I have been using various web scraping tools for a few years now to collect data for my analytics reports.

Recently, I came across ScraperAPI and was thoroughly impressed by its capabilities.

Now in this detailed ScraperAPI review, I will share my personal experience using ScraperAPI, explain all its key features, a step-by-step guide to use it, and also provide info on some free resources offered by ScraperAPI.

Why Do You Need a Web Scraping Tool?

If you are in a job where you regularly need to collect data from different websites to include in the analytics reports you create.

Manually extracting data from websites can be tedious and time-consuming. Web scraping tools automate this process by programmatically extracting data from web pages.

If you were looking for a powerful web scraping tool that could help you efficiently collect all kinds of data from e-commerce sites, review sites, classifieds, search engine results pages, and more. The key things you needed were:

  • Asynchronous scraping to speed up data collection
  • Ability to scrape JavaScript-rendered pages
  • Structured data extraction
  • Easy integration with Python data pipelines
  • Affordable pricing

After testing many tools, I finally found ScraperAPI which met all my requirements and can benefit you as well. Now let us have a look at a detailed review of ScraperAPI.

What is ScraperAPI?

ScraperAPI Review

ScraperAPI is a web scraping API that allows you to extract data from almost any website using REST API calls.

It handles all the complexities of web scraping like proxies, browsers, CAPTCHAs, etc. behind the scenes so you can focus on just getting the data you need.

ScraperAPI was founded in 2018 and has quickly become one of the most popular and reliable web scraping services.

The key reasons for its popularity are Simple and fast data extraction using API calls, Powerful async scraping for large-scale data collection, Built-in proxies and CAPTCHA solving for seamless scraping, and Extensive documentation and integration support.

In my experience, ScraperAPI solves the two biggest pain points of web scraping – speed and scale. It has enabled me to extract data from thousands of web pages per hour without any scraping failures.

ScraperAPI’s Key Offerings

ScraperAPI offers various solutions and features for effortless Web Scraping. Here are some of its most useful capabilities:

1. Async Scraper Service

ScraperAPI Async Scraper Service

ScraperAPI’s Async Scraper Service allows large-scale web scraping jobs to be performed concurrently at a 99.99% success rate.

It uses advanced mechanisms to bypass anti-scraping systems and return requested data via a simple API call.

The async scraper submits jobs through the async.scraperapi.com endpoint. It then leverages machine learning and statistical analysis to determine the optimal combination of IPs, headers, CAPTCHA solving, and other configurations to facilitate the scraping of even the most difficult websites.

Users can scrape millions of URLs without managing timeouts or retries themselves – the async scraper handles it all automatically. Results are sent back via a status URL or webhook as soon as scraping is completed.

Key benefits include built-in JavaScript rendering, geotargeting, automatic IP rotation, structured data parsing, and CAPTCHA solving.

The async scraper brings all the power of ScraperAPI’s traditional API with the added ability to scrape unlimited pages concurrently.

The async scraper is ideal for large-scale scraping projects that require data from millions of URLs. Its automated mechanisms and reliability make it perfect for those who need to scrape difficult websites at scale.

2. JavaScript Rendering

Many websites today rely heavily on JavaScript to render content. Normal scrapers cannot extract data from JavaScript-generated content.

Impressively, ScraperAPI’s headless browsers render JavaScript on the fly before scraping. This allows for collecting data seamlessly from even the most complex JavaScript sites.

3. CAPTCHA Solving

CAPTCHAs can block scrapers from accessing data. ScraperAPI has in-built OCR capabilities to solve CAPTCHAs automatically in real-time. This bypasses CAPTCHAs effortlessly without any manual work.

4. Structured Data

ScraperAPI Structured Data

ScraperAPI can extract structured data like product info, review data, search results, etc, and output in JSON format.

This structured data is readily usable for analysis and machine learning applications without needing data cleaning.

For example, I used ScraperAPI to extract 5000 Amazon product listings with full structured info like title, rating, price, etc. in a single API call.

5. Google and Amazon Scraping

ScraperAPI provides specialized solutions for scraping data from Google and Amazon. It enables users to extract structured data from Google Shopping, Google Search, Amazon Product Advertising API, and more.

This has been invaluable for me to get product listings, search results, reviews, pricing data, etc. from Amazon and Google.

For Google, it can collect data from numerous search queries and convert them into structured JSON data, which can be used for various purposes such as market research, SEO tools, and competitor monitoring.

For Amazon, ScraperAPI makes large-scale data scraping easier, bypassing Amazon’s anti-scraping technology. It provides a guide to scrape Amazon data using Python Scrapy and ScraperAPI, and features an Auto Parsing function that allows the collection of JSON data from Amazon using a standard API endpoint, returning all relevant information in a structured format.

6. Data Pipeline

ScraperAPI Data Pipeline

ScraperAPI’s Data Pipeline feature lets you automate large-scale data scraping projects without writing a single line of code.

This low-code solution reduces the need for complex coding and maintenance of your own scraper, thereby reducing engineering resources and costs.

With Data Pipeline, you can schedule automatic data collection on up to 10,000 URLs, making it ideal for managing large data extraction projects.

It provides you with complete control over how and where to get your data, allowing you to send data directly to any folder in your application, document storage space, or email.

In addition to these, the Data Pipeline feature also offers webhook integrations, eliminating the need for manual downloads or copy-pasting.

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ScraperAPI Documentation

ScraperAPI Documentation

ScraperAPI has extensive and well-organized documentation that covers all its features in depth with code examples in Python, Node.js, Ruby, PHP, and more.

Some of the helpful documentation resources are:

  • Getting Started – Installation, authentication, and first API call.
  • Extracting Structured Data – Scraping structured data from e-commerce, classifieds, directories, etc.
  • Google Services – Guides for scraping Google Shopping, Google Maps, Google Flights and more.
  • Data Pipelines – Setting up automated scraping pipelines.
  • Response Format – Details on JSON output structure.

The docs contain everything you need to become proficient at scraping data with ScraperAPI.

ScraperAPI Subscription Plans

ScraperAPI Pricing Plans

Hobby Plan ($49/month)

  • 100,000 API credits
  • 20 concurrent threads
  • US & EU geotargeting

Startup Plan ($149/month)

  • 1,000,000 API credits
  • 50 concurrent threads
  • US & EU geotargeting

Business Plan ($299/month)

  • 3,000,000 API credits
  • 100 concurrent threads
  • All geotargeting options

Enterprise Plan (Custom Pricing)

  • Customized plan for more than 10,000,000 API credits.
  • Premium features, premium support, and a dedicated account manager.

All plans come with features like JavaScript rendering, Premium proxies, JSON auto parsing, Smart proxy rotation, Custom header support, Unlimited bandwidth, Automatic retries, Desktop & mobile user agents, 99.9% uptime guarantee, Custom session support, CAPTCHA & anti-bot detection and Professional support.

ScraperAPI also provides a Free Trial which includes 5,000 API credits for 7 days.

Some Free Resources from ScraperAPI

In addition to the API, ScraperAPI also offers many free resources for learning web scraping:

Web Scraping Learning Hub

ScraperAPI-Web Scraping Learning Hub

The Web Scraping Learning Hub contains free tutorials and guides for mastering web scraping with Python and other languages.


ScraperAPI- Ebooks

ScraperAPI has published helpful free ebooks like “Web Scraping: The Basics Explained” that teach web scraping fundamentals.


They host free webinars on topics like “Scraping JavaScript Websites”, “Building Data Pipelines” etc. which provide actionable tips.

Demo Projects

The demo projects include fully coded web scraping scripts for real-world scraping tasks that you can learn from.


ScraperAPI blog

ScraperAPI’s blog contains tutorials and articles on web scraping best practices. These free resources are invaluable for mastering web scraping at no cost. They complement the API nicely.

Frequently Asked Questions

Does ScraperAPI handle proxies and browsers?

Yes, ScraperAPI manages a distributed network of proxies and browsers automatically in the background. You don’t need to worry about proxies at all.

Can I scrape data behind logins?

Yes, ScraperAPI supports scraping data behind logins by passing cookies in the API request to maintain sessions.

Does ScraperAPI integrate with Python?

Yes, ScraperAPI provides a Python library that makes integration seamless. Libraries for Node.js, Ruby, PHP, etc. are also available.

Can I scrape JavaScript websites?

Definitely, ScraperAPI’s headless browsers render JavaScript before scraping so JS sites are not an issue.

Wrapping Up the ScraperAPI Review

After extensively using ScraperAPI across my various projects, I can confidently vouch for its quality and reliability.

For my web scraping needs, ScraperAPI ticks all the right boxes. I have tried competitors like Apify, Octoparse, ParseHub, etc. but find ScraperAPI the best in terms of features, convenience, and pricing.

I highly recommend ScraperAPI to any individual, team, or company that needs to extract web data at scale. It will surely save you hours of complex coding and setup work so you can focus on deriving insights from the data.

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