Google Maps Scraping: The Complete 2026 Guide

Google Maps scraping is the fastest way to build a targeted B2B lead list in 2026. This complete guide covers the API approach, Python scripts, and no-code tools — plus how to extract reviews, what's legal, and which method actually fits your workflow.

Google Maps has over 1 billion monthly active users and 200 million+ businesses listed globally. That's a massive pool of publicly available contact data: names, phones, emails, ratings, hours. The question isn't whether to use it — it's how to do it efficiently.

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Table of Contents

1. What Is Google Maps Scraping?
2. What Data Can You Extract?
3. 3 Methods to Scrape Google Maps in 2026
4. How to Scrape Google Maps Without Code — Step by Step
5. How to Scrape Google Maps Reviews
6. Google Maps Scraper Comparison
7. Is Google Maps Scraping Legal?
8. Common Challenges and Fixes
9. Real-World Use Cases
10. FAQ

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What Is Google Maps Scraping? {#what-is}

Google Maps scraping means automating the extraction of business data from Google Maps listings. Instead of clicking through profiles one by one, you pull thousands of records at once — names, emails, ratings, reviews, hours — and export them to a CSV.

It's not new. But the tools have gotten dramatically better. A cold email agency can now build a list of 10,000 HVAC contractors filtered by review count and star rating in under an hour. No developer needed.

Why does this matter now? "Near me" searches on Google grew 150%+ over the past five years. The B2B web scraping market is projected to hit $2.7 billion by 2027. And most of the contact data you need for local business outreach is sitting right there on Google Maps, publicly accessible.

People use Google Maps data extraction for three main things:

• Lead generation — building prospect lists filtered by phone, email, star rating, or review count
• Competitive intelligence — mapping every competitor in a city and comparing their review scores
• Market research — answering questions like "How many Italian restaurants in Chicago have fewer than 20 reviews?"

One distinction worth understanding early. There are two types of Google Maps data. Segmentation data — ratings, review counts, claimed status — helps you sort prospects into campaign groups. A 4.8-star business with 300 reviews needs a different pitch than a 2.9-star place with 6 reviews. Then there's analytical data — actual review text, recurring keywords, sentiment trends — which is more useful for competitive intel. Different goals, different approaches.

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What Data Can You Extract From Google Maps? {#what-data}

More than most people expect. A single Google Maps listing can contain:

Field	Example
Business name	Joe's Plumbing
Full address	123 Main St, Chicago, IL
Phone number	+1-312-555-0100
Email address	[email protected]
Website	joesplumbing.com
Google rating	4.3 stars
Review count	87 reviews
Business categories	Plumber, Emergency Plumber
Hours	Mon–Fri 8am–6pm
GPS coordinates	41.8781° N, 87.6298° W
Social media profiles	Facebook, Instagram
Claimed listing	Yes / No

That's twelve fields from a single listing. The right Google Maps scraper can pull all of them at once, across thousands of businesses, in minutes.

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3 Methods to Scrape Google Maps in 2026 {#3-methods}

Three real options exist. Each has a different cost-to-effort trade-off.

Method 1 — The Google Maps API (Official)

Google's Place Details API returns structured JSON for any listing, given its Place ID. It's the cleanest approach — official, documented, reliable.

Setup: create a project in Google Cloud Console, enable the Places API (New), grab your API key. Here's a working Python script:

import requests
import json

API_KEY = "YOUR_API_KEY"
PLACE_ID = "ChIJN1t_tDeuEmsRUsoyG83frY4"

url = f"https://places.googleapis.com/v1/places/{PLACE_ID}"
headers = {
    "Content-Type": "application/json",
    "X-Goog-Api-Key": API_KEY,
    "X-Goog-FieldMask": "displayName,rating,userRatingCount,reviews"
}

response = requests.get(url, headers=headers)
data = response.json()

print(f"Name: {data['displayName']['text']}")
print(f"Rating: {data['rating']}")
print(f"Reviews: {data['userRatingCount']}")
print(f"Review samples: {len(data.get('reviews', []))}")

with open("place_details.json", "w") as f:
    json.dump(data, f, indent=2)

Run this and you get the business name, rating, total review count, and — here's the catch — five review samples. Not fifty. Not five hundred. Five. That's the hard limit unless you own the listing through Google Business Profile.

Cost is the other issue. Google charges $17 per 1,000 Place Details requests for basic fields. Pull data on 50,000 businesses and you're looking at $850 just for names and ratings. Add photos or reviews and the bill climbs fast.

The API works well for prototyping and small datasets. For anything past a few thousand records, it gets expensive quickly.

Method 2 — Python Scraper (DIY)

You write a script, launch a headless browser, navigate Google Maps, and pull data directly from the page. Here's a working Playwright example:

import asyncio
from playwright.async_api import async_playwright
import json

async def scrape_google_maps(query, max_results=10):
    async with async_playwright() as p:
        browser = await p.chromium.launch(headless=True)
        page = await browser.new_page()
        await page.goto(f"https://www.google.com/maps/search/{query}")
        await page.wait_for_timeout(3000)

        results = []
        listings = await page.query_selector_all('[role="feed"] > div')

        for listing in listings[:max_results]:
            try:
                name_el = await listing.query_selector('[class*="fontHeadlineSmall"]')
                name = await name_el.inner_text() if name_el else "N/A"
                rating_el = await listing.query_selector('[role="img"]')
                rating = await rating_el.get_attribute("aria-label") if rating_el else "N/A"
                results.append({"name": name, "rating": rating})
            except Exception:
                continue

        await browser.close()
        return results

data = asyncio.run(scrape_google_maps("Italian restaurants Chicago"))
print(json.dumps(data, indent=2))

This works. Until it doesn't. Google Maps loads everything through JavaScript, so you need to handle infinite scroll. DOM class names change — sometimes within weeks. Google's anti-bot system blocks IPs that send too many requests. And no matter what you try, you're capped at roughly 120 results per search.

Budget 2–5 hours for initial setup. Then budget ongoing time for maintenance, because this breaks regularly. It's the right choice for developers who need custom output or have zero budget. For everyone else, the time cost is brutal.

Method 3 — No-Code Scraping Tools (Fastest)

No terminal. No proxy headaches. No fixing broken CSS selectors at midnight.

No-code Google Maps scraping tools — IBLead, Apify, Outscraper, and others — handle the infrastructure for you. They manage anti-bot detection, maintain their own indexes, and hand you clean CSV exports.

IBLead takes a different architectural approach than most. Instead of scraping Google Maps at the moment you search, IBLead maintains a pre-indexed database of 50M+ businesses across 37 countries, updated weekly. You search, filter, and export instantly — no waiting for a scrape to run, no gaps in coverage for cities nobody has searched recently.

The practical difference: if you want every dentist in Germany, IBLead already has them indexed. You filter, hit export, and the CSV is ready in seconds.

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How to Scrape Google Maps Without Code — Step by Step {#no-code}

Using IBLead, the whole process takes about three minutes.

Step 1: Create your account. Sign up at app.iblead.com/register. The trial gives you 200 credits to test your first target market.

Step 2: Choose category and location. IBLead covers thousands of Google Maps categories. Type "plumber," "dentist," "Italian restaurant" — whatever you're targeting. Then set your geography: a city, a region, a postal code prefix, or an entire country. You can search an entire country from the Starter plan.

Step 3: Apply filters. This is where the list gets useful. Filter by minimum Google rating, number of reviews, whether the business has an email address, a website, a claimed Google listing. IBLead also lets you filter by the 160+ web technologies it detects — so you can find, say, all plumbers in Texas running WordPress but not using Google Ads.

Step 4: Export. Hit export and get a CSV. Fields include business name, phone, email, website, rating, review count, social profiles, GPS coordinates, and more — 50+ fields per listing.

The result is clean, structured, deduplicated data. $52 for 10,000 leads — that's $0.005 per contact.

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How to Scrape Google Maps Reviews {#reviews}

The star rating tells you something. The review text tells you everything.

A 4.2-star restaurant where every review mentions "long wait times" is a completely different prospect from a 4.2-star place where people rave about the food. For anyone selling to local businesses, that distinction matters.

The numbers back this up. Businesses with 4.5+ stars get 29% more clicks than lower-rated competitors (BrightLocal, 2024). Reviews don't just provide intel — they predict which businesses are winning customers.

The problem: Google's Place Details API returns 5 reviews maximum per listing. That's the hard limit. Unless you own the listing through Google Business Profile, official channels won't give you more.

Three ways around this:

1. Accept the 5-review limit — use the API for star breakdowns and review counts, skip the text
2. Python with Playwright — navigate to the reviews tab and scroll through them programmatically. Works for moderate volumes. Breaks when Google updates its frontend.
3. IBLead — scrapes up to 500 Google reviews per listing, including full text, rating, date, and author. No business ownership required. No coding. This is an exclusive feature — no direct competitor does this at scale.

How people actually use review data in practice:

• Target businesses under 3 stars with fewer than 10 reviews — they're struggling and open to paying for help
• Scan 1-star reviews for words like "rude" or "dirty" to build pre-qualified prospect lists for service improvement tools
• Track competitor review sentiment over time to spot when they're losing customers
• Find businesses where reviews mention specific pain points that match what you're selling

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Google Maps Scraper Comparison {#comparison}

Tool	Type	Reviews	Country-Level Search	Price
IBLead	Pre-indexed DB + API	Up to 500 per listing	✅ All plans	$52 / 10K leads
Google Maps API	Official API	5 max	✅	$17 / 1K requests
Outscraper	No-code + API	✅	✅	Pay-per-use
Apify	Cloud scraper	✅	✅	Pay-per-use
DIY Python	Custom	✅	⚠️ 120-result cap	Dev time
n8n workflow	Automation	Limited	⚠️ Needs data source	Varies

A few notes. Outscraper charges per record and the final bill is unpredictable at scale. Apify is built for developers — powerful, but not a point-and-click experience. DIY Python gives you full control but requires ongoing maintenance. n8n has a Google Maps scraper workflow rated 4.6/5 that's genuinely useful, but it needs a data source like SerpAPI behind it.

IBLead's key structural difference: it doesn't depend on Google's 120-result search cap because it maintains its own index. You're not triggering a live scrape — you're querying a pre-built database. That's why country-level exports work on every plan, not just enterprise tiers.

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Is Google Maps Scraping Legal? {#legal}

Short answer: scraping publicly available business data from Google Maps is legal in most cases.

The foundational case is hiQ Labs v. LinkedIn (9th Circuit, 2022). The court ruled that scraping publicly accessible data doesn't violate the Computer Fraud and Abuse Act. That ruling covers the kind of Google Maps business data we're talking about — names, phones, addresses, ratings, review counts.

GDPR in Europe treats B2B data differently from personal data. Pulling a restaurant's phone number for commercial outreach generally falls under legitimate interest (Article 6, GDPR). Pulling individual reviewer names and personal details is a different situation — be careful there.

Google's Terms of Service technically prohibit automated access. But a ToS violation is a contractual dispute, not a crime. Courts have consistently drawn a line between "a website says you can't do this" and "the law says you can't do this." Those are different things.

What's safe: business names, addresses, phone numbers, ratings, review counts, hours, categories, websites. What to watch: individual reviewer names, personal email addresses, anything that looks like personal data rather than business data.

Stick to B2B contact info for prospecting and you're operating in well-established legal territory in the US and most of Europe.

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Common Challenges and Fixes {#challenges}

Rate Limiting and IP Blocking

Hit Google too fast and they block your IP — sometimes for hours, sometimes permanently. If you're running your own scraper, proxy rotation is the minimum viable setup. Tools like IBLead handle this on their end — you never see it because the data is already indexed.

The 120-Result Cap

Google Maps shows roughly 120 listings per search. Search "restaurants in New York" and you're seeing maybe 2% of what exists. The fix: break searches down by ZIP code or neighborhood to get multiple batches. Or use a tool that maintains its own index and doesn't depend on Google's search limits at all.

The 5-Review Limit

The Place Details API gives you five reviews per listing. No workaround through official channels. For more review data, you need a Playwright script navigating the reviews tab directly, or a tool built specifically for review extraction at scale.

Dynamic JavaScript and DOM Changes

Google Maps is a JavaScript-heavy single-page app. Everything loads dynamically. Class names and element IDs change without warning. Scrapers that worked perfectly for two months break after a Google frontend update. If you're maintaining Playwright scripts, this is an ongoing reality.

Anti-Bot Detection

Google's detection gets more sophisticated every quarter — CAPTCHAs, fingerprinting, behavioral analysis. Tools with dedicated engineering teams adapt quickly. Homemade scrapers often don't.

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Real-World Use Cases {#use-cases}

B2B lead gen at scale. A cold email agency targeting HVAC contractors filtered to businesses with fewer than 10 reviews and ratings under 4 stars. They pulled 10,000+ validated business emails in one shot. The whole list was built in under an hour. No developer involved.

Competitive mapping. A reputation management agency identified every restaurant with 1–2 stars in a metro area and pitched reputation services. Those businesses know they have a problem — cold outreach to warm leads, essentially.

Market sizing. Want to know how many dentists in Dallas have an active website? Or how many plumbers in Phoenix have claimed their Google listing? Ten minutes with a Google Maps scraper gives you that answer. Useful for refining targeting before a campaign launch.

SaaS prospecting. If you sell hotel management software, scrape Google Maps for hotels with reviews mentioning "check-in" or "reservation" complaints. Those properties are pre-qualified — they're experiencing the exact problem you solve.

Automated pipelines. Connect Google Maps data exports to your CRM via Make.com or n8n. Pull businesses matching your criteria, pipe them into your outreach sequence, and let the automation run.

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FAQ {#faq}

Is it possible to scrape Google Maps?

Yes — three ways. Use Google's official API (structured, but limited to 5 reviews per listing and $17 per 1,000 requests). Write a Python scraper with Playwright or Selenium (flexible, high maintenance). Or use a pre-indexed tool like IBLead (fastest option — search, filter, export in minutes). Most non-technical users go with the third option.

Is it legal to scrape Google Maps data?

For public business data — names, phones, addresses, ratings — yes. The hiQ Labs v. LinkedIn ruling (9th Circuit, 2022) confirmed that scraping publicly accessible data isn't a federal crime. GDPR covers B2B data under legitimate interest in most cases. Google's ToS prohibits it, but ToS violations are contractual disputes, not criminal offenses.

What is the 120-result limit on Google Maps?

Google Maps only returns about 120 listings per search query. It's baked into the platform. To get around it, split searches by ZIP code or neighborhood. Or use a tool that maintains its own pre-indexed database — IBLead covers 50M+ businesses and doesn't depend on Google's search results at all.

How do I get more than 5 reviews per listing?

The official API caps you at 5 reviews unless you own the listing. For more, build a Playwright script that navigates to the reviews tab and scrolls through them — it works but requires maintenance. IBLead scrapes up to 500 reviews per listing including full text, rating, date, and author. No ownership required.

Can I scrape Google Maps with Python for free?

Technically yes — Playwright and Selenium are free libraries. But setup takes 2–5 hours, and you'll spend ongoing time fixing broken selectors whenever Google updates its frontend. Factor in proxy costs for anything beyond light testing. Free in money, expensive in time.

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Three paths to scrape Google Maps in 2026. The API is official but expensive and limited. Python gives you control but eats your time. Pre-indexed tools give you the data without the infrastructure headache.

IBLead covers 50M+ businesses across 37 countries, updated weekly. 50+ fields per listing. Up to 500 reviews per business. 160+ web technologies detected. Export in seconds, not hours.

Start free — 200 credits, no card required