Google Maps Scraping APIs: Extract Business Data at Scale
Google Maps data drives local SEO, lead generation, competitive analysis, and location intelligence. Business names, addresses, phone numbers, reviews, ratings, hours of operation, and categories — it's the most comprehensive local business database in the world. This guide covers the tools and APIs for extracting Google Maps data programmatically.
Key Takeaways
- Google Maps data is valuable for local SEO, lead gen, and market research — but Google provides no official scraping API
- Specialized Maps APIs (Outscraper, Apify, Bright Data) offer pre-built extraction with structured free JSON formatter output
- General scraping tools (SearchHive, SerpApi) can extract Maps data through search integration
- Pricing ranges from $2–$50 per 1,000 records depending on data depth and provider
- Legal considerations around Google's ToS apply, but structured extraction of public business data is widely practiced
Why Scrape Google Maps?
Google Maps contains data on over 200 million businesses worldwide. Common use cases include:
- Local SEO monitoring — track your business and competitors' listings, reviews, and rankings
- Lead generation — build contact lists for specific industries and locations (e.g., all dentists in Chicago)
- Market research — analyze business density, category distribution, and competitive landscape by area
- Location intelligence — power mapping tools, real estate platforms, and logistics systems
- Review monitoring — track customer sentiment across competitor businesses
Dedicated Google Maps APIs
Outscraper
Best for: Structured Google Maps data with a clean REST API and generous free tier.
Outscraper specializes in Google Maps and Google Search extraction. The Maps API returns business name, address, phone, website, rating, review count, hours, and category in structured JSON.
Pricing: Free (about 100 searches), then $2–$10 per 1,000 records depending on data fields selected.
from outscraper import ApiClient
client = ApiClient(api_key="YOUR_KEY")
results = client.google_maps_search(
"restaurants in Austin TX",
limit=20,
fields=["name", "full_address", "phone", "site", "rating", "reviews", "hours"]
)
for place in results:
print(f"{place['name']} — {place.get('phone', 'N/A')} — Rating: {place.get('rating', 'N/A')}")
Outscraper handles pagination, coordinate-based searches, and bulk extraction across multiple queries. One of the most cost-effective options for pure Maps data.
Apify Google Maps Scraper
Best for: Apify users who want Maps scraping integrated with other scraping workflows.
Apify's Google Maps Scraper actor extracts places data including business info, reviews, and photos. Runs on Apify's cloud infrastructure with automatic proxy rotation.
Pricing: Included in Apify plans — Free (5 CU), Starter $49/mo, Business $149/mo.
from apify_client import ApifyClient
client = ApifyClient("YOUR_TOKEN")
run = client.actor("compass/google-maps-scraper").call(run_input={
"searchQueriesArray": ["coffee shops in Portland OR"],
"maxReviewsPerPlace": 5,
"maxImagesPerPlace": 3
})
for item in client.dataset(run["defaultDatasetId"]).iterate_items():
print(item.get("title"), item.get("address"), item.get("totalScore"))
Limitation: Compute unit pricing means costs scale with complexity, not just record count. Review extraction is compute-heavy.
Bright Data Google Maps API
Best for: Enterprise-scale extraction with residential proxies and geo-targeting.
Bright Data offers a dedicated Google Maps scraping API that handles anti-bot measures through their proxy infrastructure. Supports coordinate-based searches and bulk extraction.
Pricing: Custom enterprise pricing, pay-as-you-go available.
Limitation: Enterprise-focused. Not ideal for small teams or prototyping.
SerpApi Google Maps
Best for: Developers already using SerpApi for search who want Maps data from the same provider.
SerpApi's Google Maps API returns local results with business info, ratings, and coordinates. Same authentication and billing as their search API.
Pricing: Starts at $25/month (1K searches). Maps searches count against your SerpApi monthly limit.
import requests
params = {
"engine": "google_maps",
"q": "plumbers in Miami FL",
"api_key": "YOUR_KEY"
}
results = requests.get("https://serpapi.com/search", params=params).json()
for place in results.get("local_results", []):
print(f"{place['title']} — {place.get('phone', 'N/A')} — {place.get('rating', 'N/A')}")
Limitation: Expensive for Maps-specific use. At $25/month for 1K searches, it costs 25x more than Outscraper's per-record rate.
General Purpose Tools for Maps Data
SearchHive SwiftSearch + ScrapeForge
Best for: Combining Maps search with broader web research and scraping.
SwiftSearch can search Google Maps results, while ScrapeForge extracts detailed business information from Maps listing pages. Combined with DeepDive for research, it's a complete toolkit for local business intelligence.
Pricing: Free (500 credits), Starter $9/mo (5K), Builder $49/mo (100K), Unicorn $199/mo (500K).
import requests
# Search Google Maps for businesses
search = requests.get(
"https://api.searchhive.dev/v1/swiftsearch",
headers={"Authorization": "Bearer YOUR_KEY"},
params={"q": "best sushi restaurants in NYC", "num": 10}
)
# Extract detailed data from Maps listings
urls = [r["url"] for r in search.json().get("local_results", search.json().get("organic", []))[:5]]
if urls:
scrape = requests.post(
"https://api.searchhive.dev/v1/scrapeforge",
headers={"Authorization": "Bearer YOUR_KEY"},
json={"urls": urls, "format": "markdown"}
)
for result in scrape.json().get("results", []):
print(result["url"], result["markdown"][:300])
Advantage: Same credits work for Maps search, business page scraping, competitor research, and review analysis. One API key, one billing relationship.
/blog/google-maps-scraping-apis-extract-business-data-at-scale
Google Places API (Official)
Best for: Applications that can work within Google's official data licensing.
Google's own Places API provides business data with official terms. It's not scraping — it's a licensed API with specific usage requirements and pricing.
Pricing: $17/1K requests (Basic Data), $32/1K (Contact Data including phone/website), $3/1K (Autocomplete). Monthly $200 free credit.
import requests
resp = requests.get(
"https://maps.googleapis.com/maps/api/place/textsearch/json",
params={"query": "restaurants in Austin TX", "key": "YOUR_KEY"}
)
for place in resp.json().get("results", []):
print(place["name"], place.get("formatted_address"), place.get("rating"))
Limitation: $17–$32 per 1,000 requests is expensive. $200 monthly credit helps but doesn't last long at volume. Field masking required to control costs. Strict usage policies.
Best Practices for Maps Data Extraction
Start with specific queries. Broad searches ("restaurants in USA") return too many results and consume excessive credits. Narrow by city, neighborhood, or category.
Extract reviews separately. Review data is valuable but expensive to scrape. Extract reviews only for your target businesses, not every result.
Cache results with TTL. Business data changes slowly — daily or weekly refreshes are usually sufficient. Don't re-scrape the same queries on every run.
Use coordinate-based searches. When you need businesses in a specific area, use lat/lng coordinates instead of address queries for more accurate geographic coverage.
Normalize data across sources. Different APIs return slightly different field names and formats. Build a normalization layer to standardize your data regardless of source.
Recommendation
For pure Google Maps data extraction, Outscraper offers the best value — clean API, structured output, and competitive per-record pricing. For small-to-medium volumes (under 10K records/month), it's hard to beat.
For teams that need Maps data as part of a broader data pipeline (finding businesses, scraping their websites, researching competitors), SearchHive provides the most complete toolkit. SwiftSearch finds businesses, ScrapeForge extracts their web data, and DeepDive researches them — all from one API at $49/month for 100K credits.
Start with SearchHive's free tier to test your Maps scraping workflow before scaling up.