Turn any Uber Eats restaurant page into a clean, flat menu table — one row per item with the price right at the top level. No nested JSON to dig through, no browser, no manual copy-paste.

What you get (one row per menu item)

field	description
restaurant_name	Store / restaurant name
section	Menu section breadcrumb (e.g. Breakfast > Featured items)
item_name	Menu item name
price_usd	Item price, top-level number (no nested offers.price)
price_currency	Currency code (e.g. USD)
description	Item description
image_url	Item photo
rating / num_ratings	Store rating + review count (repeated on every row)
cuisine	Cuisine tags
price_range	Store price range (e.g. $$)
phone, address, city, state, zip, country	Store location & contact
store_url, source_url	Links
parse_confidence, warnings	Structural-drift signal — you never pay for a broken parse
scraped_at	UTC timestamp

How to use

1. Open a restaurant on ubereats.com and copy the page URL (https://www.ubereats.com/store/{name}/{id}).
2. Paste one or more URLs into Store URLs.
3. Run. You get a flat table of every menu item with its price.

Input

• Store URLs — list of Uber Eats /store/ URLs.
• Max Menus — cap on number of menus (0 = unlimited).
• Delay between requests — politeness delay (default 1.5s).
• Proxy — Apify RESIDENTIAL by default (recommended for reliable access).

Pricing & reliability

• Billed per menu (one charge per restaurant successfully scraped), no matter how many items the menu has — far cheaper than tools that meter the same per-menu unit at a premium.
• You never pay for a failed parse: if the menu structure drifts, rows are returned with a low parse_confidence and the menu is not charged.
• Reliable access via residential proxy; runs entirely on the Apify platform.

More scrapers from our toolkit

Building a data pipeline? These actors pair well with this one — each runs on your own Apify account with the same pay-per-result pricing, no subscription:

• Vinted Marketplace
• Allegro Offers
• Amazon Products Scraper
• Autoscout24 Cars Scraper
• Competitor Price Monitor
• Craigslist Scraper

Chain any of them together from the Integrations tab (the Run succeeded trigger) to build a multi-step workflow — one actor's output feeds the next.

Usage statistics

This Actor creates a small, content-free summary at the end of each run. It is used only to monitor reliability and improve this Actor. A copy is saved as USAGE_STATS in your own Apify key-value store, so you can see the exact record created for your run.

Set disableUsageStats to true in the input to opt out. Nothing is sent then; your USAGE_STATS record only says that statistics were disabled.

Only these fields are recorded:

• schema version, Actor name and build number;
• UTC start and finish hour (not a precise timestamp);
• run duration, number of results and time to the first result, each as a coarse range;
• whether the result was empty, the end status, and an error type from a fixed list;
• memory setting and counts of charged events;
• names of the input fields you set, never their values;
• the selected option for input fields that offer a fixed list of choices (for example a sort order).

We do not collect input text, search terms, URLs, domains, usernames, email addresses, names, proxy credentials, tokens, scraped records, output items, raw error messages, stack traces, or hashes of any of those values. Records are kept for no longer than 13 months, used only as aggregated operational statistics, and never sold or shared.

Additional fields (Phase 2)

This Actor also records your Apify user ID, whether Apify marks the account as paying, the size range of list inputs, the selected country when the input offers a fixed list of countries, and one category from a fixed Actor taxonomy. We use these fields only for aggregate reliability, repeat-use and cross-Actor analysis; reports suppress any cell with fewer than five distinct users.

The same disableUsageStats: true input flag turns these fields off too. The user ID is removed after 13 months; we do not export, sell, share, or attempt to re-identify this data.

Run-outcome signals (v2)

To learn whether a run did what it was asked to do, the record also holds a few more coarse ranges and yes/no flags. None of them contains content:

• the result limit you asked for (a range, when the input has one) and what share of it was delivered;
• results delivered per input item you listed (a range);
• output quality as ranges: how fully the result fields were filled, the share of rows that look like errors, the share of duplicate rows, and how many different fields appeared. These are counted in memory while results are saved; no result content is kept;
• how the run was started (console, API, schedule, webhook, another Actor);
• how it ended: stopped by you, timed out, reached the requested limit, stopped by the charge limit, and how many times the platform moved the run;
• if this Actor reports it: how many items to process worked or failed (ranges) and one failure reason from a fixed list;
• a short code made from the names of the input fields you set, never their values.

Repeat-run fingerprint (v2)

When your Apify user ID is recorded (see above), the record also holds an 8-character one-way code made from your input (proxy settings left out) and this Actor's name. It only lets us see that the same account ran the same input again soon after an unsatisfying run; we never see the input itself. It is stored only in the database, never published, and reports use it in aggregate with the same five-user minimum. It is the one exception to the statement above that no hashes are collected, and disableUsageStats: true turns it off.