Zomato Scraper

What Zomato Scraper can do

• Two ways to find restaurants. Search one or more city slugs on the Delivery or Dining Out feed, with an optional keyword, or paste restaurant, feed, and keyword-search links directly.
• Full restaurant profiles. Cuisines, cost for two, per-vertical (Dining and Delivery) ratings, address, coordinates, phone numbers, chain name, and open/closed status, all on by default.
• Reviews included. Attach up to 2000 reviews per restaurant with rating, author, date, like count, tags, and full text, sorted the way you want.
• Offers filter. Narrow the Dining Out feed and keyword search to restaurants currently running a dining offer.
• Built for schedules. Incremental mode returns only new and changed restaurants on recurring runs, with a resume option for one interrupted run.
• Automatic mid-run checkpointing. A run that gets migrated or restarted by the platform picks up where it stopped instead of re-collecting and re-charging restaurants already pushed.

Choose what to collect

These inputs let you configure Zomato Scraper - Restaurants, Ratings & Reviews. Open the actor on Apify to enter your values and review the current options.

Fields in the output

The documented dataset includes the fields below. Availability can depend on the source page and selected mode.

Example output

An example from this actor’s documentation. Values are illustrative; this is not a fresh live result.

{
  "id": "18685577",
  "name": "Olive Bar & Kitchen",
  "url": "https://www.zomato.com/ncr/olive-bar-kitchen-mehrauli-new-delhi",
  "cuisines": [
    "Italian",
    "Mediterranean",
    "Continental"
  ],
  "cuisineString": "Italian, Mediterranean, Continental",
  "locality": "Mehrauli, New Delhi",
  "costForTwoText": "₹4,000 for two",
  "rating": {
    "aggregate": 4.6,
    "text": "4.6",
    "subtitle": "Very Good",
    "votes": 3049,
    "color": "3F7E00"
  },
  "ratingsByVertical": {
    "DINING": {
      "rating": 4.6,
      "reviewCount": 3049,
      "subtitle": "Very Good"
    },
    "DELIVERY": {
      "rating": 4.1,
      "reviewCount": 1200,
      "subtitle": "Very Good"
    }
  },
  "address": "One Style Mile, Kalka Das Marg, Mehrauli, New Delhi",
  "city": "New Delhi",
  "country": "India",
  "latitude": 28.5245,
  "longitude": 77.1855,
  "phones": [
    "+911234567890"
  ],
  "chainName": "Olive Group",
  "hasPromo": true,
  "promoOffer": "20% OFF",
  "detailScraped": true,
  "reviews": [
    {
      "id": "r123",
      "rating": 5,
      "ratingText": "Excellent",
      "author": "Jane D.",
      "timestamp": "5 days ago",
      "text": "Fantastic ambience and food.",
      "likeCount": 3,
      "commentCount": 0,
      "tags": [
        "Great Ambience"
      ]
    }
  ],
  "reviewsCount": 1,
  "source": "search"
}

Ways to use this data

• Food delivery market research: compare cuisines, cost for two, and delivery times across a city or neighborhood.
• Reputation and review analysis: pull full review text and ratings to track sentiment for specific restaurants or chains.
• Restaurant discovery apps: feed structured listings into your own search, map, or recommendation tool.
• Competitive monitoring: schedule the actor and use incremental mode to catch new listings, rating changes, or closures over time.
• Local business directories: collect address, phone, and status data for restaurants in a city or region.

Before you run

Start with a small input and check the resulting records against your expected fields. Source content and actor options can change; the current Apify listing is the reference for availability and pricing.

Questions about this actor