Watch stores stream into the Output tab in real time.
6. Export to JSON / CSV / XLSX / Excel when the run completes.

🤖 Use via API

curl-X POST "https://api.apify.com/v2/acts/<ACTOR_ID>/run-sync-get-dataset-items?token=$APIFY_TOKEN"\
-H"Content-Type: application/json"\
-d'{"address":"1600 Pennsylvania Avenue, Washington DC","query":"Pizza","storeType":"RESTAURANTS","maxRows":10}'

🎯 Best Use Cases

• 📊 Competitor menu & price monitoring
• 🍽️ Restaurant & store discovery / market research
• 📈 Lead lists of local merchants by category and area
• 🗺️ Delivery coverage and ETA analysis across locations

💰 Pricing

Pay per result — you are billed a flat price for each store written to the dataset (the apify-default-dataset-item event). No charge for stores that fail to collect. Failed targets never cost you anything; partial runs still return everything collected so far.

❓ FAQ

Do I need a proxy? No. The scraper starts direct and only uses Apify Proxy if Uber Eats blocks it.

Search returns fewer than maxRows? Uber Eats only lists so many stores for a given area/query — that's the ceiling, not a bug.

Why are some menus empty? Some stores (e.g. certain alcohol/convenience listings) expose no public catalog; the store record is still returned.

Which countries are supported? Anywhere Uber Eats operates — pick the matching locale (55+ supported).

🛟 Support & Feedback

Open an issue on the Actor's Issues tab in Apify Console with your run ID and input, and we'll take a look.

Data is collected only from publicly available Uber Eats pages. You are responsible for compliance with applicable laws and Uber's Terms of Service.