How to Scrape Uber Eats Data: Complete Guide for 2026

Disclaimer: This guide covers extracting publicly accessible data. Always review a site's robots.txt and Terms of Service before scraping.

TL;DR

To scrape Uber Eats restaurant data, use AlterLab's API with Python or Node.js to handle anti-bot protections automatically. Target public menu pages, extract structured fields like item names and prices via CSS selectors or Cortex AI, and scale responsibly with rate limiting. Start at T1 tier—the API promotes to T3/T4 as needed for JavaScript-dependent content.

Why collect food data from Uber Eats?

Food delivery data powers competitive intelligence for restaurants and analysts. Track real-time pricing trends across cuisines to adjust your menu strategy. Monitor competitor promotions and new dish launches for market timing. Aggregate nutritional information for dietary app development or supply chain forecasting.

Technical challenges

Uber Eats implements standard anti-bot systems: rate limiting by IP, User-Agent validation, and JavaScript challenges that block headless browsers without proper fingerprinting. Raw HTTP requests (T1/T2) typically return CAPTCHAs or empty responses for menu pages. AlterLab's Smart Rendering API manages proxy rotation, realistic headers, and headless Chrome instances to access public food data while respecting site protections.

Quick start with AlterLab API

See the Getting started guide for SDK setup. Below are examples scraping a public Uber Eats restaurant menu page (replace YOUR_API_KEY and the URL with your target).

import alterlab

client = alterlab.Client("YOUR_API_KEY")
response = client.scrape("https://www.ubereats.com/menu/example-restaurant")
print(response.text[:500])  # First 500 chars of HTML

import { AlterLab } from "@alterlab/sdk";

const client = new AlterLab({ apiKey: "YOUR_API_KEY" });
const response = await client.scrape("https://www.ubereats.com/menu/example-restaurant");
console.log(response.text.slice(0, 500));

curl -X POST https://api.alterlab.io/v1/scrape \
  -H "X-API-Key: YOUR_KEY" \
  -d '{"url": "https://www.ubereats.com/menu/example-restaurant"}'

Extracting structured data

Uber Eats menu pages use consistent HTML structures. Target .menu-item containers for dish details:

import alterlab
from parsel import Selector

client = alterlab.Client("YOUR_API_KEY")
html = client.scrape("https://www.ubereats.com/menu/example-restaurant").text
selector = Selector(text=html)

for item in selector.css(".menu-item"):
    name = item.css(".item-name::text").get()
    price = item.css(".price::text").get()
    print(f"{name.strip()}: {price}")

Node.js equivalent using cheerio:

import { AlterLab } from "@alterlab/sdk";
import cheerio from "cheerio";

const client = new AlterLab({ apiKey: "YOUR_API_KEY" });
const html = await client.scrape("https://www.ubereats.com/menu/example-restaurant");
const $ = cheerio.load(html);

$(".menu-item").each((_, el) => {
  const name = $(el).find(".item-name").text().trim();
  const price = $(el).find(".price").text().trim();
  console.log(`${name}: ${price}`);
});

Structured JSON extraction with Cortex

For typed output without manual parsing, use AlterLab's Cortex extraction API. Define a JSON schema for menu items:

import alterlab

client = alterlab.Client("YOUR_API_KEY")
result = client.extract(
    url="https://www.ubereats.com/menu/example-restaurant",
    schema={
        "type": "object",
        "properties": {
            "restaurant_name": {"type": "string"},
            "menu_items": {
                "type": "array",
                "items": {
                    "type": "object",
                    "properties": {
                        "name": {"type": "string"},
                        "price": {"type": "number"},
                        "description": {"type": "string"},
                        "rating": {"type": "number"}
                    },
                    "required": ["name", "price"]
                }
            }
        }
    }
)
print(result.data)  # Validated JSON output

Cost breakdown

AlterLab's pricing scales with technical difficulty. Uber Eats public menu pages typically require T3 (Stealth) due to anti-bot measures, but the API starts at T1 and promotes automatically—you only pay for the successful tier.

View full pricing details. Note: AlterLab auto-escalates tiers—start at T1 and pay only for the level that succeeds.

Best practices

• Rate limiting: Stay under 1 request/second per IP to avoid triggering protections (AlterLab's internal queues help manage this).
• Robots.txt: Check https://www.ubereats.com/robots.txt for crawl delays and disallowed paths.
• Dynamic content: Use wait_for parameters in AlterLab API to ensure menu items load before extraction.
• Headers: AlterLab rotates realistic browser fingerprints—avoid overriding User-Agent unless necessary.
• Error handling: Implement retries with exponential backoff for transient failures (HTTP 429/503).

Scaling up

For large-scale menu data collection:

• Batch requests: Process 100 URLs concurrently using AlterLab's /batch endpoint (reduces overhead).
• Scheduling: Use cron expressions via AlterLab's scheduling API for daily menu updates.
• Data storage: Save extracted JSON to cloud storage (S3/GCS) with timestamps for historical analysis.
• Responsible scaling: Monitor response codes—if 429s increase, reduce concurrency or add delays.

Key takeaways

• Uber Eats public menu data is accessible via AlterLab's API without building custom anti-bot solutions.
• Start with CSS selectors for simple extraction; use Cortex AI for schema-validated output.
• Budget ~$0.002/request for most Uber Eats scraping tasks (T3 tier).
• Always prioritize compliance: review ToS, rate limit, and scrape only publicly visible information.
• Scale responsibly with batch processing and scheduling for ongoing market intelligence.

Explore our dedicated Uber Eats scraping guide for advanced patterns and maintenance tips.