Automatic data scraping (parsing) has become an essential practice for developers, analysts, and automation specialists. It is used to extract massive amounts of information from websites—from competitors’ prices and reviews to social media content. To achieve this, numerous “scrapers” have been developed—libraries, frameworks, and cloud services that enable programmatic extraction of web data. Some solutions are designed for rapid parsing of static pages, others for bypassing complex JavaScript navigation, and yet others for retrieving data via APIs.
In this article, I will review the top scraping tools—both open source libraries and commercial SaaS/API services—and compare them according to key metrics: • Speed and scalability; • Ability to bypass anti-bot protections; • Proxy support and CAPTCHA recognition; • Quality of documentation; • Availability of APIs and other important features.
web scraping and what I think is important in this procedure
Speed and Performance - important things for webscraping :
How many pages/data a tool can process per second and how efficiently it uses resources. For example, asynchronous frameworks can operate significantly faster due to parallelism, whereas browser emulation (page rendering) is usually considerably slower.
Scalability - the way to speed up scrape data from website :
The ability to work with a large number of threads or nodes and distribute the load. This is crucial for “industrial” volumes of data—some open source frameworks (for example, Scrapy) are designed from the outset for high load, while SaaS platforms allow scaling up parsing in the cloud.
Bypassing Anti-Bot Protections and Handling JavaScript what else do you need to extract data from a website:
A tool’s capability to extract data from “challenging” websites. This includes rendering dynamic pages (executing JS), simulating user actions (clicks, scrolling), and masking automated traffic as genuine. For example, browser-based scrapers (Selenium, Puppeteer, Playwright) can mimic human behavior, which helps with dynamic content, although they may be detected without special plugins and configurations.
Proxy Support and CAPTCHA Bypass what else do you need to extract data from a website:
The ability to easily configure proxy servers (and rotate them) to distribute requests across different IP addresses, as well as to solve CAPTCHAs. In open source solutions, proxies usually must be integrated manually (via settings or code) and external services used for CAPTCHA recognition. In contrast, commercial API services often automatically substitute proxies and solve CAPTCHAs for you.
Documentation and Community for easy data scraping:
The availability of detailed documentation, examples, and an active community. This is critical for developers—popular open source scrapers (Scrapy, Beautiful Soup, Selenium, etc.) have large communities and abundant guides. Commercial services are also valuable if they offer clear API documentation and support.
APIs and Integration in web scraping service:
The availability of a convenient API for managing scraping or retrieving results. Open source tools usually provide a programming interface (a library) for use in code in various languages. Some frameworks (for example, Scrapy via Scrapyd) allow launching jobs via an API. SaaS solutions almost always offer a REST API for integration—for instance, you send an HTTP request and receive data in JSON.
Implementation Language and License if you want scrape data from website:
The programming languages in which the tool is available (it’s important to choose a solution compatible with your team’s stack—Python, JavaScript/Node.js are most popular for scraping, though options for Java, C#, etc., also exist), as well as the license terms (for open source—whether it can be used in commercial projects; for SaaS—the payment model). All open source parsers on our list use permissive licenses (BSD, MIT, Apache 2.0, etc.), which allow free modification and integration.
It's time to move on to the tools themselves. I have preliminarily divided them into two broad categories: open libraries/frameworks for developers and ready-made commercial solutions (including cloud-based API services and platforms). In each category, we will highlight the most powerful and in-demand solutions on the market, with an emphasis on support for Python and Node.js, though not limited to them.
Why choose Python and Node.js? Simply put, I work with these languages, and they are naturally closer to my workflow.
Open Source Libraries and Frameworks for Web Scraping
In my opinion, open source scraping tools are favored by developers who prefer full control and independence from third-party services. They require programming skills but allow flexible configuration of the data collection logic and the ability to deploy the scraper in your own environment (on a server, in a container, etc.) without extra costs. Here is a list of the top popular libraries and frameworks.
Scrapy (Python).
One of the best-known frameworks for web scraping. Written in Python, it is modular and highly efficient—built on top of the asynchronous Twisted network, which allows it to perform thousands of requests in parallel. Scrapy provides a complete “pipeline” for scraping: from managing the request queue and downloading pages to extracting data with selectors (XPath/CSS) and saving the results in the desired format (JSON, CSV, etc.). Out of the box, it supports multithreading, automatic adherence to delays between requests, and retrying failed requests. Scrapy’s scalability is proven in practice—Zyte (formerly Scrapinghub) processes over 3 billion pages per month using it. With proper configuration, this framework is capable of industrial-scale scraping. However, Scrapy has a learning curve: you must master its architecture (spiders, pipelines, middleware) and be able to write code for scrapers. On the plus side, it has extensive documentation, a large community, and many ready examples. It is BSD-licensed and free for commercial use. Overall, Scrapy is number one among open source scrapers in terms of capabilities and flexibility—an optimal choice for complex projects requiring speed and scalability.
Selenium (Multilingual).
While Scrapy focuses on speed and static sites, Selenium is geared towards emulating a real browser. This open tool for browser automation was originally created for testing web applications but is widely used for scraping. Selenium supports scripts in various languages (Python, Java, C#, JavaScript, etc.) and controls real browsers (Chrome, Firefox, Safari, Edge) via drivers. It allows a scraper to view a page “as a user”: executing JavaScript, clicking buttons, scrolling, filling forms—making it suitable for complex dynamic sites. Its main advantage is its high compatibility with any web technology (Selenium can render even complex SPAs built with React/Vue). However, there are downsides: Selenium is slow and resource-intensive, as it launches a full-fledged browser. For simple pages, it is overkill, and for mass scraping, it is limited by CPU/RAM and is harder to scale (although Selenium Grid allows distributing browsers across multiple nodes). Benchmarks show that Selenium is significantly slower than specialized scrapers that do not render pages. Also, by default, Selenium does not hide its automation—the browser runs in headless mode and can be detected by a site's anti-bot scripts unless special stealth configurations are applied. Developers often enhance it with tools like undetected-chromedriver or by modifying navigator.webdriver to hinder detection. Selenium is a project with a rich history and documentation, making it a reliable choice when a full browser is indispensable. It is distributed under Apache 2.0.
Headless Browsers: Puppeteer and Playwright (Node.js, Python).
In recent years, headless tools related to Chromium have gained great popularity.
• Puppeteer is a library from Google for Node.js that controls Chrome/Chromium via the DevTools protocol.
• Playwright is a similar tool from Microsoft, newer and supporting not only Chromium but also Firefox and WebKit, with clients available for Python and other languages.
Both tools allow a script to launch a headless browser, load a page, wait for JavaScript execution, and obtain the final HTML (or create screenshots, PDFs, etc.). Unlike Selenium, Puppeteer/Playwright work without a separate web driver, interacting directly with the browser engine—often providing better speed and stability. For example, Playwright can launch multiple browser contexts in parallel, using resources more efficiently. Nevertheless, the overhead remains high: Puppeteer requires significant CPU and memory, and Playwright isn’t as lightweight as some alternatives. They are best used selectively, for pages where JavaScript rendering is indispensable.
Regarding bypassing protections, headless browsers have an advantage: they fully execute the site’s front-end code, including AJAX and SPA routing, and carefully handle timeouts and events. However, websites have learned to detect headless Chrome based on specific environmental properties. The community has responded with plugins such as puppeteer-extra-plugin-stealth, which mask most differences of headless mode (for example, by adding missing properties to navigator, introducing noise in Canvas, removing flags). With such add-ons, Puppeteer/Playwright can pass many anti-bot filters. Yet, this arms race between bot developers and anti-bot systems is ongoing. Overall, Puppeteer and Playwright have become the standard for complex scraping: they handle sites that require JavaScript exceptionally well, processing scripts, styles, and fonts like a real browser. Playwright stands out with its support for multiple engines and auto-connection capabilities via Docker and CI/CD. Both tools are available under Apache 2.0.
Beautiful Soup and HTML Parsers (Python).
If the task is to quickly parse HTML or XML obtained from a server, BeautifulSoup4 is often chosen. This popular Python parser simplifies the parsing of HTML markup and searching for elements by tags, attributes, etc. It is very user-friendly (hence its popularity among beginners) and robust in handling imperfect HTML—able to build a correct tree even from “broken” pages.
Note that BS4 does not download pages by itself; it is typically used alongside modules like requests. A nuance in BeautifulSoup’s performance is that it supports different “parsing engines”—the built-in Python html.parser (which is slow), the fast C-based lxml extension, and others. Using BeautifulSoup in combination with lxml can improve performance significantly (by approximately 24% in tests).
Nonetheless, pure lxml or specialized parsers can be even faster. For instance, the selectolax library (Python) using the lexbor HTML engine demonstrated the best page parsing time in benchmarks—around 0.002 seconds compared to approximately 0.05 seconds for BeautifulSoup on the same document. In real-world scenarios, this difference can be critical. Thus, for maximum speed, experienced developers might choose selectolax or direct lxml, but BeautifulSoup remains the most versatile and convenient solution. It supports CSS selector searches (via BeautifulSoup-select, though not as efficiently as lxml/XPath) and automatically converts various encodings. Its only limitation is that it cannot execute JavaScript (for which the aforementioned headless tools are needed). BeautifulSoup is licensed under MIT, and documentation is even available in Russian.
Cheerio (Node.js).
In the Node.js ecosystem, Cheerio plays a similar role. It provides a jQuery-like API (using cheerio.load(html), then $('selector') for searching), which many find convenient. Cheerio operates very fast because it does not render pages in a browser or load external resources (CSS, images, etc.), but simply parses an HTML string. Essentially, it is a wrapper over the HTML parser (htmlparser2) with user-friendly methods. Like BeautifulSoup, Cheerio is used together with HTTP libraries (such as axios, node-fetch) to fetch pages. In terms of anti-bot detection, Cheerio does not mask anything—it does not execute JavaScript or interact with the site beyond retrieving HTML. Therefore, it is employed where one can configure proper HTTP requests (with the right headers, cookies, authentication) to receive preprocessed data. Typically, Cheerio is part of a custom script: first making a request through a proxy with a substituted user-agent, then parsing with Cheerio. It is MIT-licensed.
Apify SDK (Crawlee, Node.js).
It is worth mentioning Crawlee (formerly Apify SDK)—a powerful crawling framework for Node.js. This open source library, developed by Apify, combines the best of both worlds: a high-level crawler with URL queues, automatic retries, and proxy rotation, along with integration with browser-based parsers. Crawlee allows you to write crawlers in Node.js that can switch between fast HTML parsing (using Cheerio) and a full headless mode (using Puppeteer or Playwright) for pages that require JavaScript. The library supports various output formats (JSON, CSV, XML) and offers convenient data store integration. A significant advantage is its built-in proxy support: you can easily connect your own proxy list.
Thanks to its well-designed architecture (worker pools, auto-throttling of requests), Crawlee scales efficiently—Apify’s own developers download millions of pages daily using this SDK. It is licensed under Apache 2.0. For JavaScript developers, Crawlee has essentially become analogous to Scrapy. Moreover, integration with the Apify cloud platform allows you to offload the workload to the cloud if necessary, although the SDK can also be used independently.
Other Languages:
Besides Python and Node.js, scraping tools exist for most programming languages. For example, in Java, the parser Jsoup has long been popular—a lightweight HTML library with a jQuery-like API. Jsoup does not support XPath but handles HTML well and can even work through proxies. For .NET, there is Html Agility Pack and the modern AngleSharp. In Go, libraries like Colly (crawler) and GoQuery (jQuery-like parser) are available. In Scala, there’s SwiftSpider, and in PHP, options include Goutte, PHPHtmlParser, etc. However, in the context of top tools, Python and Node.js solutions are currently the most in demand, which is why they are discussed in detail here.
Below is a summary of the key features of popular open source scrapers:
There is also a small illustration demonstrating differences in HTML parsing speed among various Python libraries (lower time is better): requests-html (based on BS4) turned out to be the slowest, BeautifulSoup4 with lxml took ~0.05 seconds, pure lxml ~0.01 seconds, and selectolax emerged as the fastest at ~0.002 seconds per document. The difference is enormous, so the choice of parser depends on performance requirements.
Parsing time for one page in one round (Python libraries) according to benchmark results. Selectolax (lexbor) is the fastest, requests-html is the slowest.
Website scrapers and their comparison in one place
Now, let’s review a summary table of the capabilities of open source tools:
Tool | Language | Performance and Scalability | Bypassing Blocks (JavaScript/Anti-Bot) | Proxy and CAPTCHA | License |
Scrapy | Python | Very high (asynchronous Twisted engine, thousands of parallel requests); scales to clusters | Processes only static HTML, does not render JS; for complex sites, integrates with headless tools (Splash, Selenium); can modify headers and delays to mask requests | Proxy support via middleware/settings; auto-delays for block bypass; CAPTCHA solving through manual integration with external services | BSD (open source) |
Beautiful Soup | Python | Low (synchronous parsing); speed increases by ~25% with lxml; suitable for moderate data volumes | Only static HTML – does not execute JavaScript; for dynamic sites, requires pre-rendering with other tools | Does not handle network requests – proxies and cookies are set in the HTTP client (requests); not directly applicable to CAPTCHAs | MIT (open source) |
Selenium | Python, JS, etc. | Low speed (full browser; several seconds per page load); resource-intensive, limited to tens of parallel threads per machine | Emulates a browser – executes JS, clicks, input; can pass most anti-bot checks like a human, but headless mode is detected without special settings; requires manual stealth configuration | Proxy support via web driver options; CAPTCHA solving can be added with services like Rucaptcha via scripts (e.g., display CAPTCHA for manual solving) | Apache 2.0 (open source) |
Playwright | Node.js, Python, C# | Moderate (faster than Selenium due to headless operation and optimizations, yet still a browser); allows launching multiple browsers/contexts in parallel | Headless browser (Chromium/WebKit/Firefox) – fully renders the page; slightly less detectable than Selenium (can run non-headless for masking); provides network interceptors for bypassing protections | Proxy support via browser.newContext(proxy); for CAPTCHA – integration with external services or manual input (no built-in solution) | Apache 2.0 (open source) |
Puppeteer | Node.js | Moderate (runs headless Chromium; requires significant CPU and memory); scales well with sufficient resources (can launch many Chromium instances) | Headless Chromium – executes JS, SPA; easily detected without plugins (navigator.webdriver=true, etc.); with stealth plugins, can bypass most detections, though newer systems may still detect it | Proxies configured via Chromium launch arguments or Page.authenticate (for HTTP proxies with authentication); CAPTCHA handling similar to Playwright – external or manual methods | Apache 2.0 (open source) |
Cheerio | Node.js | High (operates at the speed of htmlparser2, without network-induced delays); bottleneck is the network/HTTP client rather than the library itself | Only parses HTML – does not execute JavaScript; unsuitable for SPAs without pre-rendering; bypasses anti-bot measures indirectly (by using proper request headers to mimic a regular browser) | Does not perform requests on its own – proxy, retry, and CAPTCHA handling are implemented in the HTTP library used; Cheerio simply extracts data from the fetched HTML | MIT (open source) |
Apify Crawlee | Node.js | High (asynchronous crawler with auto-throttling; effectively bypasses site speed limits); supports hundreds of thousands of requests; scales horizontally across nodes | Combines strategies: can quickly parse static pages and switch to Puppeteer/Playwright for complex protections; includes a built-in pool of “stealth” settings (e.g., random delays) | Built-in proxy support: can connect via a Proxy URL or use Apify Proxy with rotation; does not directly solve CAPTCHAs, but external services can be integrated into the workflow | Apache 2.0 (open source) |
Note: In addition to those listed, there are other open source tools (for example, the now outdated but noteworthy PySpider—a Python framework with a web interface and job scheduler, or Osmosis—a minimalist Node.js parser). However, their community and support are significantly smaller, so they did not make the top list. For most tasks, modern developers choose the solutions from the table above.
Commercial Solutions: API Services, Platforms, and SaaS for Web Scraping
Commercial tools are designed for situations when you need to “scrape without pain” – avoiding infrastructure management while obtaining a ready-made service. Typically, these are cloud platforms and APIs for scraping, offering powerful capabilities (large proxy pools, automatic bypassing of blocks, visual scraper builders) in exchange for subscription fees or pay-per-volume data pricing. Below, I will review several categories of such solutions:
API Services for Web Scraping and Proxies
These services are accessed via an HTTP API, where you supply a page URL and receive the HTML (or already structured data) in return. Internally, they handle all the “dirty work”: distributing requests across thousands of IP addresses, enforcing delays, solving CAPTCHAs. This approach is convenient for developers—you can integrate such an API call directly into your code without worrying about blocks. Leading API services include:
Scraper API – A specialized service with the slogan “get the HTML of any website via an API call.” Developers claim that with ScraperAPI, getting blocked is nearly impossible since the IP address changes with every request, failed attempts are automatically retried, and CAPTCHAs are solved for you. Indeed, the service substitutes proxies and user-agents, can bypass Cloudflare, and offers JavaScript rendering options. The interface is simple; for example, a GET request like
http://api.scraperapi.com?api_key=APIKEY&url=http://example.com
will return the page’s HTML. SDKs are available for Python, Node.js, and more. The service is in English, but the documentation is very detailed. ScraperAPI offers a free plan (up to 1,000 requests per month) and various pricing tiers starting at $29/month, making it one of the most popular solutions in its class.
Zyte (Scrapinghub) – A comprehensive cloud solution from the creators of Scrapy. It includes several products for scraping:
• Smart Proxy Manager (formerly Crawlera) – a distributed proxy with intelligent management;
• Splash – a proprietary headless browser for rendering pages;
• AutoExtract – an API for structured data extraction based on machine learning; and
• Scrapy Cloud – cloud hosting for your Scrapy crawlers.
Zyte’s approach is interesting because it combines open source and SaaS: you can write a scraper with Scrapy and run it in Scrapy Cloud, using Smart Proxy to bypass blocks and AutoExtract to immediately receive ready entities (products, articles, etc.) without manual rule writing. Zyte offers excellent documentation and SDKs, along with video tutorials and quick-start examples. However, the prices are significantly higher than a DIY approach: proxies start at $99/month for 200k requests, AutoExtract is billed separately, and a full enterprise package can cost thousands of dollars. Zyte is the choice for companies willing to pay for quality and enterprise-level support.
Bright Data (Luminati) – The largest proxy provider, which also offers a ready-made Web Scraping API. Their product, Web Unlocker, is positioned as an “all-in-one” solution for bypassing protections. Simply send a request via their API, and the system automatically sets the required headers, follows redirects, manages cookies, and even solves complex reCAPTCHA if needed. Essentially, Bright Data gives you access to its enormous network of millions of IP addresses (residential and mobile proxies) plus a set of scripts that mimic a real browser. As a result, you receive structured data from the desired website without the headache: “all you need to do is send a request – everything else (IP addresses, headers, cookies, CAPTCHAs) is taken care of by the system.” The downside is the cost: Bright Data is aimed at large businesses, with enterprise-level pricing (hundreds of dollars per month). Alternatives to Bright Data include Oxylabs with its Real-Time Crawler API and Web Unblocker, also targeted at maximum quality (and also expensive).
SerpAPI – A specialized API for obtaining search engine results (Google, Bing, Baidu, etc.). Scraping search results pages is complex due to constant HTML changes and strict rate limits. SerpAPI addresses this by providing ready-made endpoints: you send a request with parameters (e.g., q=USD RUB exchange rate for Google), and the service returns structured JSON with results—headlines, links, snippets, maps, and even widget data (e.g., weather, news). SerpAPI can emulate geolocation, device, and search language for accurate data. As a result, the developer receives search data via a clean API. The service offers a free plan (100 requests/month) and paid plans starting at $50/month. Its documentation and support are quite good, as evidenced by its popularity in SEO applications.
Cloud Platforms and Visual Scrapers (SaaS) - Alternative to free web scrapers
Another major group of commercial solutions are visual scraping tools, often presented as cloud services with a web interface or as desktop applications. Their target audience is not necessarily developers but anyone who needs to scrape something without digging into code; the key is to “set up a scraper without coding” by simply indicating the desired data on the page, after which the service automatically collects a large volume of information. Even experienced automation specialists can save time on routine tasks with these tools.
Octoparse – One of the most popular cloud scrapers featuring a point-and-click interface. The user launches the application (or web version), enters a website URL, and clicks to select the elements to extract. Octoparse builds a visual workflow: first, it navigates to a category page to collect links, then follows those links and extracts fields (such as title, price, etc.). It can simulate scrolling, clicking the “load more” button, logging into a site, and other interactive actions. No programming knowledge is required – everything is done via a GUI. To combat blocks, Octoparse provides automatic IP rotation: when scraping through their cloud, requests come from different IP addresses, protecting against simple bans (“foolproof” protection). It also offers task scheduling (for example, running the scraper every day at 9:00) and cloud storage for results. The free plan allows for up to 10k data points per month, which is sufficient for testing. Paid plans start at $89/month, offering more concurrent threads and data volume. The interface is in English but quite intuitive. Octoparse is popular among internet marketers and content managers attracted by the ability to obtain data “in just a few clicks.”
ParseHub – A similar tool by concept. This is a free desktop application (with a web dashboard) for scraping that also allows you to select data with the mouse. Marketed as “an advanced scraper that lets you extract data as easily as if you were clicking on it,” ParseHub focuses more on structuring results: it can directly export data in JSON, CSV, or Google Sheets via an API. ParseHub can recognize templated pages with pagination, load content that appears upon scrolling (infinite scroll), click on dropdown menus—everything needed for complex sites. The free version is limited to 200 pages per project; paid plans start at around $149/month, offering more parallel tasks and scheduling. ParseHub is an excellent choice when you need to quickly set up one-off scraping without writing code.
WebScraper.io – A well-known Chrome plugin (also available as a cloud service) that allows you to specify extraction elements directly in the browser, forming a kind of site map—a crawl plan. It supports dynamic AJAX sites, proxy servers, and multithreading. Interestingly, WebScraper is available as a free plugin but is monetized through a cloud platform with additional features (data storage, export to Dropbox/Google Sheets, API). In terms of capabilities, it is similar to Octoparse/ParseHub, although its interface is slightly less user-friendly. The paid Cloud Scraper plan starts at $50/month.
Apify – The previously mentioned platform also deserves attention as a SaaS solution. In addition to its open source SDK, Apify provides a ready-made cloud infrastructure: their website features a catalog of ready-made scripts (Actors) for popular websites—from an Amazon product scraper to an Instagram post collector. You can run these Actors and obtain data without writing code, or develop your own based on Crawlee and run it in the cloud. The advantage is its hybrid approach: combining a visual builder with the possibility of custom code. Apify offers a free tier (up to $10 in credits per month), which is sufficient for small projects; beyond that, you pay based on the resources used (RAM per hour and proxy requests). In the Apify interface, you can monitor progress in real time, view logs, and results are stored in a convenient repository. Apify also easily integrates with other services via an Open API and webhooks—allowing you to automate the entire chain (scrape data and immediately send it to Slack or Google Sheets).
Specialized and Unique Solutions
Finally, there are commercial tools that address niche or advanced scraping tasks.
Diffbot – An expensive but powerful AI scraper. Instead of selecting elements via traditional selectors, Diffbot uses computer vision and machine learning to automatically recognize the content of a page (news, product, article, comment, etc.) and extract the necessary fields. For example, if you provide Diffbot with a link to an article, it returns the headline, text, author, date, images—having determined these blocks by their design. There’s no need to write extraction rules—the service is trained on thousands of websites. Diffbot is especially effective for scraping a vast number of different domains (“it allows scaling scraping up to 10,000 domains”), forming a unified Knowledge Graph from all the collected data. It is used by large companies for news monitoring, mention analysis, and more. Pricing starts at $299/month and up (based on the number of pages processed). Nevertheless, it is a unique solution unmatched in intelligent data collection.
A-Parser – A popular desktop software for SEO scraping in the CIS (Windows/Linux). Unlike the other tools mentioned, A-Parser is distributed with a lifetime license (starting from $119) and runs locally. It is more like a combine harvester that integrates 70+ built-in scrapers for various tasks: from search engine results and suggestions from Google/Yandex to sitemap parsing, content collection, bulk link availability checking, etc. Over the years, A-Parser has become a versatile tool for SEO specialists. It offers flexible configuration: in addition to ready-made modules, you can write your own scraping templates using its built-in DSL (supporting RegExp, XPath, JavaScript). It even provides API access, allowing integration with your own scripts and remote task execution. In terms of bypassing blocks, A-Parser is designed for use with your own proxies—it supports hundreds of parallel threads with proxy lists and can randomize request parameters. In the SEO community, it is renowned for its speed and reliability (a program without an elaborate UI, but highly optimized). If your task is to collect search engine-related data, analyze competitors, or check website metrics, A-Parser is an excellent choice.
PhantomBuster – A service well-known in SMM automation circles. It provides a set of ready-made “phantoms” (scripts) for scraping data from social networks and other web platforms where traditional approaches are challenging. For example, there is a Phantom for extracting the contacts of everyone who liked an Instagram post or for collecting a list of event participants on LinkedIn. A distinctive feature of PhantomBuster is that it emulates the actions of a real user in a browser, often requiring you to provide your own cookies or access tokens for the target network. For developers, PhantomBuster is attractive as an outsourcing solution: you don’t need to develop your own bot for each social network—you can use a ready-made one. Prices are relatively low (starting from $30/month) for basic scenarios.
Online web scrapers that you have to pay for under one roof - comparison table
And, as tradition dictates, here is a comparative table of some commercial solutions and their key features:
Service/API | Type | Anti-Block Capabilities | Proxy/CAPTCHA | API/Documentation | Price (from) |
ScraperAPI | HTTP Request API | Auto IP rotation on each request; error retries; CAPTCHAs solved automatically | Large proxy pool included; CAPTCHA does not appear in the response (solved on the service side) | Excellent documentation; clients for popular languages; simple REST GET | Free for 1,000 requests/month; from $29/month |
Zyte (Scrapinghub) | Platform (Proxies + Cloud) | Smart Proxy Manager with anti-block algorithms; Splash for JS rendering; AutoExtract (ML) for content extraction | Own proxy pool of thousands; can bypass Cloudflare; CAPTCHAs via Splash (rendering) or indirectly via CAPTCHA recognition services | Rich REST API; integration with Scrapy; web interface and tutorials | Demo for 10k requests; commercial plans from $99/month (proxies), AutoExtract billed separately |
Bright Data (Luminati) | API + Control Panel | Extremely aggressive bypass: emulation of a real browser, management of headers/cookies; reCAPTCHA solving | Millions of residential IPs worldwide; automatic rotation; CAPTCHAs solved (including complex ones) provided as an extra service | Detailed API; user-friendly web panel with logs; enterprise-level support | Custom pricing; for serious projects, from ~$500/month |
Octoparse | Cloud Service + Desktop UI | Automatic IP rotation for requests (cloud mode); simulates user actions (clicks, scrolling) to bypass simple protections | Built-in proxy pool (transparent to the user); CAPTCHAs: pauses scraper and prompts manual input if necessary (partially solved) | Visual interface plus HTTP API for downloading results; help center documentation; templates for popular sites | Free (up to 10k records/month); paid from $89/month |
ParseHub | Cloud Service + Desktop UI | Executes JavaScript/AJAX on pages to bypass most basic blocks; can work through your VPN/proxy if needed | Proxies not built-in, but support connecting your own (e.g., for geolocation); does not solve CAPTCHAs, requires intervention if they appear | Visual UI; API for exporting data (JSON, CSV) and project management; excellent user guide | Free (200 pages per project); ~$149/month for advanced plans |
Apify | Cloud Platform + Marketplace | Allows running scripts on Puppeteer/Playwright—bypassing Cloudflare, mimicking a browser; offers ready-made Actors with built-in anti-detection methods | Apify Proxy (paid add-on) provides thousands of IPs globally; can connect your own proxies; CAPTCHAs handled via integrated script services | Full-featured HTTP API for launching, monitoring, and retrieving results; excellent documentation; library of Actors | Free tier (up to $20 in credits); then pay-as-you-go (e.g., ~$49/month for 220 CU, ~220k pages) |
Diffbot | API with AI Processing | Bypasses any layout using AI algorithms that “see” the page like a human; independent of HTML structure, hence resilient to site changes | Uses its own crawlers – proxies not required; minimal CAPTCHAs/blocks as the service makes few requests and appears as a normal browser | REST API; SDK for many languages; technical documentation with data structure descriptions (Knowledge Graph); strong support | From $299/month and up (enterprise-oriented, with trial limitations) |
A-Parser | Software (Desktop/CLI) | Masks scraping as a user through delay settings and parameter randomization; for search engines, works via official APIs to reduce ban risk | Supports proxy lists (with different weights, auto-updated); distributes load and switches IPs when blocked; CAPTCHAs: integrated with third-party services (2captcha, RuCaptcha, etc.) for automatic solving | Provides an HTTP API for programmatic control; configuration via files and UI; documentation available in Russian and English; active community forum | €119 one-time for basic (Lifetime); €279 for extended version; demo is functionally limited |
Note: In addition to those mentioned, many other SaaS scrapers exist on the market—for example, ScrapingBee, ScrapingAnt, Mozilla Firefox/Chrome extensions (Data Miner, Instant Data Scraper), specialized price monitoring tools (NetPeak Spider, Screaming Frog for SEO), and social media services (such as PhantomBuster for LinkedIn/Instagram). The choice depends on your specific tasks—each niche can have an optimal tool. I have reviewed what I consider the most versatile and powerful solutions.
Conclusion
In the field of web scraping, there is no single “best” tool—it all depends on the requirements of the specific task. Developers have access to a wealth of open source libraries: when speed and flexibility are essential, Scrapy or Crawlee come to the rescue; for complex JavaScript, Playwright/Puppeteer are ideal; for simple HTML, lightweight parsers like BeautifulSoup or Cheerio work best. These tools require coding but offer full control and are free. On the other hand, cloud services and APIs can save time: they handle blocking issues (proxies, CAPTCHAs), provide out-of-the-box scalability, and sometimes allow you to configure scraping without a single line of code. Their downsides are cost and dependence on a third-party platform, but for one-off projects or rapid prototyping, this is justified.
When choosing a scraper, consider the volume of data, the complexity of target websites, and the resources available for maintenance. For a small script running once a month, there’s little sense in paying hundreds of dollars—it’s simpler to use a library. However, if you need to extract gigabytes of data daily from various sites while bypassing sophisticated protections, it is often more reasonable to invest in a ready-made service rather than spending developers’ time endlessly refining a custom crawler. A hybrid approach might also be optimal: for example, perform the main scraping with an open source crawler while using a commercial proxy API to reduce the likelihood of blocks.
Happy scraping, and may the proxies be with you!
