What is Google Maps Scraping?

Google Maps scraping refers to the automated extraction of publicly available data (e.g., business names, phone numbers, ratings, reviews, coordinates) from Google search results. Google Maps data can be used for lead generation, local SEO audits, and market research.

What data can you scrape from Google Maps?

Common public data points include business addresses, names, phone numbers, websites, opening hours, ratings, customer reviews, and social media profiles.

Is Google Maps scraping legal?

Scraping Google Maps may violate Google’s Terms of Service. Use caution, scrape publicly available information, and consider the official Google Maps API as a compliant alternative.

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5 Best Google Maps Scraper APIs in 2026: Tested & Ranked

Gulbahar Karatas

updated on Jan 30, 2026

See our ethical norms

To find the best Google Maps scraper, we benchmarked the top providers, Apify, Oxylabs, Octoparse, and SerpApi by running 100 searches for each. We tested 10 categories and analyzed 4,000 business listings.

We also verified phone numbers and reviews to ensure the data is actually useful for your lead generation. This benchmark shows which tool is the most reliable for collecting real-world public data.

Google Maps scraping: Benchmark results & comparison

We measured how many data points they can collect and how often they fail. Below is a comparison of success rates and latency for each tool. This guide will help you find the most reliable Google Maps scraper for your project.

Speed comparison: How fast can they scrape?

Data depth: How many details can you get?

Reliability: Success rates of each scraper

Pros and cons of the best Google Maps scrapers

Bright Data provides a Google Maps scraper API that accepts Google Maps “Place” URLs and retrieves data from each specified business page.

The Google Maps data scraper includes a days_limit setting to collect recent data, such as reviews from the past 18 or 9 days. You can select either synchronous or asynchronous scraper mode. In synchronous mode, the browser remains open, and data is delivered immediately after the task completes.

Oxylabs’ Google Maps scraper API is a fast option with a 5-second response time, making it the second-fastest on the list. It has a 91% success rate. However, it provides only 8 data fields, the fewest in the benchmark. This means it only collects basic information. If you need simple data quickly and do not need many details, Oxylabs is a good choice.

It works well as a fast maps extractor for simple tasks. You can use it for quick lead generation if you only need basic public data. For example, it can quickly find business names and addresses for your list. If your goal is to find many potential clients quickly without needing detailed information, Oxylabs is a practical option.

Apify Google Maps scraper API is a very reliable tool for Google Maps scraping. Based on our benchmark, it has a 100% success rate, meaning it works every time without errors. It provides 42 different data fields, which is a high amount of information. While its speed is 16.9 seconds, slower than some other APIs. it is a great choice if you need a lot of detail and want a system that never fails.

You can use this Google Maps scraper when you need very detailed information. It is ideal for advanced lead generation because it provides Google Maps data, including phone numbers, website URLs, and social media profiles. This helps you build a complete profile for your potential clients. Because it accurately collects so many data points, it is a great tool for teams that want deep insights.

SerpApi is the fastest tool in this test. It provides results in only 0.2 seconds, which is almost instant. Like Apify, it has a 100% success rate, making it very stable. It offers 27 data fields, which are enough for most common needs. This tool is the best option for users who need data very quickly and want a reliable service.

For any developer building a live app, this is a very good Google Maps API. You can instantly return search results to your users, showing public data such as place IDs or locations. It is also useful for finding potential clients very fast if you are in a rush. If you need a stable way to get search results from Google Maps without any delay, Serpapi is a top choice for your project.

Octoparse provides the most details, with 44 different data fields. However, it is the slowest tool in the benchmark, taking 108 seconds to finish a task. Also, its success rate is only 47%, meaning more than half of the attempts might fail. This tool is best for users who need every possible detail and do not care about speed or high failure rates.

This tool is a good choice if you want to scrape Google Maps data and retrieve as much information as possible, such as customer reviews, opening hours, and specific addresses. Since you can export data into Excel or CSV files, it makes it easy to organize many data points. It is a helpful tool for users who need additional information for a project and are willing to wait longer for the results.

Google Maps scraper benchmark methodology

We benchmarked 4 data providers (Apify, Oxylabs, Octoparse, SerpApi) to evaluate their ability to scrape Google Maps data. We executed 100 queries per provider across 10 distinct business categories in New York, US.

Test parameters

Location: We used “New York, US” as the constant location for all queries.
Categories (10): We selected high-volume categories, including coffee shop, restaurant, gym, pharmacy, hotel, hospital, bank, supermarket, gas station, and hair salon.
Runs: We performed 10 repetitions per category (total 100 runs per provider).
Target: We requested 10 results per query. In total, each provider was tested on 1,000 individual business listings (10 categories × 10 runs × 10 results).

Provider implementations

Apify: We used the compass/crawler-google-places Actor in asynchronous mode. We submitted search queries with location parameters, polled for the run status, and retrieved the results from the default dataset upon successful completion.
Oxylabs: We used the Realtime Scraper API in synchronous mode. We sent POST requests with source: google_maps and geo_location parameters, waiting for direct JSON responses containing the scraped data.
Octoparse: We used the “Google Maps Leads Scraper” template via the Cloud Extraction API. We dynamically updated task parameters based on the search keyword, started the extraction task, polled for status, and stopped the task early once we reached our target item count to measure speed efficiently.
SerpApi: We used the google_maps engine in synchronous mode. We made a single GET request to the search endpoint with a constructed query (“{category} in {location}”) and processed the local_results JSON array to extract place data.

Measurement metrics

Success rates

We defined three levels of success:

Submission success: We considered a submission successful if the API accepted our initial request (HTTP 200/202).
Execution success: We considered an execution successful if the job completed successfully.
Validation success: We applied a strict set of rules to ensure data usability. We considered a result VALID only if it met the criteria below. We calculated the validation score based on the ratio of valid fields to total checked fields (minimum 60% required), with specific strict overrides.

Required fields

We required these fields to exist and pass validation. If either was missing or invalid, we marked the entire result as INVALID.

Name: Must be a non-empty string.
URL: Must be a valid Google Maps or website URL (containing “http” or “maps.google”).

Conditional fields (must be valid if present)

We did not strictly require these fields to exist, but IF data was returned for them, we required it to be valid.

Address: If present, must be a non-empty string and not “N/A”.
Phone number: If present, must contain at least 5 digits.
Reviews count: If present, must be a non-negative number.

We tracked these three success rates throughout the pipeline to identify failure points at each stage. For the final analysis, we report the validation success rate, which measures end-to-end performance from the API call to semantically relevant, citation-verified content.

Our validation success metric captures end-to-end pipeline performance. Each trial progresses through three sequential stages: submission, execution, and validation. A trial that fails at any earlier stage cannot proceed to later stages and is recorded as a failed trial (score of 0) in the final validation calculation.

For example, if we send 100 requests:

96 pass submission (4 failures recorded as 0)
Of those 96, 91 pass execution (5 more failures recorded as 0)
Of those 91, we validate the returned data and calculate individual validation scores

The final validation success rate includes all trials: the 9 failures (scored as 0) plus the 91 validated results. We report the median validation score for each request across all trials.

Time metrics:

Total time: We calculated the median (P50) duration from the initial request to the final data retrieval for each category for 10 results. We treated high-latency runs (>1800s) as failures.

Available metadata:

We counted the number of structured data fields returned alongside the raw text, including citations, links, response text, location, model version, and others.