How to Scrape Google Maps Reviews, Photos & Local Guide Profiles in 2026 (Beyond the 5-Review API Limit)

If you build software for multi-location franchises, hospitality brands, healthcare clinics, or local SEO agencies, you have almost certainly run into the most frustrating limitation of the official Google Places API:

• No matter how much you pay Google ($17 to $40+ per 1,000 requests), the official Places API returns a maximum of 5 reviews per business location.

You cannot paginate past review #5. You cannot filter by "Newest" to catch a 1-star customer complaint posted 10 minutes ago. You cannot extract full customer photo galleries, and you cannot inspect the public review history of a contributor to tell whether a sudden burst of 1-star reviews is coming from a real customer or a coordinated competitor attack.

In this guide, we break down how data teams, reputation platforms, and local lead generation agencies extract complete Google Maps review histories, high-resolution customer photos, and Local Guide contributor portfolios at scale—either as a Done-For-You Excel/CSV delivery or via self-serve cloud APIs.

Official Google Places API vs. UnitBytes Google Maps Engines#

The 3 Core Pillars of Google Maps Intelligence#

To give businesses complete visibility into their physical locations and competitors, UnitBytes provides three specialized extraction engines that work together:

1. Real-Time Review & Sentiment Tracking (google-maps-reviews-tracker)

For multi-location restaurant groups, dental practices, auto dealerships, and hotel chains, waiting days to notice a 1-star review costs real revenue.

• Sort by Newest: Poll your locations daily or hourly to detect new reviews the moment they are published.
• Owner Response Auditing: Track response_from_owner_text and response_from_owner_date across 500+ franchise branches to verify whether local store managers are replying to unhappy customers within SLA.
• Detailed Sub-Ratings: Capture structured aspect ratings (e.g., Food: 5/5, Service: 2/5, Atmosphere: 4/5) and dining/service metadata.

2. Local Guide & Contributor Intelligence (google-maps-contributor-scraper)

When a business suddenly receives five 1-star reviews in a single afternoon, the first question is: Are these legitimate customers, or fake accounts?

• By passing the reviewer's contributor URL or ID into the Google Maps Contributor & Local Guide Scraper, you can inspect their entire public footprint: Local Guide level, badge count, total reviews written, and every other business they have rated across the map.
• Market researchers also use anonymized contributor footprints to map consumer brand affinities (e.g., "Customers who rate Location A 5 stars also frequently visit Brands B and C").

3. Visual Storefront & Menu Auditing (google-maps-photos-scraper)

A business's Google Maps photo gallery is often the #1 conversion factor for local searchers.

• The Google Maps Photos Scraper extracts high-resolution user-submitted and owner-uploaded photos, upload dates, and contributor metadata—ideal for auditing storefront signage, menu pricing boards, and interior renovations across thousands of competitors.

Two Ways to Get Your Google Maps Data#

Option B: Automate Review Monitoring in Python#

You can integrate the Google Maps Reviews Tracker directly into your backend or BI workflow in under 20 lines of Python:

from apify_client import ApifyClient
import pandas as pd

client = ApifyClient("YOUR_APIFY_TOKEN")

# Track newest reviews across your target Google Maps locations
run_input = {
    "placeUrls": [
        "https://www.google.com/maps/place/Tartine+Bakery/@37.761425,-122.4241037,17z"
    ],
    "maxReviewsPerPlace": 250,
    "sort": "newest",
    "language": "en"
}

run = client.actor("unitbytes/google-maps-reviews-tracker").call(run_input=run_input)

reviews = list(client.dataset(run["defaultDatasetId"]).iterate_items())
df = pd.DataFrame(reviews)

# Filter for urgent 1-star and 2-star reviews without an owner response
urgent_alerts = df[(df["rating"] <= 2) & (df["ownerResponse"].isna())]
print(f"Extracted {len(df)} total reviews ({len(urgent_alerts)} urgent alerts requiring response).")
df.to_csv("google_maps_reviews_audit.csv", index=False)

Sample Extracted Review Payload#

{
  "reviewId": "ChdDSUhNMG9nS0VJQ0FnSUN6cXZyNjdBRRAB",
  "placeTitle": "Tartine Bakery",
  "reviewerName": "Sarah Jenkins",
  "reviewerLocalGuideLevel": 6,
  "reviewerTotalReviews": 84,
  "rating": 5,
  "publishedAt": "2026-10-14T16:42:10Z",
  "reviewText": "Best morning bun and country bread in San Francisco. Line moves fast even on weekends!",
  "ownerResponse": "Thank you for visiting us, Sarah! See you again soon.",
  "reviewPhotos": [
    "https://lh5.googleusercontent.com/p/AF1QipN_sample_high_res_photo_1"
  ]
}

Ready to Monitor Your Locations or Audit Competitors?#

Deploy any of the three Google Maps engines self-serve on Apify Cloud, or use the Done-For-You Dataset Request Form below to have our engineering team deliver your custom review or location dataset in Excel, CSV, or BigQuery.