How to Scrape Uber Eats: Complete Guide for 2026

Why scrape Uber Eats?

Uber Eats publishes structured restaurant data across thousands of cities. Menus, prices, delivery fees, availability windows, and ratings change frequently. Scraping this data feeds several practical workflows.

Price monitoring. Restaurants adjust menu prices based on demand, ingredient costs, and seasonal promotions. Tracking these changes lets you build competitive intelligence dashboards or alert systems for price drops on specific items.

Market research. Aggregating restaurant density, cuisine types, and delivery zones across neighborhoods reveals gaps in food delivery coverage. Urban planners, real estate analysts, and food industry consultants use this data to identify underserved areas.

Menu data extraction. Food tech startups need structured menu data to train recommendation engines, build nutrition databases, or power recipe applications. Uber Eats provides one of the largest publicly accessible restaurant menus in North America and Europe.

Anti-bot challenges on ubereats.com

Uber Eats protects its pages with standard anti-bot measures. You will encounter JavaScript-rendered content, request fingerprinting, and rate limits tied to IP reputation.

The site loads restaurant listings and menu items dynamically through XHR calls. A simple HTTP GET returns a skeleton page with no useful data. You need a headless browser to execute the JavaScript and wait for the API responses to populate the DOM.

IP reputation matters. Uber Eats blocks datacenter IP ranges aggressively. Residential or mobile proxies work better, but managing a proxy pool at scale adds operational overhead. You also need to rotate User-Agent strings, handle cookies, and mimic realistic browsing patterns to avoid fingerprinting.

The anti-bot bypass API handles these challenges automatically. It rotates proxies, renders JavaScript, and escalates through scraping tiers based on the target site's defenses. You send a URL, you get back the rendered HTML or structured JSON. No proxy management, no CAPTCHA solving, no session handling.

Quick start with AlterLab API

Here is the fastest way to scrape a Uber Eats restaurant page. Install the SDK, authenticate, and send your first request.

For a full walkthrough of installation and configuration, see the getting started guide.

Python SDK

import alterlab

client = alterlab.Client("YOUR_API_KEY")
response = client.scrape(
    "https://www.ubereats.com/store/mcdonalds-store-id",
    formats=["json"],
    min_tier=3
)
print(response.json)

The min_tier=3 parameter skips basic HTTP scraping and goes straight to headless browser rendering. Uber Eats pages require JavaScript execution, so starting at tier 3 saves you a round-trip retry.

cURL

curl -X POST https://api.alterlab.io/v1/scrape \
  -H "X-API-Key: YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "url": "https://www.ubereats.com/store/mcdonalds-store-id",
    "formats": ["json"],
    "min_tier": 3
  }'

Node.js

const { AlterLab } = require("alterlab");

const client = new AlterLab("YOUR_API_KEY");
const response = await client.scrape({
  url: "https://www.ubereats.com/store/mcdonalds-store-id",
  formats: ["json"],
  min_tier: 3,
});

console.log(response.json);

The response returns the fully rendered page content. With formats=["json"], AlterLab attempts to extract structured data automatically, including any JSON payloads embedded in the page's initial state or API responses.

Extracting structured data

Uber Eats pages contain restaurant information embedded in the DOM and in JSON-LD structured data blocks. Here are the most reliable extraction targets.

CSS selectors for restaurant data

import alterlab
from bs4 import BeautifulSoup

client = alterlab.Client("YOUR_API_KEY")
response = client.scrape(
    "https://www.ubereats.com/store/restaurant-slug",
    min_tier=3
)

soup = BeautifulSoup(response.text, "html.parser")

# Restaurant name
name = soup.select_one("h1[data-testid='restaurant-name']")
print(name.text if name else "Not found")

# Rating and review count
rating = soup.select_one("span[data-testid='restaurant-rating']")
reviews = soup.select_one("span[data-testid='review-count']")

# Delivery fee
delivery_fee = soup.select_one("span[data-testid='delivery-fee']")

# Menu items
menu_items = soup.select("div[data-testid='menu-item']")
for item in menu_items[:5]:
    item_name = item.select_one("span[data-testid='item-name']")
    item_price = item.select_one("span[data-testid='item-price']")
    print(f"{item_name.text}: {item_price.text}")

JSON-LD extraction

Uber Eats embeds structured data using schema.org markup. This is often cleaner than parsing the DOM.

import alterlab
import json

client = alterlab.Client("YOUR_API_KEY")
response = client.scrape(
    "https://www.ubereats.com/store/restaurant-slug",
    min_tier=3,
    formats=["json"]
)

# If AlterLab extracted JSON-LD, it appears in the structured data
if response.json and "structured_data" in response.json:
    restaurant_data = response.json["structured_data"]
    print(json.dumps(restaurant_data, indent=2))
else:
    # Fallback: parse from raw HTML
    from bs4 import BeautifulSoup
    soup = BeautifulSoup(response.text, "html.parser")
    script = soup.find("script", type="application/ld+json")
    if script:
        print(json.loads(script.string))

Common data points and their locations

The data-testid attributes are stable across redesigns because Uber Eats uses them for internal testing. They are more reliable than class names, which change with each frontend deployment.

Common pitfalls

Rate limiting

Uber Eats throttles requests from single IPs. If you send more than 10-15 requests per minute from the same address, you will see HTTP 429 responses or CAPTCHA challenges.

Use rotating proxies or let the scraping API handle rotation for you. With AlterLab, proxy rotation happens automatically. You can also add delays between requests in your own code:

import alterlab
import time

client = alterlab.Client("YOUR_API_KEY")
urls = [
    "https://www.ubereats.com/store/restaurant-1",
    "https://www.ubereats.com/store/restaurant-2",
    "https://www.ubereats.com/store/restaurant-3",
]

for url in urls:
    response = client.scrape(url, min_tier=3)
    print(response.status_code)
    time.sleep(2)  # 2-second delay between requests

Dynamic content and lazy loading

Menu items on Uber Eats load as you scroll. The initial page render shows only the first few items. To get the full menu, you need to simulate scrolling or intercept the underlying API calls.

import alterlab

client = alterlab.Client("YOUR_API_KEY")
response = client.scrape(
    "https://www.ubereats.com/store/restaurant-slug",
    min_tier=3,
    wait_for="div[data-testid='menu-item']",
    timeout=15000
)

The wait_for parameter tells the headless browser to wait until menu items appear in the DOM before returning the page. This handles lazy-loaded content without manual scroll simulation.

Session and location handling

Uber Eats serves different content based on your geographic location. A restaurant page viewed from New York shows different availability and pricing than the same page viewed from London.

Set the headers parameter to include location-relevant headers, or use the proxy_country parameter if your scraping provider supports it:

import alterlab

client = alterlab.Client("YOUR_API_KEY")
response = client.scrape(
    "https://www.ubereats.com/store/restaurant-slug",
    min_tier=3,
    headers={
        "Accept-Language": "en-US,en;q=0.9",
    },
    proxy_country="US"
)

Without location context, you may get empty menus or "delivery not available" messages instead of actual restaurant data.

Scaling up

Scraping a single restaurant page is straightforward. Scraping thousands of restaurants across multiple cities requires a different approach.

Batch processing

Process URLs in parallel using async requests or worker queues. AlterLab supports concurrent requests with automatic rate limiting on the provider side.

import alterlab
import asyncio

async def scrape_restaurant(client, url):
    return await client.scrape_async(url, min_tier=3)

async def main():
    client = alterlab.AsyncClient("YOUR_API_KEY")
    urls = [f"https://www.ubereats.com/store/{slug}" for slug in restaurant_slugs]
    results = await asyncio.gather(*[scrape_restaurant(client, u) for u in urls])
    return results

asyncio.run(main())

Scheduling recurring scrapes

Restaurant menus and prices change. Set up scheduled scrapes to track changes over time.

import alterlab

client = alterlab.Client("YOUR_API_KEY")
schedule = client.schedules.create(
    url="https://www.ubereats.com/store/restaurant-slug",
    cron="0 8 * * *",  # Daily at 8 AM UTC
    min_tier=3,
    formats=["json"],
    webhook_url="https://your-server.com/webhook/ubereats"
)
print(f"Schedule created: {schedule.id}")

This runs the scrape daily and pushes results to your webhook endpoint. You can diff consecutive runs to detect menu changes, price updates, or restaurant closures.

Monitoring and change detection

Use the monitoring feature to track when specific data points change on a page.

import alterlab

client = alterlab.Client("YOUR_API_KEY")
monitor = client.monitors.create(
    url="https://www.ubereats.com/store/restaurant-slug",
    check_interval="6h",
    selectors=["span[data-testid='item-price']"],
    notify_on_change=True,
    webhook_url="https://your-server.com/webhook/price-alerts"
)

This checks the page every 6 hours and triggers your webhook when any menu item price changes. Useful for competitive intelligence and dynamic pricing analysis.

Cost management at scale

Cost scales with request volume and tier level. Uber Eats pages require tier 3 (headless browser) at minimum. If you encounter CAPTCHAs, the system auto-escalates to higher tiers.

Check AlterLab pricing for current per-request rates. You can set spend limits on API keys to prevent budget overruns. For large-scale operations, batch your requests during off-peak hours and use caching to avoid re-scraping unchanged pages.

Key takeaways

Uber Eats requires headless browser rendering due to JavaScript-loaded content. Start with min_tier=3 to skip unnecessary retries.

Use data-testid CSS selectors for stable extraction. They change less frequently than class names. JSON-LD blocks provide cleaner structured data when available.

Handle rate limiting with delays or let your scraping API rotate proxies automatically. Set location headers to get accurate menu availability and pricing.

Scale with async batch requests, scheduled recurring scrapes, and webhook-based change detection. Set spend limits on API keys to control costs.

Related guides

• How to Scrape DoorDash
• How to Scrape Amazon
• How to Scrape eBay