Choosing Go vs Python for Web Scraping: Comparison Inside

For a long time, Python has been the undisputed leader in building parsers, and overall, it performs exceptionally well when working with datasets. Everything would be perfect if not for its slowness. This is where the "newcomer" developed by Google steps in — the Go programming language (or simply Golang).

As you might guess, this article is a detailed comparison of Golang vs Python in web scraping tasks. Below, we’ll explain which programming language performs better in different situations.

Python for Web Scraping: The Current Standard

The Python programming language was first released in 1991 and has been continuously evolving ever since. It’s a high-level dynamic language, meaning it executes code in real time without prior compilation. You can type commands in the console and instantly see the results.

In the TIOBE index, Python holds the #1 position (note: the ranking is largely based on media mentions and search query statistics). Just when it seemed like it couldn’t go any higher, Python surprised everyone in July 2025 by becoming even more popular. The main reason? A growing ecosystem of libraries for integrating with neural networks and AI models.

Why Python Is Popular for Scraping

• Readable, non-compiled code. You can write scripts “here and now” without waiting for compilation. The syntax is concise and requires minimal boilerplate.
• A huge selection of libraries. For web scraping, Python offers parsers, HTTP clients, database connectors, AI model integrations, headless browser drivers, data formatting utilities, and more. Many tools come pre-installed, and the rest can be pulled from the official repository. The package manager pip even supports local or corporate repositories. Adding proxy servers and anti-blocking tools (like CAPTCHA solvers) to your scripts is straightforward.
• Asynchronous support out of the box. Mostly via libraries, but Python handles async calls with ease.
• Plenty of ready-made scripts. You can grab a sample script and adapt it, or simply copy-paste and run.
• Scraping frameworks like Scrapy. This is essentially a ready-to-go multithreaded scraper with an extension system that can be adapted to almost any use case. For instance, you can connect headless browsers and rotating proxies. 
• Cross-platform compatibility. Python runs on PCs and smartphones, Linux, macOS, and Windows, on servers or in isolated containers. Even obscure operating systems are supported. Ever heard of Jython? It allows Python to run inside Java machines, making it possible to execute scripts even on old phones without a proper OS.

Popular Python Scraping Tools

For clarity, let’s break Python scraping libraries into categories:

HTTP Clients — used to send HTTP requests:

• Requests (the most popular and versatile),
• HTTPX (a more modern, asynchronous alternative to Requests),
• urllib / urllib3 (built-in default solution),
• aiohttp (great for handling large numbers of parallel requests),
• PyCurl (Python bindings for libcurl),
• Treq (built on top of Twisted, which itself is a framework for building high-performance web servers).

HTML Parsers — used to extract data from HTML code:

• BeautifulSoup (the most user-friendly and functional HTML/XML parser, works with other parsers’ syntax),
• lxml (fast and efficient, supports XPath syntax),
• html5lib (slower but highly tolerant of malformed code).

Web Drivers — used to control headless browsers:

• Selenium (formerly the most popular and feature-rich option),
• Playwright (an official Microsoft tool with powerful features and auto-installed browsers),
• Pyppeteer (Python port of Puppeteer),
• nodriver (can directly communicate with an installed Chrome browser and doesn’t require fingerprint-hiding tools for headless mode).

Data Formatting Tools — many formats (JSON, XML, YAML, CSV) are supported at the system-library level, but there are also specialized libraries for quick format conversions:

• PrettyTable (for visually appealing tables),
• Pandas (a comprehensive data management framework for grouping, cleaning, formatting, converting, etc.).

Full-Fledged Web Scraping Frameworks — these stand apart as complete solutions:

• Scrapy (a full-featured scraping framework with built-in crawler support, pipelines, middleware, and proxies; you just configure your rules and data schemes, which takes minutes),
• Feapder (a framework developed by a team from China),
• PySpider (a ready-to-use scraper with a web interface),
• Scrapling (a fast scraper with integrated Playwright and proxy support),
• Selectolax (a parser written in Cython),
• AutoScraper (a lightweight Python web crawler), and others.

Key drawback of ready-made scrapers and frameworks: many lose support quickly, as developers tend to abandon them without sponsorship or long-term funding.

When Python Isn’t the Best Fit

You may want to avoid Python if:

• Performance is critical — for example, if your project requires handling millions of requests per second. Python will be too slow and resource-hungry in such cases.
• The parser must run on mobile or low-power devices — Python can be heavy and inefficient for constrained environments.
• You plan to parse web applications with heavy, complex JavaScript code — in this case, it’s better to rely on JavaScript itself and its dedicated web drivers.

Web Scraping with Go: Speed and Concurrency

The Go programming language (Golang) was created at Google in 2007 and became publicly available in 2009. Go is a compiled language focused on high performance and the convenient development of multithreaded applications. Unlike Python, you can’t run Go code directly in a console, but once compiled, Go produces standalone executables with no external dependencies, and they run instantly without delays.

Go can’t yet be called a mainstream language, but its position has been steadily strengthening year after year. The main reason is its natural fit for domains where it truly shines: cloud services, microservice architecture, and high-load systems. Web scraping with Go is increasingly in demand in scenarios where speed and massive parallel data processing are critical.

Benefits of Go for Web Scraping

• High performance. Go code runs significantly faster than Python, which is especially important when scraping millions of pages.
• Built-in concurrency. With goroutines and channels, you can launch thousands of concurrent HTTP requests without losing performance.
• Minimal dependencies. The standard library already includes everything you need: HTTP clients, JSON/XML handling, and networking tools.
• Cross-platform compatibility. Compiled binaries run on Linux, Windows, macOS, and even on ARM-based servers.
• Network efficiency. Go was designed from the ground up for high-load servers, so sending requests and managing connections is extremely efficient.
• Reliability and simplicity. The language is compact, and strict typing reduces the risk of errors.

Libraries and Tools for Scraping with Golang

Here are the Go scraping libraries you need to know:

HTTP Clients — for sending requests in Go:

• net/http (the standard package, a basic solution for handling GET/POST requests, cookies, headers, sessions, etc.),
• resty (a user-friendly library with extended HTTP functionality, similar to Python’s Requests).

HTML/XML Parsers — responsible for extracting data (the core of scraping in Go):

• goquery (similar to BeautifulSoup; allows querying elements with CSS selectors and working with the DOM structure),
• htmlquery (useful for parsing HTML with XPath syntax),
• gjson (fast JSON parsing and querying),
• xmlpath (XPath-like querying, but for XML).
  
  

Web Drivers and Headless Browsers — for interacting with dynamic web pages:

• chromedp (direct control of Chrome via the DevTools protocol),
• rod (another library for headless Chrome, modern and flexible),
• playwright-go (Go bindings for Playwright, supporting Chromium, Firefox, and WebKit).
  
  

Full-Fledged Frameworks — complete solutions for scraping in Go:

• Colly (the most popular Go framework for web scraping, supports crawling, middleware, proxies, and rate limiting),
• Gocrawl (a very simple framework for site crawling),
• Ferret (a hybrid tool that combines scraping and browser automation, with its own DSL scripting language).

Limitations of Go in Scraping

Using Go for scraping may be inefficient if:

• You’re building a small project or prototype. Python lets you hack together a script much faster.
• You need a mature framework like Scrapy with a large ecosystem of extensions. Go still lacks equivalents.
• Your parser requires deep integration with ML/AI models. Go has almost no libraries for this, while Python’s ecosystem is extremely advanced.
• You value maximum flexibility and lightweight code. Python is much friendlier in this regard, especially for beginners.
  
  

Performance and Code Maintainability Comparison: Python vs Go

Let’s summarize the differences in a table to highlight the strengths and weaknesses of Python vs Golang web scraping:

Using Proxies in Scraping: Python vs Go

It’s no secret that when scraping pages at scale, proxy servers become a cornerstone. They not only help bypass blocks but also distribute load and improve anonymity.

So how do things look in Go vs Python?

Python and Proxies in Web Scraping

• Proxy support is either built into HTTP clients or configured at the environment/browser level.
• SOCKS5 connections are slightly more complex, requiring an additional library.
• Proxies can be with or without authentication. Username and password are typically passed in a simple format, e.g., http://user:pass@host:port.
• The official module catalog offers several libraries for manual proxy rotation.

Services like Froxy (with automatic rotation and API/dashboard management) are extremely easy to integrate. The main drawback: under heavy parallel connections, the requests library may start to lag. Alternatives like httpx or aiohttp are better suited. Ultimately, limitations often come from the Python interpreter and environment itself — memory allocation and calculation speed can hit a ceiling.

Go and Proxies in Web Scraping

• Go doesn’t have proxy support “out of the box,” but the standard net/http package allows you to define Transport.Proxy and configure rotation at the lowest level.
• SOCKS5 connections are supported via a dedicated package.
• Authentication is handled manually or through a custom DialContext.
• Go handles tens of thousands of parallel connections through goroutines effortlessly. A proxy pool can be stored in memory and switched instantly, with minimal CPU and RAM load.

The key drawback: you’ll need to do a lot of manual setup to build your own proxy handling layer. There are very few ready-made solutions — almost nothing to copy-paste for a quick start.

Which One to Choose for Your Use Case?

In short: Python web scraping means convenience, fast development, and a rich ecosystem. Go web scraping means execution speed, scalability, and maximum reliability in production.

But let’s break it down more practically, tied to real-world scenarios — continuing the Go vs Python comparison.

Choose Python for Scraping if:

• You need to build a working scraper or prototype quickly — to test a hypothesis or monitor competitors across a limited number of pages.
• You require integration with neural networks and AI — for building pipelines, recognizing screenshots, detecting unusual patterns, etc.
• Your scraping process relies on headless browsers. Python has a much wider selection of web-driver libraries, making browser automation easier. Even anti-detect solutions use the same connectors.
• Your work revolves around REST/GraphQL APIs. Python makes handling structured data easier, mainly thanks to its vast set of specialized libraries.

Choose Go for Scraping if:

• You need to process massive amounts of pages — industrial-scale data collection.
• You require thousands of simultaneous proxy connections. Goroutines can run each request under its own IP.
• Resource consumption is critical (e.g., on server hardware or clusters).
• Your entire project is already running on Go, and the scraper must integrate seamlessly. In this case, dropping Go simply isn’t an option.

Conclusion

We’ve compared the two most popular programming languages used for building scrapers — Go vs Python.

• Golang is the stronger choice for large-scale, high-load projects, often in demanding corporate environments.
• Python remains the go-to option for everything else. It’s virtually irreplaceable when it comes to AI and LLM integration.

Regardless of which programming language you use, a scraper won’t work effectively without quality proxies. That’s where we come in. Try Froxy in action and see for yourself the wide network coverage and convenient proxy rotation, and location management.