Google Maps Scraping in 2026: The Complete Guide (No-Code, Python & API)

By François — Scrap.io | Last updated: July 2026

I spent a week last year helping a buddy set up a cold email campaign for his roofing company. We needed leads — specifically, roofers in the Dallas-Fort Worth area with bad ratings and no website. Guess where we found them all? Google Maps. The platform sits on over a billion monthly users (Google via Statista), and it's the number-one place people check before choosing a local business — 83% of consumers use it, ahead of every other review platform (BrightLocal, 2025). That's a ridiculous amount of contact data just... sitting there.

Google Maps scraping is basically automating that process. Instead of clicking through listings one at a time and copying phone numbers into a spreadsheet like it's 2009, you use a script or tool to grab thousands of records at once. Names, emails, ratings, reviews, hours — all of it, exported to a CSV before your coffee gets cold.

This guide breaks down how to scrape Google Maps in 2026 using three methods: the official API, Python code, and no-code tools. We'll cover reviews extraction, what's legal (and what's gray), the tools worth paying for, and real campaigns that actually worked. We also do web scraping Google Maps for a living at Scrap.io, so — fair warning — we have opinions.

What Is Google Maps Scraping? (And Why It Matters in 2026)

Google Maps scraping is the automated extraction of public business data — names, addresses, phones, emails, ratings, reviews — from Google Maps listings into a structured file like a CSV or Excel sheet. You do it with the official API, a custom script, or a no-code tool. The point: turn thousands of listings into a usable prospect list in minutes instead of weeks.

Somebody on r/coldemail posted last month about extracting 10,000+ validated emails from Google Maps in a single campaign. HVAC contractors. Filtered by review count and rating. The thread blew up. That's Google Maps scraping working exactly as it should.

Why does this matter now? A few reasons, and I'll be blunt about them. "Near me" searches on Google keep climbing (Think With Google). Local intent is baked into how people search. And Google Maps still sits on 225,676,406 indexed businesses across 195 countries with publicly available contact data (Scrap.io, July 2026). Everyone's already doing this — the question is whether you're doing it efficiently or wasting time.

Most people use Google Maps data extraction for lead gen. That's the bread and butter: building prospect lists filtered by phone, email, star rating. But competitive intelligence is a close second — you can map out every competitor in a city and compare their review scores in minutes. And then there's market research. Stuff like: "How many Italian restaurants in Chicago have fewer than 20 reviews?" That's a ten-minute answer when you know how to pull the data.

One thing worth understanding early. There are really two flavors of Google Maps data. Segmentation data — ratings, review counts, rating breakdowns — is what you use to sort prospects into groups for campaigns. A 4.8-star business with 300 reviews doesn't need the same pitch as a 2.9-star place with 6 reviews. Obviously. Then there's analytical data — actual review text, recurring keywords, sentiment shifts over time. That's more for competitive intel work. Different animals, different approaches.

What Data Can You Extract From Google Maps?

Way more than you'd think. Google Maps data extraction goes deep if you have the right Google Maps scraper. Here's the full picture:

That's twelve fields from a single listing. You can extract phone numbers from Google Maps on their own, or grab everything at once with the right setup. What you actually need depends on the campaign.

Here's what that looks like inside a real no-code tool — you pick a category, pick a location, and the search does the rest:

3 Ways to Scrape Google Maps in 2026

OK so — how to scrape Google Maps. You've got three real options here. The official Google API, which is clean but expensive. Rolling your own Python scraper, which is free but painful. Or using a no-code tool that does the heavy lifting for you. (Spoiler: most people — including a lot of developers I know — end up going with option three.)

Method 1 — The Google Maps API (Place Details)

This is the official path. Google's Place Details API returns structured JSON for any listing as long as you have its Place ID.

Getting started is pretty quick. Set up a project in Google Cloud Console, turn on the Places API (New), copy your API key. If you get lost, we wrote a guide on getting your Google Maps API key that walks through the whole thing with screenshots.

Here's a Python script that actually works — I've run it myself:

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']}")

You run this, you get back the business name, rating, total reviews, and... five review samples. Just five. Not fifty, not five hundred. Five reviews maximum — unless you literally own the business through Google Business Profile. I remember the first time I hit that wall. Thought my code was broken. Nope. That's just how it works.

Then there's the cost. Place Details runs about $17 per 1,000 requests at the Pro field tier (Google Maps Platform pricing, 2026). Pull data on 50,000 businesses and that's $850 for names and ratings alone. Photos? Reviews? More. Way more. And here's the gotcha most people miss: the tier is set by which fields you ask for — request one Pro-tier field and the whole response bills at Pro pricing. Run the numbers yourself with the Google Maps API pricing calculator before committing.

The API is great for dev prototyping and small datasets. For anything past a few thousand records? Too slow, too expensive.

Method 2 — Python Scraper (DIY)

You write a script, launch a headless browser, have it navigate Google Maps, and pull data straight off the page. Sounds cool. Is cool — until something breaks at 3 AM and you realize you're maintaining infrastructure instead of closing deals.

You'll need Python 3.9+, either Playwright or Selenium, and patience. 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)
        listings = await page.query_selector_all('[role="feed"] > div')
        # ...parse name, rating, then close
        await browser.close()

Looks neat on screen. Reality is messier. Google Maps loads everything through JavaScript, so you need to deal with infinite scroll. The DOM class names? They change. I've seen selectors go stale in under two weeks. And Google's anti-bot system doesn't play nice — too many requests and your IP is toast. Oh, and you max out at roughly 120 results per search no matter what you try.

Plan on 2–5 hours for initial setup. Then plan on regular fixes, because this stuff breaks constantly. If you'd rather stand on someone else's code, there's a whole ecosystem of open-source scrapers on GitHub worth knowing — the most-cited one, gosom/google-maps-scraper (written in Go, ~3,600 stars), pulls around 120 places per minute with a proper proxy setup. It's genuinely good. It also doesn't extract emails, and you own the proxies and the fixes. We also have a dedicated guide to scrape Google Maps reviews with Python if you want the full walkthrough. You can extract GPS coordinates too with similar scripts.

Who is this for? Developers who need very custom output or who have zero budget. Google Maps scraping with Python gives you total control, sure. But the time cost is brutal.

Method 3 — No-Code Scraping Tools (Fastest Route)

No terminal. No proxy headaches. No fixing broken CSS selectors on a Sunday.

A bunch of Google Maps scraping tools handle everything for you: Scrap.io, Outscraper, Apify, GmapsExtractor, various Chrome extensions. They run the infrastructure, manage anti-bot stuff, and hand you clean CSV or Excel files. If you're a developer who still wants automation, most of them — Scrap.io included — expose a Google Maps scraper API so you can wire the data straight into your own pipeline.

Why Scrap.io specifically? Because it's the only one I've seen that lets you pull every single listing in an entire country with two clicks. Not a neighborhood. Not a city. A country. 225 million+ businesses indexed, 195 countries covered, zero duplicate records in your exports. I've personally done extractions of 140,000+ results and the platform didn't flinch.

How to Scrape Google Maps Without Code — Scrap.io Step-by-Step

Google Maps scraping without code takes four steps. I'm not exaggerating — I've timed it and the whole process takes maybe three minutes if you know what niche you're targeting.

Step 1: Sign up on Scrap.io. Free trial, activated with a card. Thirty seconds. You start with 100 export credits to test your first target market.

Step 2: Category + location. There are 4,000+ categories in the system. Type "plumber" or "Italian restaurant" or whatever you're after. Then pick your geography — could be a single city, a state, or an entire country. I've done "dentists in all of France" in one go. Took a few minutes to process. Worth it.

Step 3: Filters. This part is actually fun. You can filter by minimum rating, number of reviews, whether they have an email on file, a website, social profiles, claimed Google listing — it goes pretty deep. The advanced filtering options are what make or break a good lead list. And here's the part that saves real money: filters are applied before a single credit is consumed. No point paying to export 10,000 businesses when half of them don't have contact info.

Step 4: Hit export. CSV or Excel. Columns include business name, phone, email, website, rating, review count, social links, and a lot more. You can also connect the Scrap.io API if you want to feed data straight into a CRM.

End result: clean, structured, deduplicated data. You can scrape Google Maps without writing code and honestly get better data than most custom Python scripts would produce.

How to Scrape Google Maps Reviews in 2026

The star rating tells you something. But the review text tells you everything. A 4.2-star restaurant where every review mentions "long wait times" is a very different beast from a 4.2-star place where people rave about the pasta. If you're selling to local businesses, those details are your prospecting gold.

And ratings do more than color your intel — they predict clicks. Businesses with 4.5+ stars pull noticeably more traffic than lower-rated competitors (BrightLocal, 2025). So reviews directly signal which businesses are winning customers and which ones are hurting.

The problem? Google's API is stingy. The google maps api review limit is 5 reviews per listing. Five. Unless you own the business listing through Google Business Profile, that's all you're getting through official channels. I've had clients ask me to "just get more reviews from the API" and the answer is always the same: you can't. Google won't let you.

So what do you do? The API works if you just need the star breakdown and don't care about actual review text. Python with Playwright can scrape the reviews tab directly — it's complicated but doable for moderate volumes. Our tutorial covers how to scrape Google Maps reviews with Python step by step. And then there's Scrap.io's reviews feature, which pulls rating breakdowns and keyword data at scale — no coding, no business ownership required.

Here's where people get creative with review scraping. An agency I know targets businesses under 3 stars with fewer than 10 reviews — they're clearly struggling and are open to paying for reputation help. Another approach: scan 1-star reviews for words like "rude" or "dirty" and build a prospect list of businesses that need service improvement. Competitors' reviews are also fair game for tracking sentiment shifts. And you can use Google Maps reviews as social proof in your own marketing while you're at it.

Google Maps Scraper Comparison — Tools & APIs (2026)

Enough talk. Let me show you what each Google Maps scraping tool actually offers:

Quick notes on each. Apify's Compass actor is the most-used community scraper out there — rated 4.7/5 across 1,657 reviews (Apify, 2026) — but it bills by compute, not by results, which makes budgeting a guessing game. Outscraper charges per record and the final bill is hard to predict — we did a full Outscraper vs Scrap.io breakdown with real numbers. Bright Data is enterprise-grade and priced for a buyer who runs data pipelines for a living — overkill for finding 500 plumbers in Ohio. HasData nudges you toward its commercial API but works fine for basic dev jobs. GmapsExtractor is fine for basic stuff, but forget about country-level extractions.

Want the full head-to-head? We tested and ranked the 10 best Google Maps scrapers — pricing, data freshness, and where each one falls flat. And if you're weighing a specific one, here's our Octoparse vs Scrap.io comparison.

Is Google Maps Scraping Legal? (2026 Answer)

Scraping publicly available business data from Google Maps — names, addresses, phones, ratings, review counts — is legal in most cases. US courts have backed this repeatedly, and no login is required to see the data. What matters is what you collect (public business info, yes; personal data, careful) and how you use it for outreach.

People always ask this first, so that's the short version. Now the longer one.

The big court case is hiQ Labs v. LinkedIn, 9th Circuit, 2022. The court said scraping publicly available data doesn't violate the Computer Fraud and Abuse Act. That ruling is basically the foundation of web scraping legality in the US right now, and yes — it 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 usually falls under legitimate interest (Article 6, GDPR). Pulling individual reviewer names out of reviews? That's a different story, and I'd be careful there.

What about Google's Terms of Service? They technically say no automated access. But — and this is important — a ToS violation is a contractual dispute, not a crime. Courts have repeatedly 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.

The safe list: business names, addresses, phone numbers, ratings, review counts, hours, categories, websites. The watch-out list: 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 fine pretty much everywhere. Detailed analysis here: Is it allowed to scrape Google Maps?

Common Challenges in Google Maps Scraping (And How to Solve Them)

Rate Limiting and IP Blocking

Hit Google too hard, too fast, and they block your IP. Sometimes for hours, sometimes permanently. If you're rolling your own scraper, proxy rotation is the minimum. Or just use something like Scrap.io where the infrastructure is someone else's problem. More on this trade-off in our DIY vs professional scraper guide.

The 120-Result Cap

Google Maps only returns about 120 listings per search. Type "restaurants in New York" and you're seeing maybe 2% of what actually exists. The fix: break your search down by ZIP code or neighborhood. Or use a tool that's already built its own index and doesn't depend on Google's search limits at all. GeoSearch — a radius or a hand-drawn polygon — is how you carve up a territory without hitting the wall:

The 5-Review Limit

Mentioned this already but it trips people up constantly. The Place Details API gives you five reviews max. Period. Unless you own the listing. No workaround through official channels — you need a dedicated scraper or a tool built specifically for review extraction.

Dynamic JavaScript & DOM Changes

Google Maps is a JavaScript-heavy single-page app. Everything loads dynamically. Class names and element IDs change without notice. I've seen scrapers break after working perfectly for two months, just because Google tweaked one div. If you're maintaining your own Playwright scripts, this is your life now.

Frequent Anti-Bot Updates

Google gets smarter every quarter. CAPTCHAs, fingerprint detection, behavioral analysis. Every update kills a batch of homemade scrapers. Tools that have dedicated engineering teams (Scrap.io, Outscraper, Apify) adapt fast. Your weekend project? Probably won't keep up.

Messy or Stale Listings

Google Maps isn't always right. Businesses move, rebrand, or shut down and never update the listing. If you're building a prospect file, that's wasted outreach — or worse, you're pitching a company that's already gone. A tool pulling data in real time helps; so does the ability to scrape closed businesses on purpose (a whole prospecting angle of its own — think commercial real estate and supplier arbitrage). And to cover a precise zone rather than a fuzzy search radius, polygon targeting is the cleanest way to extract every business in an area:

Real-World Use Cases for Google Maps Scraping

This is the part where theory meets real money. People scrape Google Maps for leads in ways you might not expect, and Google Maps business data extraction is behind some pretty creative campaigns.

B2B lead gen at scale. That cold email agency I mentioned earlier? HVAC contractors, US-wide, filtered to businesses with fewer than 10 reviews and ratings under 4 stars. They pulled 10,000+ validated business emails in one shot (r/coldemail thread). No developer needed. The whole list was built in under an hour. You can find emails on Google Maps as part of any standard extraction.

And here's the number that shows why filtering before you pay matters so much. Pulled fresh from our own index in July 2026: there are 72,507 HVAC contractors on Google Maps in the US. Of those, only 35,193 have a usable email — roughly 48.5% (Scrap.io, July 2026). Think about what that means with a pay-per-record tool. You'd export all 72,507, then delete more than half. With pre-extraction filtering, Scrap.io applies the "has email" filter before consuming a credit — so you pay for ~35,000 contactable businesses, not 72,000 rows of which half are dead ends. Counting is free, by the way. Size the market before spending a cent.

Spying on competitors. Not the James Bond kind. The "map every competitor in your city and compare their review scores" kind. A reputation management agency I follow on LinkedIn used exactly this approach — they identified every restaurant with 1-2 stars in a metro area and pitched reputation services (Alex Berman's LinkedIn post). Those businesses know they have a problem. Cold outreach to warm leads, basically.

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. It's the same reason so many people gripe about the alternatives — one founder on r/Entrepreneur put it bluntly: "I have built my own google maps scraper... but it is really expensive to run." The DIY route sounds free. It rarely is.

SaaS prospecting. If you sell hotel management software, you can scrape Google Maps for hotels with reviews that mention "check-in" or "reservation" complaints. Those properties need what you're selling. You can target businesses with negative reviews to build a list that's basically pre-qualified.

Automated pipelines. Connect Google Maps data to your CRM with Make.com workflows or n8n. Or let an AI do the driving — the Scrap.io MCP for AI agents plugs the live database straight into Claude, ChatGPT or Gemini, so you can pull leads in plain English. Extract all businesses from an area and pipe them straight into your outreach sequence. Set it and forget it — mostly.

Frequently Asked Questions

Is it possible to scrape Google Maps?

Yep — three ways. Use Google's official API (structured but limited to 5 reviews per listing). Write your own Python scraper with Playwright or Selenium (flexible but high maintenance). Or use a no-code tool like Scrap.io (fastest option — handles up to 10,000 requests per minute). Most non-technical people go with option three and never look back.

Is it legal to scrape Google Maps data?

For public business data — names, phones, addresses, ratings — yes. The hiQ Labs v. LinkedIn case (9th Circuit, 2022) confirmed that scraping publicly accessible data isn't a federal crime. GDPR covers B2B data under legitimate interest for the most part. Google's ToS says no scraping, but violating ToS isn't illegal — it's a contract issue. Big difference.

Is Google Maps scraper free?

Sort of — depends how you count "free." Your options: Google's API has a $200/month free credit. Python and open-source repos (like gosom on GitHub) are free but cost you time and proxies. Scrap.io's Chrome extension is free and unlimited for viewing emails on Maps. And Scrap.io's trial gets you 100 leads. After that, paid plans start at $35/month (annual) for 10,000 exports — often cheaper than the API once you run the numbers. Check the API pricing comparison.

What is the fastest Google Maps scraper?

Depends what you mean by fast. If you want zero coding and instant exports, Scrap.io handles up to 10,000 requests/min and entire countries in two clicks. If you're a dev and want consistent API response times, Google's official API works — but you're paying per call. Custom Python scrapers are only as fast as your server and proxy setup allow.

What is the limit of scraping Google Maps?

Multiple limits. Google shows ~120 results per search — that's baked into the platform. The API gives you 5 review samples unless you own the listing. Rate limits depend on your account tier. Scrap.io gets around the 120-result cap because it has its own index of 225M+ businesses worldwide. Different architecture, different limits.

Can Google Maps scraping be detected?

If you're running a homemade scraper, yes — Google's anti-bot systems flag unusual request patterns, browser fingerprints, and speed, then serve CAPTCHAs or block your IP. Dedicated tools like Scrap.io avoid this because they don't hammer Google's live interface in real time; they query their own pre-built index. Detection is a DIY problem, not a data problem.

Is it possible to extract data from Google Maps?

Yes, and a lot of it. Business names, addresses, phones, emails, websites, ratings, review counts, GPS coordinates, social media profiles, hours, categories — that's just the basics. Scrap.io also pulls website technologies, ad pixels, SEO metadata, and multiple classified email addresses (contact, sales, marketing) per business from their websites.

How do I scrape Google Maps reviews without being the business owner?

You can't get more than 5 through the official API. For more, build a Playwright script that navigates to the reviews tab and scrolls through them, or use Scrap.io which gives you rating breakdowns and keyword data without owning anything. Python walkthrough is here: scraping Google Maps reviews with Python.

Can I scrape Google Maps with Python for free?

Technically yes. Playwright and Selenium are free libraries. But your time isn't free, and you'll spend 2-5 hours on setup plus ongoing maintenance whenever Google changes their frontend (which happens often). Factor in proxy costs too if you're doing more than light testing.

What data points does Google Maps include for each business?

Depends on the listing, but up to 40+ fields: name, address, phone, site, email, categories, rating, review count, rating breakdown, hours, GPS coordinates, photos, social profiles, owner responses, and more. Full list is in the data table earlier in this article.

How do I get more than 120 results from a Google Maps search?

Three options. Split your search by ZIP code or neighborhood to get multiple batches of 120. Use API pagination if you're going the developer route. Or use Scrap.io — it doesn't depend on Google's search limits because it maintains its own index of 225M+ businesses. You just tell it what you want and it pulls everything.

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Three paths to scrape Google Maps in 2026, and they're all pretty different. The API is official but pricey and limited. Python gives you control but eats your time. Scrap.io gives you the data without the hassle — 225M+ businesses indexed, real-time on every export, an API and an MCP connector for automation, and a no-code interface that actually works.