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Zomato Restaurants Scraper

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from $2.43 / 1,000 results

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Zomato Restaurants Scraper

Zomato Restaurants Scraper

Scrapes restaurant listings from Zomato city pages. Returns each restaurant as a flat row with name, cuisine, rating, cost for two, and location. Supports multiple city URLs and proxy rotation.

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from $2.43 / 1,000 results

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ParseForge

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Zomato Restaurants Scraper

Scrape restaurant listings from any Zomato city page, up to hundreds per run. Every restaurant comes with its name, cuisine, rating, cost for two, and location. No API key required. Export to CSV, JSON, Excel, or XML.

Zomato's official API is restricted to registered partners and does not serve public restaurant listing data for bulk analysis. This actor reads the public city listing pages directly, so you can collect restaurant profiles from Delhi, Mumbai, Bangalore, or any Zomato city URL. It returns each restaurant as one flat row, ready for market research, competitor tracking, or lead generation.

Who uses itWhat they scrape Zomato for
Market researchersMap the restaurant density and cuisine mix in a target city.
Food delivery startupsBuild a prospecting list of restaurants not yet on their platform.
Restaurant tech vendorsFind restaurants by area and rating for POS or SaaS outreach.
Data analystsTrack rating trends and pricing across neighborhoods over time.

What it does

This Actor collects restaurant listings from Zomato city pages and returns each restaurant as a structured row with its name, cuisine, rating, cost for two, and location.

  • ๐Ÿ“ City-level scraping: feed one or more Zomato city listing URLs and collect every restaurant on each page.
  • ๐Ÿ”ข Max items control: set a hard limit on the number of restaurants extracted per run to stay within your budget.
  • ๐Ÿ›ก๏ธ Proxy rotation: built-in Apify proxy support keeps your requests distributed and reduces blocking.

Results export to CSV, JSON, Excel, or XML, or straight from the API.

What you can do with Zomato data

๐Ÿ“Š Market sizing and cuisine analysis.

A restaurant-tech analyst scrapes all Bangalore listings to count restaurants by cuisine and average cost for two, then sizes the addressable market for a new B2B product.

๐Ÿ“‹ Lead list generation.

A food-delivery sales team scrapes Mumbai restaurants rated above 4.0 and exports the list to their CRM for direct outreach.

๐Ÿ“ Location intelligence.

A real-estate consultant scrapes multiple Delhi neighborhoods to map restaurant clusters and advise a client on the best high-street location for a new cafe.

๐Ÿ“ˆ Competitor price tracking.

A restaurant owner scrapes their city page weekly, filters by their own cuisine, and tracks how competitors adjust their cost-for-two over a quarter.

Why choose this scraper

What you get
No API registrationReads public Zomato pages, so you skip OAuth and rate-limit negotiations.
Fixed flat schemaEvery restaurant lands as one row with the same columns, ready for analysis.
Multi-city runsQueue several city URLs in one run and get a combined dataset.

How it compares

This actor focuses on restaurant listing data from city pages, while the Zomato Restaurant Reviews Scraper targets individual review-level data from restaurant profile pages.

FeatureParseForgeZomato Restaurant Reviews Scraper ๐Ÿฝ๏ธ
Scrapes city listing pagesYesNot listed
Extracts restaurant name and cuisineYesNot listed
Extracts cost for twoYesNot listed
Extracts user reviews and review textNot listedYes
Multi-URL input supportYesNot listed

Configure the run

Drive the Actor from one or more Zomato city listing URLs, and set a maximum item count so only the top N restaurants reach your dataset. The Input tab lists every parameter.

A first run with the defaults:

{
"startUrls": [
{
"url": "https://www.zomato.com/bangalore/restaurants"
}
],
"proxyConfiguration": {
"useApifyProxy": true
}
}

A larger pull:

{
"startUrls": [
{
"url": "https://www.zomato.com/bangalore/restaurants"
}
],
"proxyConfiguration": {
"useApifyProxy": true
},
"maxItems": 200
}

Pricing

Pay-per-result: $0.00269 per result collected. You pay only for the results written to your dataset.

Results collectedApproximate cost
100 results$0.27
1,000 results$2.69
10,000 results$26.90

New Apify accounts start with $5 in free credit.

Free users

Free-plan runs return up to 10 results as a preview. Upgrade your Apify plan to collect up to 1,000,000 results per run.

Run it

  1. Create a free Apify account with $5 in credit.
  2. Open the Zomato Restaurants Scraper.
  3. Set your inputs and any filters, then click Start.
  4. Export the results as CSV, Excel, JSON, or XML from the Dataset tab.

Run it programmatically through the Apify API (run-sync-get-dataset-items) or the ApifyClient for JavaScript and Python.

Use with AI agents (MCP)

Give an AI agent live access to Zomato through the Model Context Protocol. Add the Actor to Claude, Cursor, or any MCP client:

$claude mcp add --transport http apify "https://mcp.apify.com?tools=parseforge/zomato-restaurants-scraper"

Then prompt it in plain language to run the scraper and read back the results.

Troubleshooting

Why am I getting no results?

Check that your start URL is a valid Zomato city listing page and that the page loads in a regular browser. Also verify that your max items is set to a number greater than zero.

The actor returns fewer items than my max items setting.

The city page may have fewer total listings than your limit, or some listings may have been filtered out by Zomato. Try a broader city URL or a different location.

I am getting blocked or seeing captchas.

Enable the proxy configuration and select Apify Proxy. For very large runs, reduce the max items per run and split the work across multiple runs with different city URLs.

Some restaurant fields are empty in my dataset.

Zomato listings sometimes omit fields like cost for two or rating for newer or unclaimed restaurants. The actor returns whatever is publicly visible on the listing page.

The run takes a long time.

Large max items values mean more pages to load. Lower the max items or split your target cities into separate, smaller runs that can execute in parallel.

FAQ

QuestionAnswer
Can I scrape a specific cuisine or only whole city pages?The actor starts from the city listing URLs you provide. If Zomato offers a filtered URL for a cuisine, you can use that as a start URL. The actor does not apply its own cuisine filter.
How many restaurants can I scrape in one run?You set the maximum with the 'Max items' field. The actor stops when it reaches that count or when the city page runs out of listings, whichever comes first.
Does this scrape restaurant menus or reviews?No, this actor collects the listing-level data you see on a city page: name, cuisine, rating, cost for two, and location. It does not open individual restaurant pages for menus or reviews.
Which cities are supported?Any city that has a Zomato listing page. Provide the URL, and the actor will scrape it. Common examples include Delhi NCR, Mumbai, Bangalore, Pune, Hyderabad, and Chennai.
Do I need a Zomato account or API key?No. The actor reads the public pages that anyone can visit in a browser. No login, no API key, no OAuth.
What output formats can I get?You can export your dataset as CSV, JSON, Excel, or XML directly from the Apify platform.
Will I get blocked by Zomato?The actor uses Apify proxy rotation to distribute requests. For large runs, we recommend setting a reasonable max items count and using the proxy configuration to reduce the chance of blocking.
Can I schedule this to run daily?Yes. Once you have configured the actor, you can set a schedule in Apify to run it every day, week, or any interval you need.
Does the actor handle infinite scrolling on Zomato?Yes, the actor navigates the listing pages to collect restaurants beyond the first page, up to your max items limit.

Browse the full ParseForge collection for more scrapers.

๐Ÿ†˜ Need help? Email parseforge@protonmail.com with your run ID, your input, and what you expected.

โš ๏ธ Disclaimer. This Actor is unofficial and is not affiliated with, endorsed by, or sponsored by Zomato Ltd. It collects only publicly available data. You are responsible for using the collected data in compliance with the source's terms of service and applicable data-protection laws, including GDPR, CCPA, and PIPL. Do not use it to collect personal data unlawfully.