A Grubhub scraper collects public restaurant data from Grubhub, such as menus, prices, and reviews, as clean files. For example, chains use it to compare prices with rivals, while food tech teams use it to fill menu databases. However, Grubhub data varies by city and restaurant, and it changes often. So this guide explains what a Grubhub scraper can extract, how it works, and how to stay within the rules.

What a Grubhub Scraper Collects at a Glance

Grubhub Today: Ownership and Market Share

Grubhub is one of the oldest US food delivery apps. It started in Chicago in 2004 and merged with Seamless in 2013, according to Wikipedia. Later, Just Eat Takeaway bought it in 2021. Then, in January 2025, Wonder completed its purchase of Grubhub for $650 million. Wonder is a virtual food hall company founded by Marc Lore, and about 375,000 merchants came with the deal. In addition, Grubhub+ has been included with Amazon Prime since May 2024.

Grubhub is smaller than its two big rivals. In March 2024, it took 8% of observed US meal delivery spending, according to Bloomberg Second Measure. By contrast, DoorDash took 67%, and Uber Eats took 23%. So a Grubhub scraper works best as part of a wider view that also covers DoorDash and Uber Eats. For that comparison, see our DoorDash vs Uber Eats vs Grubhub report.

What Data Can a Grubhub Scraper Extract?

A Grubhub scraper can capture most of what a diner sees on a restaurant page. The main field groups are:

• Restaurant profile: name, address, cuisine tags, hours, and delivery area, so you can map every listing in a city.
• Menu structure: categories, item names and descriptions, as well as photos where shown.
• Item prices: base prices, plus the price of each add-on or size option.
• Fees and minimums: delivery fee, service fee, and minimum order, because these change what a diner pays.
• Delivery estimates: the expected delivery or pickup time for each address.
• Offers: discounts and free-delivery deals shown on the page.
• Ratings and reviews: star rating, review count, review text, and review dates.

In addition, each record should carry a timestamp and a location, such as a ZIP code. Otherwise, you can’t compare prices fairly over time or across areas.

How a Grubhub Scraper Works, Step by Step

A reliable Grubhub scraper follows six steps:

1. Set the scope: first, choose the cities, ZIP codes, cuisines or restaurant chains you need.
2. Find the listings: then collect the public restaurant pages that serve each address.
3. Extract the fields: pull the menu, prices, fees, delivery times, and ratings from each page.
4. Clean and standardize: convert prices into one format, and link each add-on to its base item.
5. Match across apps: next, link each restaurant to its DoorDash and Uber Eats listings, for example by name and address.
6. Schedule updates: finally, repeat on a set schedule, and flag price or menu changes.

Each step needs care. For instance, the same chain may list slightly different menus in different cities. So good matching rules matter as much as the extraction itself.

Extracting Grubhub Menus and Prices

Menus sit at the core of most Grubhub scraper projects. However, a menu price alone rarely tells the full story.

Capture Every Add-On

Many dishes come with sizes, toppings, or sides that change the price. So capture each option with its own price, and link it to its main item. Otherwise, a burger and the same burger as a meal look like two unrelated products.

Compare the Total, Not Just the Menu

Diners pay the menu price plus fees. In fact, an October 2025 LendingTree study found delivery cost 79.5% more than pickup on average. That study covered five chains on DoorDash, Grubhub, and Uber Eats. So collect fees and minimum orders next to menu prices. Then compare the same basket across all three apps in each ZIP code. To learn more, see our Grubhub restaurant data scraping service and competitor price monitoring.

Extracting Grubhub Reviews and Ratings

Reviews show why diners come back, or why they don’t. So most teams pair menu data with ratings data:

• Track trends: record the star rating and review count for each location over time.
• Find themes: group review text into issues, such as late delivery, cold food or missing items.
• Benchmark rivals: compare your ratings with nearby rivals on Grubhub, DoorDash, and Uber Eats.

Ratings can move sales, too. For example, a 2011 Harvard Business School study linked one extra Yelp star to 5% to 9% more revenue. Although that study looked at Yelp, it shows how much ratings can matter.

Finally, keep privacy in mind. Collect ratings and review text, but don’t store reviewers’ personal details. FoodSpark, for instance, does not collect personal or identity-related information. To learn more, see our review API.

Grubhub Scraper Challenges and Rules

Running a Grubhub scraper takes more than a script. Here are the main hurdles:

• Location-based results: listings, fees, and delivery times change with the delivery address, so scope by ZIP code.
• Changing menus: restaurants update items and prices often, so schedule regular refreshes.
• Similar listings: chains may list several nearby locations with similar names, so match by address.
• Page changes: layouts change over time, so test your extraction every week.
• Bot protection: delivery apps often limit automated traffic, so do-it-yourself scripts need constant upkeep.

Check the Rules First

Grubhub’s terms of use ban robots, spiders, web crawlers, and other automated means that scrape, copy, or monitor its platform. That wording comes from the December 2024 version, as recorded by ConductAtlas. So read the current terms, and get legal advice for your use case. Also, collect only public data, and never collect personal information. This guide is not legal advice.

Grubhub Scraper Options: DIY, Tools or a Data Provider

There are four ways to get Grubhub data, and each suits a different team:

In short, build your own Grubhub scraper only if you can maintain it. Otherwise, a provider is often the faster start, especially when you also need DoorDash and Uber Eats data.

How FoodSpark Helps

FoodSpark collects publicly available Grubhub data, such as menus, prices, cuisines, ratings, and reviews. We support city, neighborhood, and ZIP code-level extraction. Then we deliver the data as CSV, JSON, XML, or Excel, or through an API. In addition, we can add DoorDash and Uber Eats to the same feed, so you can compare all three at once. We do not collect personal or identity-related information. To start, see our Grubhub restaurant data scraping service, Grubhub API, and food data API pricing.

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What Data Can a Grubhub Scraper Extract?

Need these fields for your cities? Get a free 100-row Grubhub sample in CSV or JSON.

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Grubhub Scraper: FAQs

What is a Grubhub scraper?

A Grubhub scraper is a tool or service that collects public Grubhub menus, prices, fees, and reviews as structured data.

Is it legal to use a Grubhub scraper?

It depends on where you are and how you use the data. Grubhub’s terms ban automated scraping, so get legal advice first. Also, collect only public data.

Who owns Grubhub now?

Wonder, the food hall company founded by Marc Lore, completed its $650 million purchase of Grubhub in January 2025.

What is Grubhub’s market share in the US?

Grubhub took 8% of observed US meal delivery spending in March 2024, according to Bloomberg Second Measure.

Can I compare Grubhub prices with DoorDash and Uber Eats?

Yes. First, collect the same restaurants on all three apps by ZIP code. Then compare menu prices plus fees.

How do I get a free Grubhub data sample?

First, tell us the cities, ZIP codes, or chains you need. Then FoodSpark sends a free 100-row sample in CSV or JSON.