Uber Eats Menu Scraper

Built for pricing teams, F&B brands, dark kitchens, and market researchers — export every Uber Eats dish with section, price, and description in one flat spreadsheet.

No login. No API key. No Uber Eats account.

Item names, prices, menu sections, and store context ship on every row. Search by city, territory, or paste store URLs — same discovery as the Pro Scraper, but each dataset row is a menu item, not a CRM lead.

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Who is this for?

You are…	Typical goal	Suggested setup
Pricing / revenue ops	Benchmark competitor menu prices in a city	City or URLs
F&B brand / supplier	Map categories and price bands across a market	Territory + cuisine filter
Dark kitchen / franchise	Compare menus across locations of the same brand	URLs — bulk paste store links
Consultant / agency	Deliverable: full menu export for a client market	City + raise maxResults
Market research	Price distribution and category mix by geo	Territory with maxResults cap
Data / ops	Scheduled refresh on a known restaurant list	URLs mode

By default you get itemName, section, price, currency, storeName, and city — one row per dish (outputMode: flat). Descriptions and store ratings are included when Uber Eats publishes them on the store page.

Use deduplicateItems: true (default) when the same burger appears in “Featured” and “Burgers”. Turn it off only if you need every section occurrence for audit trails. Use outputMode: nested when you want one JSON object per restaurant with an items[] array (API workflows).

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What you get out of it

Benefit	Why it matters
Spreadsheet-ready pricing	Flat export — filter by sectionTop, pivot on itemName, compare price across storeName
Same discovery as Pro	City, territory, and URL modes — no second tool to list restaurants
Honest menu depth	Full item list from public store pages — not just min/max price bands
Geo context on every row	city, country, storeId — join runs or dedupe across exports
Transparent runs	Logs show progress %, ETA, and a downloadable RUN_LOG

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How much does it cost to scrape Uber Eats menus?

Pricing is pay-per-event (plus light HTTP compute — no proxy required for menus):

Event	Price	When charged
apify-actor-start	$0	Every run (no run fee)
uber-eats-menu-item	$0.003	Each menu row (flat mode)
uber-eats-store-with-menu	$0.03	One row per store (nested mode)
uber-eats-store-no-menu	$0.005	Store page returned no usable menu
apify-default-dataset-item	$0	Guard — avoids double-charging

Scenario	Listed PPE (items only)	Typical total
Try: 3 stores, ~300 items	~$0.90	~$0.95–1.05 with compute
15 stores, ~1 400 items (City Annecy)	~$4.20	~$4.50–5.50
1 000 menu items	~$3.00	~$3.50–4.50
10 000 menu items	~$30.00	~$32–40

HTTP-only runs keep compute low. There is no residential proxy on this Actor — menus are read from public store HTML.

Console setup (Monetization): set apify-actor-start and apify-default-dataset-item to $0; add uber-eats-menu-item ($0.003), uber-eats-store-with-menu ($0.03), uber-eats-store-no-menu ($0.005).

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Is scraping Uber Eats free?

Apify charges for Actor runs (PPE + compute). A 3-store Try (~300 menu rows) is usually about $1 all-in (items + light compute). There is no separate Uber Eats API fee — this Actor uses public store pages only.

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Is it legal to scrape Uber Eats?

This Actor only reads public restaurant and menu pages that Uber Eats shows to consumers. You are responsible for using the data in line with GDPR, local marketing laws, and Uber Eats' terms. Do not use scraped data for unlawful competitive practices or misrepresentation.

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Quick start

1. Open Input → leave Search mode on City (default)
2. Enter a city name (e.g. Annecy) — country is inferred automatically
3. Max restaurants defaults to 15; clear Cuisine filter to include all restaurants in the city
4. Leave Include descriptions on; keep Deduplicate items on for cleaner spreadsheets
5. Click Run → open Dataset → export CSV, JSON, or Excel

For a single competitor, switch to URLs mode and paste one store link.

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Search modes

City — One or more cities (default)

Best for local price benchmarks. Items from each restaurant appear as that store is scraped.

{
  "mode": "city",
  "cityNames": ["Annecy", "Paris"],
  "maxResults": 15,
  "categories": [],
  "includeDescriptions": true,
  "deduplicateItems": true,
  "outputMode": "flat"
}

Prefix a country to disambiguate: fr:Annecy, us:New York.

Territory — Country or region

Best for large market slices. Set a Max restaurants cap unless you need a full-country sweep.

{
  "mode": "territory",
  "country": "fr",
  "region": "fr:ara",
  "categories": ["burger"],
  "maxResults": 500,
  "outputMode": "flat"
}

Set maxResults: 0 for no restaurant ceiling (use a long Run timeout in Run options).

URLs — Specific restaurants

Best for head-to-head competitor menus.

{
  "mode": "urls",
  "startUrls": [
    { "url": "https://www.ubereats.com/fr/store/mcdonalds-epagny/b4043WTlR_iSrNu0-Y2DYQ" },
    { "url": "https://www.ubereats.com/fr/store/burger-king-epagny/ZOjYdsqgQcqPQ5q40GJoXQ" }
  ],
  "includeDescriptions": true,
  "outputMode": "flat"
}

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Input parameters

Field	Default	Description
mode	city	city, territory, or urls
cityNames	["Annecy"]	Cities for City mode
country / region	—	Territory mode
startUrls	—	Store URLs for URL mode
maxResults	15	Max restaurants per city or territory (0 = no cap)
categories	["fast-food"]	Cuisine filter; empty array = all cuisines
includeDescriptions	true	Item description text
deduplicateItems	true	Merge duplicate items across sections
outputMode	flat	flat = one row per item; nested = one row per store
sectionFilter	—	API-only: regex or string list to keep matching sections

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Output fields (flat mode)

Group	Fields
Store	storeName, storeUrl, storeId, storeSlug, city, country
Menu	section, sectionTop, itemName, description, price, currency
Context	rating, reviewCount, itemIndex, scrapedAt
Errors	error (only on failed stores in edge cases)

CSV uses semicolon separators when exported locally for Excel-friendly import.

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Typical coverage (reference runs)

Based on HTTP smoke runs on French store pages:

Field	Coverage
itemName	~100% of extracted items
price / currency	~100%
description	~95% (chains sometimes omit on promos)
section / sectionTop	~100%
Item images	Not exported in v1 (planned v1.1)
Modifiers / sizes / add-ons	Not in public JSON-LD — not exported

maxResults limits restaurants, not menu rows. One McDonald's location can yield 150–200+ items.

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Pricing & research workflows

Goal	Filter / columns	Suggested input
City price map	Group by storeName, compare price on itemName	City mode, categories: []
Category benchmark	Filter sectionTop = Burgers or Menus	City or URLs
Chain vs indie	Compare storeName across URLs	URLs mode, 5–10 stores
Deduped master menu	deduplicateItems: true	Default
Refresh cadence	Key on storeId + itemName + price	URLs list from Pro Scraper export

Spreadsheet tip: pivot on sectionTop and storeName; use price as values. Filter currency when mixing US and EU stores.

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How it works

1. Discover restaurants (city / territory listing, or your URLs)
2. Fetch each public store page (HTTP, browser-like headers)
3. Parse menu from structured page data (hasMenu → sections → items)
4. Normalize rows — optional dedupe and section filter
5. Export to Dataset (and local CSV on apify run)

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Output example

One menu item from a City · Annecy · 3 restaurants run:

{
  "storeUrl": "https://www.ubereats.com/fr/store/mcdonalds-epagny/b4043WTlR_iSrNu0-Y2DYQ",
  "storeId": "b4043WTlR_iSrNu0-Y2DYQ",
  "storeSlug": "mcdonalds-epagny",
  "storeName": "McDonald's® (Epagny)",
  "city": "EPAGNY",
  "country": "FR",
  "section": "NOUVEAUTÉS",
  "sectionTop": "NOUVEAUTÉS",
  "itemName": "BEEF MOZZA CHEDDAR",
  "description": "Pain spécial, fromage fondu, steak haché, préparation panée à base de mozzarella et de cheddar, tomate, salade, oignons, sauce.",
  "price": 14.7,
  "currency": "EUR",
  "rating": 3.9,
  "reviewCount": 4000,
  "itemIndex": 12,
  "scrapedAt": "2026-06-08T08:00:00.000Z"
}

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Limitations

• Modifiers, sizes, and add-ons are not exposed in public menu JSON — not exported in v1.
• Item images are only partially present in page HTML — full image pass planned for v1.1.
• Large territory runs without maxResults can take hours and produce hundreds of thousands of menu rows — set caps and timeout accordingly.
• Uber Eats may show promotional sections (e.g. “buy one get one”) — items can appear in multiple sections unless dedupe is on.

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Also available

• Uber Eats Pro Scraper — CRM-ready restaurant leads: phones, emails, company IDs, SIRET, territory search. Use Pro to list restaurants, then paste url values into this Actor's URLs mode for full menus.
• Uber Eats Reviews Scraper — monitor store ratings and latest public customer comments for the same store URLs (reputation snapshot, not full review history).

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Local development

npm install
npm run build:schema
npm run build
npm test
mkdir -p storage/key_value_stores/default
cp input.json storage/key_value_stores/default/INPUT.json
apify run

apify run validates storage/key_value_stores/default/INPUT.json against the input schema — include every field you need, or use apify run --input-file=./input.json.

Local runs write output.csv (UTF-8 BOM, semicolon-separated) next to the project root.

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Support

Contact corentin@outreacher.fr for custom Uber Eats pipelines, menu monitoring, or hub Actor integrations.