How to Scrape Google Maps in 2026 (Without Getting Blocked)

The fastest way to scrape Google Maps in 2026 is to use a managed cloud scraper like g-maps-scraper.com — pick a city, pick a business type, download the CSV. If you want to do it yourself with code, you'll need Playwright, a residential proxy pool, and a grid-search algorithm. This guide covers all three approaches and explains exactly when to choose each.

Method 1: The cloud scraper (5 minutes, no code)

If you only need the data — not the technical experience — this is the right choice for 95% of people. Here's the entire workflow:

1. Sign up. Create an account at g-maps-scraper.com. 30 free searches, no credit card.
2. Pick a city and a category. The interface autocompletes from 33,000 cities worldwide. The category can be anything Google Maps recognizes — "dentist," "italian restaurant," "real estate agency," "yoga studio."
3. Click search. A worker spins up in the cloud and starts grid-searching the city. Most jobs finish in 2–10 minutes depending on city size.
4. Download the CSV. Open it in Excel, Google Sheets, or import directly into your CRM.

There's nothing else. You don't manage proxies, you don't fight CAPTCHAs, you don't watch your code break when Google ships a UI tweak.

Method 2: Manual scraping (slow, but free)

If you only need a handful of leads, you can do it by hand:

1. Open maps.google.com in an incognito window.
2. Type your search ("dentist Berlin").
3. Scroll the left-hand result panel to the bottom — Google loads results in batches.
4. Click each pin and copy the name, address, phone, and website into a spreadsheet.

This works fine for 10–20 prospects. Beyond that, your wrist hurts and Google starts capping your search at ~120 results because it doesn't expect a single user to need more.

Method 3: DIY with Playwright (the developer route)

If you want full control and don't mind maintaining code, here's the rough shape of a Playwright-based scraper:

from playwright.async_api import async_playwright

async def scrape_maps_search(query: str, lat: float, lng: float, zoom: int = 15):
    async with async_playwright() as p:
        browser = await p.chromium.launch(headless=True)
        context = await browser.new_context(
            user_agent="Mozilla/5.0 ...",
            viewport={"width": 1280, "height": 800},
        )
        page = await context.new_page()
        url = f"https://www.google.com/maps/search/{query}/@{lat},{lng},{zoom}z"
        await page.goto(url, wait_until="networkidle")

        # Scroll the results pane until no more results load
        results_panel = page.locator('div[role="feed"]')
        previous_count = 0
        for _ in range(40):
            await results_panel.evaluate("el => el.scrollTo(0, el.scrollHeight)")
            await page.wait_for_timeout(800)
            current_count = await page.locator('div[role="feed"] > div').count()
            if current_count == previous_count:
                break
            previous_count = current_count

        # Collect all business URLs
        urls = await page.locator('a[href*="/maps/place/"]').evaluate_all(
            "els => els.map(e => e.href)"
        )
        await browser.close()
        return urls

That's just the search step. You then need a second scraper for each business URL to pull the structured data, plus a third pass to crawl websites for emails. Plus a grid algorithm so you can scrape an entire city, not just one viewport.

The hard parts

Writing the code is the easy part. The hard parts are:

• Bot detection. Google Maps fingerprints headless browsers aggressively. You need playwright-stealth and rotated residential proxies (not datacenter IPs — those get blocked instantly).
• The 120-result cap. A single Maps search will only ever return ~120 results, no matter how many businesses exist. You bypass this with a grid search: split the city into a mesh of small geo cells and run a separate query at each cell's center coordinates, then de-duplicate the union.
• Selectors break. Google ships UI changes every few weeks. Every time they tweak a class name or a DOM structure, your selectors break and the scraper silently returns nothing.
• Rate limiting. Hit Maps too fast and you get a CAPTCHA wall. The polite cadence is roughly 1 search per 3–5 seconds per IP.
• Email extraction. Maps almost never shows emails. To get them, you visit each business's website and parse contact pages, footers, "about" pages, and mailto: links — a whole separate sub-scraper.

How to avoid getting blocked

Whether you're using your own scraper or a managed service, the core anti-block tactics are the same:

• Use residential proxies, not datacenter. Datacenter IPs are flagged within minutes.
• Rotate user agents and viewport sizes to look like real browsers.
• Add jitter to delays — don't use exactly 3.0 seconds between requests, use a random 2.5–4.5 second range.
• Stealth plugin. playwright-stealth patches the dozen or so browser fingerprint leaks that Maps checks.
• Don't log in. Logged-in scraping risks the account, not just the IP.
• Distribute load. Multiple workers across multiple proxies finish faster and look less suspicious than one worker hammering away.

Which method should you actually use?

For everything in the middle — and "the middle" is where most users live — a managed scraper is dramatically cheaper than the engineering hours required to build and maintain your own.

Try it now

Start with 30 free searches at g-maps-scraper.com. If the data quality matches what you need, upgrade to a paid tier and run as many cities as you want.

Next: How does email extraction from Google Maps actually work?