OpenTable Scraper

OpenTable Scraper extracts restaurant listings, full profile details, and diner reviews from OpenTable — for just $2 per 1,000 restaurants (listing details $3 per 1,000, reviews $2 per 1,000). Search by city, cuisine, or map area, paste an OpenTable search URL, or scrape a specific restaurant profile — then download structured data via the Apify platform (API access, scheduling, integrations, proxy rotation, and run monitoring).

Use it to map local dining markets, track ratings and availability, enrich CRM or research datasets, or feed competitive-intelligence dashboards — without building your own Akamai-aware crawler.

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What can OpenTable Scraper do for you?

• 🗺️ Map a city's dining scene — Pull restaurant directories by location, cuisine, or map area with ratings, price band, and review counts.
• 🏆 Competitive intelligence — Capture hours, amenities, dress code, parking, websites, and social links from restaurant profiles.
• 💬 Mine diner reviews — Paginate reviews with overall/food/service/ambience/value scores, dates, and nicknames — capped per restaurant when you need it.
• ⏰ Availability-aware search — Set reservation date, time, and party size so search results reflect real availability.
• 🔁 Automation-ready — Schedule runs, trigger via API, or pipe results into Zapier / Make / webhooks.
• 🛡️ Bot-resistant by design — Stacks TLS impersonation, stealth browser, and paced GraphQL with residential proxies to get past OpenTable's Akamai Bot Manager.

What data can you extract?

Category	Fields
🏷️ Identity	restaurantId, name, url, searchQuery
⭐ Ratings	rating, reviewCount, recentReservationCount
📍 Location	neighborhood, city, state, postCode, country, address, latitude, longitude
🍽️ Offer	cuisine, priceBand, currencySymbol, diningStyle, dressCode
📞 Contact	phone, email, website, facebookUrl, orderOnlineLink
🕐 Profile extras	hoursOfOperation, amenities, paymentOptions, parkingDetails, publicTransit, executiveChef, hasTakeout, hasDelivery, outdoorSeating, photos
💬 Reviews	reviews[] with overall, food, service, ambience, value, text, submittedDateTime, nickname
🚩 Flags	detailsFetched, reviewsFetched

Why use this scraper?

• 📊 Market research — Understand restaurant supply, price bands, and rating distributions in any city.
• 🕵️ Competitor monitoring — Track how competitors' ratings, review counts, and hours change over time.
• 🤝 Lead generation — Build contact lists (phone, email, website) of restaurants for outreach or partnerships.
• ⚙️ Automation — Replace manual copy-paste with scheduled runs and API-driven data pipelines.
• 💰 Pay only for results — Per-event pricing means you never pay for empty or failed requests.

Pricing

How much does it cost to scrape OpenTable?

Pricing is pay-per-event — you're charged only for what the Actor successfully produces:

Event	What you get	No discount	Bronze (5% off)	Silver (10% off)	Gold (15% off)
dataset-item	Each restaurant pushed to the dataset	$2.00 / 1,000	$1.90 / 1,000	$1.80 / 1,000	$1.70 / 1,000
listing-details	Each restaurant with full profile enrichment	$3.00 / 1,000	$2.90 / 1,000	$2.80 / 1,000	$2.70 / 1,000
restaurant-reviews	Each restaurant with completed review extraction	$2.00 / 1,000	$1.90 / 1,000	$1.80 / 1,000	$1.70 / 1,000

• No hidden fees or limits on usage.
• Use maxItems and maxReviews to control spend exactly.
• You pay for Apify platform usage (compute + residential proxy traffic) separately on your Apify plan.

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How to use OpenTable Scraper

1. Create a free Apify account and open OpenTable Scraper.
2. Add Start URLs (search or /r/... profile links) and/or Search Queries (e.g. Dresden, Italian in Berlin).
3. Optionally set reservation date, time, and party size.
4. Enable Scrape Listing Details and/or Scrape Restaurant Reviews when you need them.
5. Keep Residential proxies enabled (recommended).
6. Click Start, then download the dataset as JSON, HTML, CSV, or Excel.

Input

Configure these fields in the Input tab (or pass JSON via API):

Field	Type	Required	Description
startUrls	array	No	OpenTable search or restaurant profile URLs (takes priority when provided)
searchQueries	string list	No	Location / restaurant terms resolved via OpenTable autocomplete
date	date	No	Reservation date YYYY-MM-DD (defaults to tomorrow)
time	string	No	Preferred time in HH:MM (default 19:00)
partySize	integer	No	Number of diners 1–20 (default 2)
scrapeListingDetails	boolean	No	Open each profile page and extract full details (charges a listing-details event per restaurant)
scrapeReviews	boolean	No	Paginate and extract reviews (charges a restaurant-reviews event per restaurant)
maxReviews	integer	No	Cap reviews per restaurant, 0 = all
maxItems	integer	No	Cap restaurants, 0 = unlimited
proxyConfiguration	object	No	Proxy settings — Apify RESIDENTIAL strongly recommended

📝 Copy for Use:

{
  "startUrls": [
    {
      "url": "https://www.opentable.com/s?dateTime=2026-08-15T19%3A00%3A00&covers=2&latitude=51.052008&longitude=13.741322&areaId=geohash%3Au31f2twp&suggestedSearchName=Dresden&shouldUseLatLongSearch=true&showMap=true"
    }
  ],
  "scrapeListingDetails": true,
  "scrapeReviews": true,
  "maxReviews": 5,
  "maxItems": 2,
  "proxyConfiguration": {
    "useApifyProxy": true,
    "apifyProxyGroups": ["RESIDENTIAL"]
  }
}

Output

Each dataset item is one restaurant. You can download the dataset in JSON, HTML, CSV, or Excel from the Output tab.

{
  "restaurantId": 227388,
  "name": "Peter Pane - Dresden",
  "url": "https://www.opentable.com/r/peter-pane-dresden-am-zwinger-dresden",
  "cuisine": "Burgers",
  "priceBand": "€30 and under",
  "rating": 4.5,
  "reviewCount": 515,
  "neighborhood": "Old Town",
  "city": "Dresden",
  "state": "SN",
  "postCode": "01067",
  "address": "Wilsdruffer Straße 14-16, Dresden, SN, 01067",
  "phone": "0351 48676840",
  "latitude": 51.0515654,
  "longitude": 13.7349184,
  "diningStyle": "Casual Dining",
  "hasTakeout": true,
  "searchQuery": "Dresden",
  "detailsFetched": true,
  "reviewsFetched": true,
  "reviews": [
    {
      "reviewId": 123,
      "text": "Great burgers...",
      "overall": 5,
      "food": 5,
      "service": 4,
      "ambience": 4,
      "value": 5,
      "nickname": "Diner123",
      "submittedDateTime": "2026-07-01T12:00:00"
    }
  ]
}

Why Choose This Scraper?

• Affordable: Pay only $2 per 1,000 restaurants scraped — details at $3/1,000 and reviews at $2/1,000, with up to 15% off on higher Apify plans.
• Comprehensive: Listings, full profiles, and reviews in one run — 40+ fields per restaurant including hours, amenities, parking, and photos.
• Easy to Use: No code required. Paste a search URL or type a city name; the Input tab has sane defaults for everything else.
• Reliable: Purpose-built Akamai bypass (TLS impersonation → stealth browser → paced GraphQL) with sticky residential sessions, plus automatic retries for failed seeds.

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Running via Apify Console

You can run this actor from the Apify Console by providing the necessary input parameters.

Running via API

You can trigger this actor using the Apify API, passing the required input in the request body.

Example (Python)

from apify_client import ApifyClient

client = ApifyClient("YOUR_APIFY_TOKEN")

run_input = {
    "searchQueries": ["Dresden"],
    "time": "19:00",
    "partySize": 2,
    "scrapeListingDetails": True,
    "scrapeReviews": True,
    "maxReviews": 5,
    "maxItems": 10,
    "proxyConfiguration": {
        "useApifyProxy": True,
        "apifyProxyGroups": ["RESIDENTIAL"],
    },
}

run = client.actor("rigelbytes/opentable-scraper").call(run_input=run_input)
dataset_items = client.dataset(run["defaultDatasetId"]).list_items().items

Example (JavaScript)

import { ApifyClient } from "apify-client";

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

const runInput = {
  searchQueries: ["Dresden"],
  time: "19:00",
  partySize: 2,
  scrapeListingDetails: true,
  scrapeReviews: true,
  maxReviews: 5,
  maxItems: 10,
  proxyConfiguration: {
    useApifyProxy: true,
    apifyProxyGroups: ["RESIDENTIAL"],
  },
};

const run = await client.actor("rigelbytes/opentable-scraper").call(runInput);
const { items } = await client.dataset(run.defaultDatasetId).listItems();

Example (cURL)

curl -X POST \
  "https://api.apify.com/v2/acts/rigelbytes~opentable-scraper/run-sync-get-dataset-items?token=YOUR_APIFY_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
    "searchQueries": ["Dresden"],
    "time": "19:00",
    "partySize": 2,
    "scrapeListingDetails": true,
    "scrapeReviews": true,
    "maxReviews": 5,
    "maxItems": 10,
    "proxyConfiguration": {
      "useApifyProxy": true,
      "apifyProxyGroups": ["RESIDENTIAL"]
    }
  }'

Scheduling & Deduplication

• Scheduling: Restaurant ratings, review counts, and availability change daily — run a daily schedule for monitoring, or weekly for market research snapshots. With multiple searchQueries, create a separate schedule per query term (e.g. one per city) to keep datasets clean and focused. Set this up in the Apify Console Scheduler tab or via the Actor API.
• Deduplication: The actor stops at the maxItems cap and each output item is keyed by its unique restaurantId. OpenTable itself doesn't expose a revision number, so repeated runs of the same query naturally re-emit the same restaurants. Deduplicate in your downstream pipeline by restaurantId — for example:

import pandas as pd

df = pd.concat([pd.read_json(run1), pd.read_json(run2)])
deduped = df.drop_duplicates(subset="restaurantId", keep="last")
print(len(deduped))

• Webhooks: Trigger a webhook on run completion to feed your database, dashboard, or notification pipeline automatically.

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📰 News & Media

Scrape news, articles, and media content for research.

• BizBuySell Scraper — Scrape BizBuySell business-for-sale listings via search URLs or structured filters, with optional full listing details.
• SEC IPO Event Intelligence — Monitors SEC EDGAR for IPO filings and converts them into structured business events, with optional AI company...
• SEC Filings Scraper — Retrieve clean, normalized SEC EDGAR filing data by ticker, CIK, or company name — with document links, optional...

🛠️ Developer Tools

General-purpose scraping and automation tools.

• EU Tenders Scraper — Scrape EU public procurement notices from TED (Tenders Electronic Daily) with advanced search filters,...
• Web Crawler — This web crawler is designed to provide users with complete flexibility by allowing them to use their **own...

Understanding Proxies:

When scraping data or browsing anonymously, proxies are essential. They act as intermediaries, masking your original IP address and allowing you to send requests from another location.

Why Use Proxies?

• Avoid IP Blocks: By routing requests through proxies, you prevent the target website from recognizing your IP as a scraper or spammer.
• Access Geo-restricted Content: Proxies let you access content or websites restricted by location.
• Enhance Anonymity: Hide your actual IP, ensuring privacy while scraping or browsing.

Types of Proxies

1. Residential Proxies
   • Real IP addresses provided by ISPs to home users.
   • They mimic regular users, making them harder to detect.
   • Best for: Long-term, undetectable scraping, and avoiding blocks.
2. Data Center Proxies
   • IP addresses from servers in data centers.
   • Faster and cheaper than residential proxies but easier to detect and block.
   • Best for: High-speed scraping, but with a higher risk of detection.
3. Mobile Proxies
   • IPs provided by mobile carriers (3G/4G/5G networks).
   • Very difficult to detect, as they appear as regular mobile users.
   • Best for: Mobile-related scraping or avoiding sophisticated blocks.

Rotating Proxies vs. Straight Proxies

• Rotating Proxies: Every request you send goes through a different proxy, making it harder for websites to detect patterns.
• Straight Proxies: All requests are sent through the same proxy, making it easier to track your IP.

FAQs & Troubleshooting

Q: Why did my run return zero restaurants?

OpenTable is protected by Akamai Bot Manager (_abck / ak_bmsc cookies), and datacenter IPs are frequently blocked with "Access Denied". Make sure proxyConfiguration uses the Apify RESIDENTIAL proxy group, and retry with a slightly different query or date — the Actor retries failed seeds once with a fresh proxy session.

Q: What counts as one "result" for pricing?

Pricing is per event: dataset-item charges once per restaurant pushed to the dataset, listing-details once per restaurant with successful profile enrichment, and restaurant-reviews once per restaurant with a completed review extraction. Restaurants scraped as plain listings with no details or reviews enabled only trigger dataset-item.

Q: Can I scrape just the reviews for a single restaurant?

Yes — put the restaurant profile URL (https://www.opentable.com/r/...) in startUrls, set scrapeReviews: true, and optionally cap with maxReviews. Only that restaurant's reviews will be paginated.

Q: What's the difference between startUrls and searchQueries?

startUrls take priority and accept exact OpenTable search URLs (/s?... — useful when you need a specific date/time or map area) or profile URLs. searchQueries are free-text terms (e.g. Dresden, Italian in Berlin) resolved through OpenTable's autocomplete when startUrls is empty.

Q: How do I control my spending on a run?

Use maxItems to cap the number of restaurants and maxReviews to cap reviews per restaurant. Leave scrapeListingDetails and scrapeReviews off for cheap listing-only runs, and enable them only for the restaurants you need enriched. Each event also honors the run-level spending limit you set in Apify.

Q: Why is the availability/date information just a snapshot?

Reservation slots change constantly. The Actor captures availability at run time; treat date, time, and any availability-oriented signals as a point-in-time snapshot. For monitoring, schedule recurring runs (e.g. daily) rather than relying on a single scrape.

About Rigel Bytes

Rigel Bytes specializes in web scraping, automation, and data analytics. We help businesses extract and leverage valuable data for informed decision-making.

Contact Us

Ready to unlock the power of data? Reach out to us at (contact@rigelbytes.com) or book an appointment with us to learn more about how we can help you achieve your data goals.

Detailed Data

Below are two example records from a real run — full JSON is available in the dataset.

[
  {
    "restaurantId": 227388,
    "name": "Peter Pane - Dresden",
    "url": "https://www.opentable.com/r/peter-pane-dresden-am-zwinger-dresden",
    "cuisine": "Burgers",
    "priceBand": "€30 and under",
    "currencySymbol": "€",
    "rating": 4.5,
    "reviewCount": 515,
    "recentReservationCount": 21,
    "neighborhood": "Old Town",
    "city": "Dresden",
    "state": "SN",
    "postCode": "01067",
    "address": "Wilsdruffer Straße 14-16, Dresden, SN, 01067",
    "addressLine1": "Wilsdruffer Straße 14-16",
    "latitude": 51.0515654,
    "longitude": 13.7349184,
    "phone": "0351 48676840",
    "diningStyle": "Casual Dining",
    "hasTakeout": true,
    "outdoorSeating": false,
    "photoUrl": "https://resizer.otstatic.com/v3/photos/92048640-2?width=320&height=320&webp=true",
    "photos": [
      "https://resizer.otstatic.com/v4/photos/92048640-2?width=640&height=360",
      "https://resizer.otstatic.com/v4/photos/32441351-3?width=640&height=360"
    ],
    "searchQuery": "Dresden",
    "detailsFetched": false,
    "reviewsFetched": false,
    "reviews": null
  },
  {
    "restaurantId": 158685,
    "name": "Ayers Rock",
    "url": "https://www.opentable.com/r/ayers-rock-dresden",
    "cuisine": "Australian",
    "priceBand": "€30 and under",
    "currencySymbol": "€",
    "rating": 4.8,
    "reviewCount": 898,
    "recentReservationCount": 11,
    "neighborhood": "Old Town",
    "city": "Dresden",
    "state": "SN",
    "postCode": "01067",
    "address": "Münzgasse 8, Dresden, SN, 01067",
    "addressLine1": "Münzgasse 8",
    "latitude": 51.0529291,
    "longitude": 13.741416,
    "phone": "0351 4901188",
    "diningStyle": "Casual Dining",
    "hasTakeout": false,
    "outdoorSeating": false,
    "photoUrl": "https://resizer.otstatic.com/v3/photos/25341929-1?width=320&height=320&webp=true",
    "photos": [
      "https://resizer.otstatic.com/v4/photos/25341929-1?width=640&height=360",
      "https://resizer.otstatic.com/v4/photos/29522160-4?width=640&height=360"
    ],
    "searchQuery": "Dresden",
    "detailsFetched": false,
    "reviewsFetched": false,
    "reviews": null
  }
]