Google Maps Reviews and Business Data: API Guide

For B2B sales teams and local SEO agencies, Google Maps is the world’s largest database of leads. Whether you need to extract business emails, phone numbers, or analyze customer sentiment from reviews, the data is there, but getting it out is a nightmare.

In this guide, we compare the three main ways to scrape Google Maps in 2026: The Open Source Way (Free but unstable), The Subscription Way (Apify/Outscraper), and The API Way (SearchCans).

Key Takeaways

• SearchCans’ Google Maps endpoint ("t": "maps") returns structured JSON of reviews, ratings, business info, and phone numbers , no Selenium or infinite scroll logic required

• SearchCans uses a credit-based API model; compare the current pricing and credit usage with the volume, fields, and review depth your Maps workflow needs.

• Three use cases dominate Maps data extraction: lead generation (business contacts), sentiment analysis (review monitoring), and competitive intelligence (competitor rating comparisons)

• DIY Maps scrapers fail fast in 2026 , Google’s obfuscated class names, dynamic DOM, and anti-bot fingerprinting break Selenium scrapers within days of deployment

Why Scraping Google Maps is Harder Than Search

Unlike standard Google Search results (which are static HTML), Google Maps is a heavy, dynamic web application.

Infinite Scroll

You can’t just curl a URL. You need to simulate scrolling to load more reviews.

Complex DOM

Google uses obfuscated class names that change frequently, breaking DIY scrapers instantly.

Data Volume

A single popular restaurant might have 5,000 reviews. Extracting them all requires thousands of interactions.

Method 1: The Open Source / DIY Route

If you browse GitHub, you will find tools like google-maps-reviews-scraper. These typically use Selenium or Playwright to control a Chrome browser.

The Workflow:

1. Launch a headless browser.

1. Navigate to the business URL.

1. Click “More reviews”.

1. Scroll down… wait… scroll down… wait.

The Problem:

While free, this method is slow and fragile. If Google detects your automated driver (which is easy in 2026), you get CAPTCHA-blocked immediately. Furthermore, managing the infinite scroll logic for thousands of items is computationally expensive.

For more on resilient request handling, see our guide to fixing 429 errors.

Method 2: The Subscription SaaS (Apify / Outscraper)

Platforms like Apify and Outscraper offer specialized “Google Maps Scrapers” as a service.

Apify

Offers a powerful “Google Maps Reviews Scraper” actor. It extracts reviews, rating, text, and owner responses.

Cost

You usually pay a monthly subscription (e.g., $49/mo) for “Compute Units.” Large scrapes can drain these units quickly.

Outscraper

Positions itself as a “Pay as you go” service but focuses on exporting to CSV/Excel for non-coders.

The Verdict:

These tools are great for non-technical users who want a CSV file, but for developers building an automated pipeline, the “per-result” pricing or monthly subscription can be overkill.

Method 3: The Developer’s API (SearchCans)

If you are a developer building a Lead Gen tool or a Review Monitor, you want raw JSON data via an API, without managing browser infrastructure or paying huge monthly fees.

SearchCans provides a dedicated Google Maps endpoint that handles the scrolling and parsing for you.

Comparison: Cost & Flexibility

For a complete price breakdown, check out our 2026 pricing comparison.

Code Example: Extracting Reviews with Python

Instead of writing 200 lines of Selenium code, you can get reviews with one request:

import requests

API_KEY = "YOUR_SEARCHCANS_KEY"

def get_reviews(query):
   url = "https://www.searchcans.com/api/v1/search"
   params = {
       "s": query,          # e.g., "Starbucks New York reviews"
       "t": "maps",         # Use the Maps engine
       "d": 20,             # Number of results
       "p": 1
   }
   headers = {
       "Authorization": f"Bearer {API_KEY}",
       "Content-Type": "application/json"
   }

   response = requests.post(url, json=params, headers=headers)
   return response.json()

# Result: Structured JSON with reviewer name, rating, text, and date.
# Similar to SerpApi's structure but at a fraction of the cost

Use Cases for Maps Data

1. Lead Generation

Extract business contact information for B2B outreach:

def extract_business_info(location, category):
   query = f"{category} in {location}"
   data = get_reviews(query)

   businesses = []
   for item in data.get('data', []):
       businesses.append({
           'name': item.get('title'),
           'address': item.get('address'),
           'phone': item.get('phone'),
           'rating': item.get('rating'),
           'website': item.get('website')
       })

   return businesses

# Extract all restaurants in Manhattan
restaurants = extract_business_info("Manhattan NY", "restaurants")

2. Sentiment Analysis

Monitor customer sentiment across multiple locations:

def analyze_reviews(business_name):
   reviews = get_reviews(f"{business_name} reviews")

   positive = 0
   negative = 0

   for review in reviews.get('data', []):
       rating = review.get('rating', 0)
       if rating >= 4:
           positive += 1
       elif rating <= 2:
           negative += 1

   return {
       'positive': positive,
       'negative': negative,
       'sentiment': 'positive' if positive > negative else 'negative'
   }

3. Competitive Intelligence

Track how your business compares to competitors:

def compare_competitors(your_business, competitors):
   all_businesses = [your_business] + competitors
   comparison = {}

   for business in all_businesses:
       data = get_reviews(business)
       comparison[business] = {
           'avg_rating': data.get('avg_rating'),
           'total_reviews': data.get('total_reviews'),
           'recent_reviews': len([r for r in data.get('data', []) if r.get('is_recent')])
       }

   return comparison

Integration with CRM Systems

Automatically enrich your CRM with Maps data:

def enrich_crm_leads(company_names):
   enriched_data = []

   for company in company_names:
       maps_data = get_reviews(company)

       enriched_data.append({
           'company': company,
           'phone': maps_data.get('phone'),
           'website': maps_data.get('website'),
           'rating': maps_data.get('avg_rating'),
           'address': maps_data.get('address')
       })

   return enriched_data

Best Practices

1. Rate Limiting: Even with concurrent lanes, add small delays between requests when the source or workflow requires it

1. Data Validation: Always validate extracted phone numbers and emails

1. Duplicate Detection: Maps data can have duplicate listings

1. Geo-targeting: Use specific location parameters for better results

For more on handling high-volume scraping, see our guide on scaling AI agents with Parallel Lanes.

Rule G+: The “Not For” Clause: SearchCans is purpose-built for real-time data acquisition via the Google Maps engine. It is NOT a full Google Maps Platform replacement (no routing, no directions API, no real-time distance matrix). Use SearchCans to extract public business listings, reviews, and contact data at scale , not for navigation or geospatial computation.

Frequently Asked Questions

Q: What data can SearchCans extract from Google Maps?

A: SearchCans’ Maps endpoint returns structured JSON including business name, address, phone number, website, average rating, total review count, and individual review text. This covers lead generation, sentiment analysis, and competitive intelligence use cases.

Q: How does SearchCans compare to Apify for Google Maps scraping?

A: Compare the current plan and credit terms for each provider. SearchCans returns structured API data, while the total cost depends on the endpoint, fields, retries, and review depth.

Q: Can I extract reviews from businesses with thousands of ratings?

A: Yes. SearchCans handles infinite scroll and DOM traversal server-side. Use the p parameter to page through additional results beyond the first batch.

Q: How should I plan concurrency for SearchCans Maps API calls?

A: SearchCans uses a Parallel Lanes model. Concurrency depends on the selected plan and available lanes, so add retries, backoff, and source-specific compliance checks rather than assuming capacity is unbounded.

Q: Does SearchCans store the Maps data it fetches?

A: No. SearchCans operates as a transient pipe , data is discarded from RAM once delivered to your application, supporting GDPR and CCPA compliance for enterprise pipelines.

Conclusion

For one-off exports, a CSV tool may be enough. For applications that monitor reviews or find leads continuously, compare a structured API with DIY maintenance using the same fields, volume, retries, and review requirements.

Start mining local leads today. Check out our complete documentation or explore more web scraping tutorials.

👉 Get your API Key at SearchCans.com

Start your free trial at SearchCans → , 100 credits included, no credit card required.