Google Maps has data on over 200 million places worldwide. Business names, ratings, review counts, phone numbers, addresses, opening hours, website URLs - all publicly visible, all extremely valuable if you need it at scale. The problem is extracting it reliably in 2026 without running into rate limits, CAPTCHAs, or getting your IP range blacklisted after 50 requests.
This post covers why people scrape Google Maps, what makes it hard, and how to get the data you actually need without building and maintaining a fragile scraper yourself.
Google Maps is one of the most complete business directories on the planet. Unlike industry databases that charge thousands per year for outdated CSVs, Maps data is constantly refreshed by users, business owners, and Google's own crawlers. For certain use cases, there's nothing comparable.
The main reasons people want this data:
Google has a Places API. It's not free at any meaningful scale. The pricing as of 2026:
If you want 10,000 businesses with full details - name, rating, review count, phone, address, website - you're looking at $300-$700 just in API costs. And there are strict rate limits: 600 requests per minute by default, with quota increases requiring a support ticket. For bulk pulls, this is slow and expensive.
The API also doesn't give you review text by default. Review content requires an additional Places Details call per business. At scale this multiplies your costs fast.
Google Maps is one of the harder targets in web scraping. Here's why:
Maps doesn't serve static HTML. The page is a heavy React application that loads business data via internal APIs after the initial shell loads. A simple HTTP request gets you a JavaScript bundle, not business listings. You need a headless browser or a way to intercept the internal API calls that the Maps frontend makes - which requires understanding how those requests are structured and authenticated.
Google has some of the most sophisticated bot detection systems out there. They analyze request patterns, browser fingerprints, mouse movements, scroll behavior, timing between requests, and dozens of other signals. Datacenter IPs get flagged almost immediately. Even residential proxies trigger detection if the request cadence looks robotic or if the browser fingerprint doesn't match a real browser environment precisely.
When Google suspects automation, it serves challenges - reCAPTCHA v3 scoring, invisible challenges based on behavioral signals, and occasionally hard CAPTCHAs that block the page entirely. These don't appear consistently, which makes them harder to handle than a simple "always solve CAPTCHA on step X" flow.
Even when you get past bot detection, Maps rate-limits aggressively. Too many requests from the same IP in a short window triggers throttling or blocks. This means you can't just parallelize naively - you need careful request spacing and IP rotation with clean residential proxies.
Google updates the Maps UI and internal API contracts regularly. A scraper that works perfectly today can break silently after a Maps update. Someone has to maintain the extraction logic and patch it when things change. That's ongoing engineering work, not a one-time build.
There are a few ways people tackle Google Maps extraction:
Clean, reliable, and legal. Expensive at scale and missing review text in most tiers. Good for low-volume lookups where cost isn't a concern. Not practical for bulk pulls of thousands of businesses.
Companies like Outscraper, DataForSEO, and others sell Maps data as a service. Quality varies. They're usually fine for one-off datasets but get expensive fast, and you're dependent on their update cadence and coverage decisions.
Playwright or Puppeteer with residential proxies, custom fingerprinting, careful rate limiting, and a maintenance plan for when Google updates things. Realistic timeline to build something reliable: 2-4 weeks for a developer who knows what they're doing. Ongoing maintenance: several hours per month minimum. This is the approach that makes sense if Maps data is core to your product and you have the engineering resources.
Delegate the infrastructure problem. Someone else handles the proxies, browser fingerprinting, rate limiting, and maintenance. You call an API, you get data. This is the right choice for most use cases - especially if you're a developer, agency, or analyst who needs the data but doesn't want scraping to become a core engineering project.
Our Google Maps Scraper actor on Apify handles all the infrastructure complexity. It runs in a managed cloud environment with residential proxy rotation, anti-detection browser settings, and automatic retry logic. You define what you want to scrape - search queries, specific place URLs, or category + location combinations - and it returns structured JSON.
The actor covers:
Pricing is per-result at $0.005/result - so 1,000 businesses with full details costs about $5. Compare that to $300+ via the Places API for the same dataset.
If you're already pulling job listings data with our LinkedIn Jobs Scraper or product data with our Amazon Scraper, the pattern is identical - same Apify client, same dataset output format.
The actor input is a JSON object. Here's a typical run configuration for pulling coffee shops in Chicago with reviews:
{
"searchQuery": "coffee shops in Chicago",
"maxResults": 100,
"includeReviews": true,
"maxReviewsPerPlace": 10,
"language": "en",
"countryCode": "us"
}
You can also pass specific Google Maps URLs directly if you want data from a known place or search result page:
{
"startUrls": [
{ "url": "https://www.google.com/maps/search/plumbers+near+Austin,+TX" }
],
"maxResults": 50,
"includeReviews": false
}
Each result in the output dataset looks like this:
{
"businessName": "Intelligentsia Coffee",
"rating": 4.6,
"reviewCount": 1842,
"address": "53 W Jackson Blvd, Chicago, IL 60604",
"phone": "+1 312-253-0594",
"website": "https://www.intelligentsia.com",
"category": "Coffee shop",
"openingHours": {
"Monday": "7:00 AM - 6:00 PM",
"Tuesday": "7:00 AM - 6:00 PM",
"Wednesday": "7:00 AM - 6:00 PM",
"Thursday": "7:00 AM - 6:00 PM",
"Friday": "7:00 AM - 6:00 PM",
"Saturday": "8:00 AM - 5:00 PM",
"Sunday": "8:00 AM - 5:00 PM"
},
"latitude": 41.8782,
"longitude": -87.6298,
"plusId": "ChIJN6RuCxMsDogRGE9wy9e1bOA",
"photosCount": 847,
"reviews": [
{
"reviewerName": "Sarah K.",
"rating": 5,
"date": "2026-03-15",
"text": "Best espresso in Chicago, hands down. The single origins they rotate through are exceptional.",
"likesCount": 12
}
]
}
The output goes directly to an Apify dataset that you can download as JSON, CSV, or XLSX - or paginate through via the Apify API if you're processing it programmatically.
A common workflow: pull all HVAC companies, plumbers, or electricians in a target metro. Filter by review count (businesses with 20+ reviews are established enough to have a real budget) and rating (3.5-4.2 stars are often better targets than 5-star businesses, which are usually either tiny or already have a strong marketing partner). Export to CSV, import into your CRM. You're looking at about 500-2,000 qualified leads per major metro at roughly $2-5 in scraping costs.
If you manage a restaurant or a chain, knowing exactly what reviewers praise and criticize about competitors is valuable. Pull review text for 10-20 competitors in your category, run it through a sentiment classifier or just read it manually. You'll quickly see patterns - competitors getting hammered on wait times, praised for specific dishes, complaining about parking. That's real signal for your own positioning.
Agencies doing local SEO need a baseline: how many reviews does the client have vs. the top 3 competitors? What's the review velocity (new reviews per month)? Are competitors responding to reviews? What categories are they listed under? Pulling this data programmatically for 10-20 clients at once takes minutes instead of hours of manual Maps browsing.
For teams building scheduled lead-gen pipelines or competitor monitoring on top of Maps data, reliable hosting matters. Kinsta and DigitalOcean are the two options data engineers reach for most -- DigitalOcean for raw Droplet simplicity, Kinsta when you also want managed infrastructure and CDN caching for any front-end dashboards sitting on top of your data.
Google Maps is genuinely one of the richest sources of local business data available. The official API is expensive for bulk use, and building a reliable scraper from scratch is a significant engineering project that needs ongoing maintenance. For most use cases - lead gen, competitor research, local SEO audits - a managed actor at $0.005/result is the practical answer.
The actor handles the hard parts: residential proxy rotation, browser fingerprinting, rate limiting, and keeping up with Maps UI changes. You define the search and get structured JSON back.
If you're working with other data sources alongside Maps - job listings, product data, social profiles - check out the LinkedIn Jobs Scraper guide and the Amazon Product Scraper guide for the same pattern applied to those platforms.
Questions or edge cases? The actor's Apify page has examples and the input schema documented. If your use case doesn't fit the standard configuration, leave a question there and I'll take a look.
If you need residential proxies for this scraper, Oxylabs offers reliable datacenter and residential proxy pools — same infrastructure used in enterprise-grade web intelligence pipelines.
Want to master web scraping end-to-end? The Complete Web Scraping Playbook 2026 covers proxies, anti-bot bypass, data pipelines, and selling data — all in one PDF guide.
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