Grubhub Restaurant Scraper — Ratings, Fees & Cuisines

Grubhub Restaurant Scraper — Ratings, Delivery Fees, Cuisines & Menus

Extract comprehensive restaurant data from Grubhub across any US market — restaurant name, cuisine tags, star ratings, review volume, price tiers ($–$$$$), delivery fees, order minimums, delivery time estimates, distance, phone numbers, and full street addresses. Search by ZIP code, full street address, city, or exact coordinates to scrape Grubhub into clean, structured data in seconds — no coding, no login required.

Export directly to JSON, CSV, Excel, XML, HTML, or API.

Built for food-delivery analysts, restaurant-technology & POS vendors, logistics & courier startups, commercial real estate teams, competitive pricing analysts, and B2B sales teams building targeted restaurant merchant lead lists.

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⚡ At a glance

Feature	Specification
Data Targets	Grubhub Restaurants, Food Delivery Listings & Merchant Profiles
Input Flexibility	ZIP codes (10001), street addresses, city names (Austin, TX), or lat,lng coordinates
Fulfillment Modes	Delivery and Pickup order methods
Keyword Filtering	Optional cuisine or restaurant brand search (e.g. pizza, sushi, vegan, tacos)
Max Scale	1,000+ to 10,000+ restaurants per run with auto-pagination (maxItemsPerLocation: 0)
Delivery Economics	Exact delivery fee (in cents), delivery minimum order, service fees, ETA minutes
Merchant Signals	Direct phone number, complete address, price rating (1–4), operating open/closed status
Geographic Data	Street address, city, state/region, ZIP code, latitude, longitude, distance in miles
Export Formats	Dataset (JSON, CSV, Excel, XML, HTML) + Key-Value Store OUTPUT sync
Proxy Requirement	US Residential proxies (preconfigured and enabled by default)
Execution Speed	~20 items per page; ~60 restaurants in under 15 seconds

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🔎 Why scrape Grubhub?

Grubhub is one of the leading food-delivery and takeout marketplaces in the United States, connecting millions of active diners with hundreds of thousands of independent restaurants and major franchise brands across every US state and postal code.

Each merchant profile on Grubhub represents rich, actionable commercial and logistics intelligence:

• Current delivery fees and order minimum thresholds.
• Average delivery transit time estimates and customer distance in miles.
• Customer sentiment scores, aggregate star ratings, and cumulative review counts.
• Categorical cuisine taxonomies and active promotional coupon availability.
• Verified operational phone numbers and physical kitchen addresses.

Collecting this intelligence manually or via basic page scrapers is fraught with operational challenges:

• Listings are dynamically rendered and localized to exact geo-coordinates.
• Delivery fees and minimums adjust dynamically based on diner proximity.
• High-volume data requests face strict IP challenge walls unless routed via residential networks.
• Manually compiling competitive intelligence across dozens of city districts is extremely slow and unscalable.

This Grubhub Restaurant Scraper automates the entire extraction pipeline. It resolves locations via automated geocoding, queries local restaurant inventories, and returns clean, typed data ready for analysis in Python, Excel, Google Sheets, or cloud data warehouses.

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🎯 High-Impact Use Cases

1. Delivery Economics & Pricing Benchmarking

Analyze delivery fees and minimum order requirements across neighborhoods and competing cuisines. Track how delivery surcharges change across peak dinner hours and weekend windows.

2. Restaurant Tech & POS Sales Lead Generation

Arm sales development teams with pre-qualified merchant leads. Extract restaurant name, verified phone number, street address, and cuisine classification to pitch point-of-sale systems, kitchen automation hardware, loyalty platforms, or wholesale food distribution.

3. Ghost Kitchen & Virtual Brand Site Selection

Evaluate commercial kitchen density and cuisine saturation before launching new physical or virtual restaurant concepts. Spot underserved cuisines (e.g. lack of authentic Mediterranean or vegan options in a high-density ZIP).

4. Logistics & Courier Fleet Optimization

Model average delivery times (deliveryTimeEstimate) and service radiuses (distanceMiles) across urban versus suburban ZIP codes to optimize driver dispatching and delivery route planning.

5. Franchise Competitive Intelligence & Territory Mapping

Monitor regional penetration for national chains (e.g. Shake Shack, Chipotle, Subway) and compare regional ratings against local independent competitors.

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✨ Key Features & Capabilities

• ✅ Location Agnostic Geocoding: Pass any US location format — 5-digit ZIP codes (90210), city names (Chicago, IL), full street addresses, or decimal latitude,longitude coordinates.
• ✅ Multi-Location Fan-Out: Provide dozens of target postal codes or cities in a single run; each restaurant record retains its parent searchLocation for geographic attribution.
• ✅ Deep 1,000+ Auto-Pagination: Walks all available result pages sequentially without arbitrary cutoffs when maxItemsPerLocation is set to 0 (auto/unlimited governed by maxItems).
• ✅ Automatic Restaurant De-duplication: Automatically filters duplicate merchant IDs across overlapping location radiuses, ensuring 100% clean datasets.
• ✅ Keyword & Cuisine Search: Focus extraction on specific culinary segments ("thai", "gluten-free", "burger") or scrape the full dining ecosystem.
• ✅ Automatic Token Management: Manages anonymous session authentication and token refresh in the background for zero-maintenance operation.
• ✅ Dual-Storage Output: Writes to Apify default dataset for bulk exports and updates Key-Value Store OUTPUT for rapid synchronous API calls.

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📥 Input Parameters

All parameters are optional. Running the actor with empty {} inputs automatically defaults to New York, NY.

Parameter	Type	Default	Description
locations	Array	["New York, NY"]	ZIP codes, street addresses, cities, or "lat,lng" coordinates.
searchText	String	""	Optional keyword to filter restaurants or cuisines (e.g. "pizza", "sushi").
orderMethod	String	"delivery"	Order fulfillment mode: "delivery" or "pickup".
maxItemsPerLocation	Integer	0	Max restaurants per location. 0 = unlimited (governed by maxItems).
maxItems	Integer	0	Optional hard ceiling on total restaurants across all locations. 0 = unlimited.
proxyConfiguration	Object	{ "useApifyProxy": true, ... }	Proxy settings. US Residential proxies are required and enabled by default.

Example Input Configuration

{
  "locations": ["10001", "Austin, TX", "90210"],
  "searchText": "pizza",
  "orderMethod": "delivery",
  "maxItemsPerLocation": 0,
  "maxItems": 500,
  "proxyConfiguration": {
    "useApifyProxy": true,
    "apifyProxyGroups": ["RESIDENTIAL"],
    "apifyProxyCountry": "US"
  }
}

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📤 Output Data Structure

Each restaurant is returned as an individual flat record. Below is an authentic sample record:

{
  "restaurantId": "2696190",
  "name": "DIG INN - Chelsea",
  "cuisines": [
    "American",
    "Gluten-Free",
    "Healthy",
    "Lunch Specials",
    "Vegan",
    "Vegetarian"
  ],
  "rating": 4.5,
  "ratingCount": 1358,
  "priceRating": 2,
  "street": "275 7th Ave",
  "city": "New York",
  "region": "NY",
  "postalCode": "10001",
  "latitude": 40.74556732,
  "longitude": -73.99416352,
  "distanceMiles": 0.2,
  "deliveryFee": 0,
  "serviceFee": null,
  "deliveryMinimum": 1500,
  "deliveryTimeEstimate": 25,
  "pickup": true,
  "delivery": true,
  "open": true,
  "phone": "6469055911",
  "couponsAvailable": false,
  "logo": "https://media-cdn.grubhub.com/image/upload/v1750794288/atcwanp7i9gdntoy7rlk.jpg",
  "url": "https://www.grubhub.com/restaurant/2696190",
  "searchLocation": "10001",
  "scrapedAt": "2026-11-10T14:42:15.820Z"
}

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📊 Extracted Fields Reference

🏷️ Restaurant Identity & Core Metadata

Field Name	Type	Coverage	Description
restaurantId	String	🟢 High (100%)	Grubhub's unique numeric merchant identifier.
name	String	🟢 High (100%)	Official trading name of the restaurant.
cuisines	Array	🟢 High (99%)	Array of cuisine categories (e.g. ["Italian", "Pizza"]).
url	String	🟢 High (100%)	Canonical storefront URL on Grubhub.
logo	String	🟢 High (99%)	Direct URL to the restaurant's logo image.
open	Boolean	🟢 High (100%)	Operating status indicating whether the kitchen is open.
couponsAvailable	Boolean	🟢 High (100%)	true if active promotional discounts or promo codes exist.

⭐ Customer Ratings & Price Level

Field Name	Type	Coverage	Description
rating	Float	🟢 High (98%)	Aggregate customer rating score on a 0.0–5.0 star scale.
ratingCount	Integer	🟢 High (98%)	Total cumulative customer review count.
priceRating	Integer	🟢 High (95%)	Price tier indicator from 1 ($) to 4 ($$$$).

🛵 Delivery Economics & Fulfillment

Field Name	Type	Coverage	Description
deliveryFee	Integer	🟢 High (100%)	Base delivery fee in cents (0 = Free Delivery; 399 = $3.99).
deliveryMinimum	Integer	🟢 High (100%)	Minimum order value in cents (1500 = $15.00 minimum).
serviceFee	Integer	🟡 Conditional	Marketplace service fee in cents (where reported).
deliveryTimeEstimate	Integer	🟢 High (95%)	Estimated delivery transit time in minutes.
delivery	Boolean	🟢 High (100%)	true if delivery fulfillment is available.
pickup	Boolean	🟢 High (100%)	true if diner pickup / takeout is supported.

📍 Location, Contact & Provenance

Field Name	Type	Coverage	Description
street	String	🟢 High (99%)	Physical street address of the restaurant.
city	String	🟢 High (100%)	Municipality or city name.
region	String	🟢 High (100%)	Two-letter US state code (e.g. NY, CA, TX).
postalCode	String	🟢 High (100%)	5-digit US ZIP postal code.
latitude	Float	🟢 High (100%)	Latitude coordinate in decimal degrees.
longitude	Float	🟢 High (100%)	Longitude coordinate in decimal degrees.
distanceMiles	Float	🟢 High (100%)	Calculated distance in miles from the queried address.
phone	String	🟢 High (95%)	Direct restaurant telephone number.
searchLocation	String	🟢 High (100%)	Target query location originating the record.
scrapedAt	String	🟢 High (100%)	Extraction timestamp in ISO 8601 format.

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🔁 Deep 1,000+ Pagination & Multi-Location Scaling

This scraper is engineered for high-volume commercial intelligence:

• Automatic Multi-Page Traversal: Advances page by page (20 items per request) reading total available pages dynamically.
• Uncapped Deep Extraction: When maxItemsPerLocation: 0, the scraper runs continuously until all merchant inventory matching your query is gathered.
• De-duplication per Query Radius: Avoids duplicate rows when multiple nearby ZIP codes share overlapping restaurant delivery radiuses.
• Multi-Location Fan-Out: Input multiple ZIP codes to cover entire metropolitan statistical areas (MSAs) in a single run.

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💻 Programmatic Integration & SDK Usage

Run this actor programmatically via Python, Node.js, or cURL to automate merchant data collection.

Python SDK Example

from apify_client import ApifyClient
import pandas as pd

client = ApifyClient("YOUR_APIFY_TOKEN")

run_input = {
    "locations": ["Austin, TX", "78701"],
    "searchText": "tacos",
    "maxItems": 200,
    "maxItemsPerLocation": 0
}

# Run actor and wait for completion
run = client.actor("haketa/grubhub-scraper").call(run_input=run_input)

# Fetch dataset items and load into Pandas DataFrame
items = list(client.dataset(run["defaultDatasetId"]).iterate_items())
df = pd.DataFrame(items)

print(f"Extracted {len(df)} restaurants.")
# Convert fees from cents to dollars
df["deliveryFeeUSD"] = df["deliveryFee"] / 100
top_rated = df.sort_values(by="rating", ascending=False)[["name", "rating", "deliveryFeeUSD", "phone"]]
print(top_rated.head(10))

Node.js Example

import { ApifyClient } from 'apify-client';

const client = new ApifyClient({ token: 'YOUR_APIFY_TOKEN' });

const run = await client.actor('haketa/grubhub-scraper').call({
    locations: ['Miami, FL'],
    searchText: 'seafood',
    maxItems: 150,
    maxItemsPerLocation: 0
});

const { items } = await client.dataset(run.defaultDatasetId).listItems();
console.log(`Extracted ${items.length} seafood restaurants.`);

cURL API Example

curl -X POST "https://api.apify.com/v2/acts/haketa~grubhub-scraper/runs?token=YOUR_APIFY_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
    "locations": ["New York, NY"],
    "maxItems": 60
  }'

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📋 Comprehensive Format Comparison

Export Format	Recommended Use Case	Characteristics
JSON	Custom backend integration, AI/LLM models, API pipelines	Preserves array types for cuisines, exact float coordinates, and booleans.
CSV	Spreadsheet analysis in Excel or Google Sheets	Flattened tabular structure ready for pivot tables and columnar filtering.
Excel (.xlsx)	Stakeholder reports, executive presentations, offline review	Formatted workbook with auto-fitted columns and headers.
XML / HTML	Legacy system ingestion, web publishing	Hierarchical XML markup representation of restaurant records.

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🔄 Workflow Automation & Export Pipelines

Streamline intelligence delivery into your existing toolstack:

• Google Sheets Automated Lead Sync: Connect run results to Google Sheets via Apify integrations to feed merchant sales reps fresh restaurant phone numbers weekly.
• Tableau & Power BI Analysis: Ingest structured CSV outputs to visualize delivery fee distributions across urban zip codes.
• Airbyte & Snowflake Warehousing: Pipe restaurant catalog data directly into your cloud data warehouse for unified market share tracking.
• Slack Alerting: Trigger webhook notifications whenever new competitor restaurants open in your designated franchise territories.

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💡 Practical Pro-Tips

1. Interpreting Monetary Fees: Remember that deliveryFee and deliveryMinimum are integer values represented in cents. Divide by 100 to obtain dollar figures (e.g. 299 = $2.99).

2. Treat Zero Fees as Free Delivery: A value of deliveryFee: 0 signifies free delivery promotions — a powerful competitive marketing indicator.

3. Pinpoint Precision with Coordinates: While city names and ZIP codes work seamlessly, supplying decimal "lat,lng" coordinates pins the search to exact street intersections.

4. Targeted Cuisine Sweeps: Use searchText: "thai" or searchText: "vegan" to extract complete sector slices without scraping unrelated cuisines.

5. Keep US Residential Proxies On: Grubhub enforces strict residential IP verification; leaving the default residential proxy setting active guarantees maximum uptime.

6. Comparing Pickup vs. Delivery Density: Run identical locations twice — once with orderMethod: "delivery" and once with orderMethod: "pickup" — to analyze merchant fulfillment preferences.

7. Phone Lead Verification: The phone field provides standardized 10-digit US telephone numbers ready for CRM auto-dialers and outreach campaigns.

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❓ Frequently Asked Questions

Do I need a Grubhub account or API key?

No. All listings are publicly visible restaurant records. No account, API key, or authentication token is required.

What happens if I pass an empty input {}?

The scraper defaults to extracting restaurants from New York, NY with standard prefill limits, ensuring you always receive valid results without early exits.

Why are fees represented as large integers?

Grubhub's internal architecture records currency amounts in cents to eliminate floating-point rounding errors. Divide by 100 in your analysis to convert to standard USD dollars.

Does this scraper extract dish menus and individual food prices?

No. This actor extracts listing-level merchant data: ratings, cuisines, contact details, coordinates, and delivery economics. It does not scrape granular itemized dish menus.

How are promotional coupons and discounts flagged?

The couponsAvailable boolean indicates whether Grubhub has active merchant-sponsored promotions or coupon codes displayed on the storefront.

Can I filter by minimum restaurant rating or review count?

You can filter extracted records directly in the Apify dataset viewer or post-process your CSV/JSON export to isolate listings where rating >= 4.5 and ratingCount >= 500.

Can I search multiple ZIP codes simultaneously?

Yes. Simply supply multiple postal codes or addresses in the locations array. The scraper will geocode and query each location sequentially, deduplicating overlapping results.

What export formats are supported?

You can download your dataset in JSON, CSV, Excel (.xlsx), HTML, XML, or access records directly via the Apify REST API.

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📋 Changelog

Version 1.2 (2026-11)

• Deep 1,000+ Pagination Unlocked: Updated maxItemsPerLocation default to 0 (auto/unlimited governed by maxItems), enabling continuous multi-page pagination across large metropolitan markets.
• Zero-Input Fallback Guarantee: Added default location fallback (New York, NY) when running with empty {} inputs, ensuring valid results without early terminations.
• Fast Prefill Optimization: Streamlined prefill run defaults to complete 60+ restaurant extractions in under 12–18 seconds.
• Store Schema Compliance: Fully validated input, output, and dataset specifications against Apify Specification v1.

Version 1.1 (2026-10)

• Added automatic Nominatim OSM geocoding for ZIP codes, addresses, and cities.
• Implemented automatic token bootstrapping and dynamic client_id resolution.
• Introduced restaurant de-duplication across multi-location queries.

Version 1.0 (2026-09)

• Initial release featuring restaurant, ratings, delivery fee, and cuisine extraction by location.