Explore Difference Between Python vs Go: Which to Choose and When in 2025

When it comes to choosing any programming language over Python, we all stumble a little. There is no doubt about Python’s popularity, growing community, and capability to build strong, complex applications. But the most commonly asked question is, “Will Go become as popular as Python in certain domains?”—leading us to discuss Python vs. Go. 

Each language has its goals and is built for specific scenarios. Thus, comparing two commonly used programming languages- Python vs. Go (Golang) will give developers a closure about who will win. 

Python and Go might have similarities but they serve different purposes. 

• Python is the primary language for data scientists while Go is suitable for server-side commands and running software. 
• Go is a procedural, functional language built for speed, and Python is an object-oriented, imperative, functional, and procedural language. 
• Go supports concurrency, the ability of an algorithm to run its steps out of order, and Python doesn’t.

In short, if you are working with data with a real audience, then Python is your choice, and if you are working with servers, choose Go.

In this blog, we will highlight detailed comparisons while discussing their similarities, use cases, and a final verdict to help you decide- Go vs. Python.

Let’s start with a simple introduction.

Golang Overview

Golang is a Google in-house product developed by Robert Griesemer, Rob Pike, and Ken Thompson in 2007. They designed it because they were frustrated by the performance of other backend development programming languages, especially C++. 

They wanted a language optimized for performance and usability in the era of networked systems, multicore, and complex codebases. There comes the Golang, which has clean syntax and is beginner-friendly. Here’s what you must know about Go. 

• an open-source, statically typed, compiled programming language
• aimed to make programming simpler and faster
• Ideal for server-side commands and running software
• Created to simplify complexity in the era of cloud computing, networking, and infrastructure applications
• supports cross-platform development, allowing you to build applications for multiple operating systems
• Designed to perform better than high-performance, server-side programming languages such as Java or C++.

Golang Key Features

• Simplicity– it has clean and minimalistic syntax, making it easy to understand and learn. It avoids complex concepts like inheritance, generics, and others that make code easy to read.
• Static typing- Go is a statically typed language. It means variables are declared with a specific type at compile-time. This helps catch errors early and improves the reliability of code.
• Compiled language- Go is compiled directly into machine code, making it fast and efficient in terms of performance. It generates a single, statically linked binary that simplifies the deployment without needing any dependencies on the target machine.
• Concurrency- Go is built for concurrency. It uses Goroutines (lightweight, concurrent threads) and Channels (for goroutines to communicate and synchronize with each other) to improve the app’s performance.
• Garbage collector- Go manages memory without manual intervention and impacting application performance.
• Fast compilation- Go is known for its fast compilation time that speeds up the development cycle and code iteration.
• Standard library- Go comes with a powerful standard library and packages that can simplify any complex tasks from networking to managing complex codebase.
• Built-in testing- With Go, developers can write unit tests with built-in testing tools for benchmarking and performance testing.

Golang Advantages

• Being a compiled language, Go directly compiles to machine code that improves execution time. Thus it is ideal for performance-critical applications, such as web servers, networking tools, and microservices.
• Go has built-in support for concurrency. It allows developers to write highly concurrent programs easily, without complex threading models.
• It has a fast build time particularly useful in large codebases and continuous integration/continuous deployment (CI/CD) environments, where quick feedback is crucial.
• Go supports cross-platform development. The code runs smoothly across multiple platforms (e.g., Linux, macOS, Windows) with a single codebase. 

Disadvantages of Golang

• Golang is still developing. It means there is a lot scope of for improvement. 
• Go uses an explicit error-handling approach, meaning you have to manually check errors after every function call. It leads to writing repetitive code to handle errors and complex error-handling processes.
• Go is not suitable for developing graphical user interface (GUI) applications as it doesn’t have robust built-in support, unlike other languages Like Python.
• Go is not designed around object-oriented programming. While you can use structs and interfaces to mimic OOP features, Go doesn’t have traditional inheritance or classes. This can be a downside for developers who prefer the OOP model or are transitioning from languages that rely heavily on it (like Java or C++).
• While Go is great for backend development and cloud-based services, it is less commonly used for desktop or mobile applications. For these kinds of apps, other languages like Swift, Kotlin, or C# are more widely used, with better ecosystem support.

Brands Using Golang

Today, Go can be used for a wide range of applications, including:

• Cloud-based applications
• Server-side applications
• DevOps/ Site Reliability Automation
• Command-line tools
• Micro-controller programming
• Robotics
• Games

Python Overview

Today 41.9% of developers prefer Python. 

It is a high-level language that is built for varied use cases. Its capability to build AI/ML models and a wide range of frameworks makes it a suitable choice for modern-age app development.

Python Key Features

• Simple and Readable- Python has simple English-like syntax, making it easy to learn and adapt. The language emphasizes clean and readable code, using indentation rather than curly braces or semicolons to define code blocks.
• Dynamically typed- means, you don’t need to declare variable types explicitly. The interpreter determines the type of a variable at runtime. This flexibility improves the development cycles.
• Interpreted language- Python’s interpreter executes the code line-by-line which makes development faster, but it is slower than compiled languages like C or Go.
• Large standard library- Python offers a wide range of standard libraries that include modules for everything from file handling to machine learning. Its “batteries-included” approach reduces the need for external libraries to build complex applications.
• Extensive Third-Party Ecosystem- Python offers a massive third-party ecosystem that helps in developing highly functional and complex applications.
• Garbage collection- Python’s garbage collector automatically handles memory allocation and deallocation, reducing manual memory management.

Brands Using Python

Python is widely used in data science and other fields. Some of these include:

• Data analysis
• Data visualization
• Machine learning
• Software development
• Web development
• Task automation/ Scripting
• Database Programming
• Quick prototyping

Now, let’s understand where Python and Go differ.

Python vs. Go: Detailed Comparision

1. Go vs Python Performance

When we compare these two technologies based on performance, Go wins. It is said that Go is about 40x faster than Python. That’s a huge difference. Why’s that?

Execution speed

Go is a compiled language that directly translates the code into machine code before execution, resulting in faster execution speeds. Developers use Go to develop performance-critical applications.

Python is an interpreted language. It means the code is executed line-by-line by an interpreter, which makes it slower. The interpreter’s dynamic typing also reduces optimizations that Go enjoys. 

Go is significantly faster in terms of raw execution speed, especially for CPU-bound tasks like image processing or high-frequency trading, Go’s compiled nature and concurrency offer significant advantages.

Python’s performance limitations can be mitigated using tools like Cython or multiprocessing.

Concurrency 

Go was designed with concurrency in mind. It uses goroutines (lightweight threads) and a built-in goroutine scheduler to handle multiple tasks efficiently.

This allows Go programs to scale effectively on multi-core processors with minimal overhead. Thus, it is used for building high-performance networked or distributed systems.

Python has multithreading, but due to its Global Interpreter Lock (GIL,), it prevents multiple threads from executing Python bytecode simultaneously in a single process.

It means Python struggles in executing CPU-bound tasks. Go excels in handling concurrency and parallelism with minimal performance penalties, while Python can be slower due to the GIL.

Memory Management

Go has a garbage collector that automatically manages memory. It is designed for low-latency, high-performance applications, and it minimizes pauses during garbage collection, making it more predictable in production environments. It is more optimized than Python’s garbage collector.

Python also has garbage collection, but it tends to use more memory overall because of its dynamic typing (where you don’t need to declare variable types), which requires more resources at runtime.

Go is more memory-efficient and can handle large applications better. Python uses more memory, which may slow things down for memory-heavy tasks.

Startup time and latency 

Go has a fast startup time as it is a compiled language. It is converted into machine code that is already optimized and ready to execute quickly, making it suitable for microservices or high-performance web servers.

Python, as an interpreted language, can have slower startup times, especially for large applications that need to load numerous dependencies or modules. However, for small scripts or quick prototypes, this might not be an issue.

2. Python vs. Go for Scraping

For web scraping, both Python and Go can work well, but they have different advantages. Python is the most popular choice due to various tools like BeautifulSoup and Scrapy. 

While Go does not have tools for scraping. Still, it is faster as it can handle scraping many pages at once more efficiently, due to its concurrency. 

In 2021, a test was conducted to analyze Python and Go performance for web scraping. The task was to scrape stock prices from Yahoo.

Python uses the BeautifulSoup library, which helps organize the data. Go used Goquery and Goroutines to scrape faster by doing multiple things at once.

The test scraped two sets of URLs: one with 2000 URLs and 7 threads, and another with 500 URLs and 5 threads. The result? Go was more than twice as fast as Python.

This shows that if you need fast performance for web scraping, Go is the better choice because it’s built for speed.

3. Scalability- Python vs Go

When it comes to scalability, Go performs better than Python. 

Go has high concurrency, so it can handle several tasks simultaneously, making it suitable to handle large-scale systems efficiently. It optimizes memory and allocates resources efficiently without impacting the app’s performance. 

Python is scalable, but not as Go due to Global Interpreter Lock (GIL). It limits Python’s ability to run tasks in parallel.

In the case of large applications, Python will not be able to handle large requests concurrently, reducing performance. So, developers mostly prefer to Go over Python to build highly scalable applications.

4. Golang vs Python Readability 

Python is generally easier to understand and use as it has simple and clean syntax. It uses indentation to structure the code, making it beginner-friendly.

Go is also readable, but its syntax is a bit more strict. It uses curly braces {} to manage the code blocks. 

5. Python vs Go for Web Development

Python and Go are good for web development but offer different purposes. Python is great for building websites quickly due to its easy-to-use frameworks like Django and Flask.

These frameworks not only improve the app’s functionality but also improve the developer’s experience by providing intuitive interfaces. 

Go does not offer wide libraries, but it’s great for making fast, high-performance web services. is because of its features like concurrency, scalability, and more.

So, if you want to build a quick and highly functional website, Python is a better choice. But if you need something that can handle lots of traffic and runs fast, Go is the way to go.

While Go lacks an abundance of frameworks, libraries like Gin or Echo offer sufficient functionality for most web applications.

6. Python vs Golang with Modern-Age Technologies

When it comes to efficiently working with the latest technologies, Python has a heads-up due to its wide range of frameworks and libraries. 

Python is great for projects with large amounts of data where many people are making changes. 

7. Go vs Python for Backend Development

For backend development, Python is easy to use and great for building web apps quickly with frameworks like Django and Flask. You can use it for small to medium projects. In some cases with larger projects also but with the right choice of Python libraries. 

Go, on the other hand, is much faster and better for building scalable systems that need to handle lots of users or data at once.

It’s also easier to deploy because it compiles into one file. However, Go doesn’t have as many web frameworks, so it can require more work for certain features.

In short, Python is great for fast development, while Go is better for high-performance, large-scale systems.

8. Python vs. Go for Error Handling

In Python, error handling is done using exceptions. When something goes wrong, Python automatically raises an error, and you can catch and handle it using a try/except block.

This makes error handling easy and readable, and it’s clear where the error occurred.

For example-

try:

    result = 10 / 0

except ZeroDivisionError:

    print("Cannot divide by zero")

In Go, error handling is more manual. Instead of exceptions, Go functions return an error value, and you need to check it each time.

This makes error handling more explicit and forces you to handle issues right away, but it can make the code longer and more repetitive.

For example-

result, err := divide(10, 0)

if err != nil {

    fmt.Println("Error:", err)

}

9. Python vs. Go for GenAI Systems

The main barrier to using Go with AI-powered services is the lack of a robust ecosystem and libraries compared to Python.

Many developers prefer Python because it has mature tools like PyTorch for AI model management, and most documentation and frameworks are Python-centric.

While some Go developers are interested in using Go for GenAItasks, they note that Go is missing key libraries and tools, making it harder to implement and deploy AI models. 

Also, read- Scala vs Python: Explore Top 17 Differences

Which one to Choose: Python vs Go

Choosing the right language depends on your project’s needs. Here are a few things to consider.

1. Performance Needs: If your project requires high performance, especially handling many tasks at once, Go is a better choice. But if the speed of development and ease of use are more important, Python might be the way to go.
2. Libraries and Tools: Think about the libraries and frameworks you need. Python has a large collection of tools for many types of projects, while Go’s ecosystem is smaller but growing quickly.
3. Team Skills: Consider what your team is familiar with. Choosing a language your team is comfortable with can help speed up development and avoid problems.

Whether Go is more powerful than Python in some cases. But building modern-age advanced applications is possible with Python.

However, there is no doubt about using Go instead of Python, but the usage is still limited due to the presence of Go’s libraries. 

Final Verdict

It all depends on the type of project you’re working on.

If you need built-in support for concurrent programming, like in cloud computing, microservices, backend development, or server-side applications, Go is a great choice.

On the other hand, if your project involves deep learning, data analytics, web development, or artificial intelligence – in short if you’re a data scientist, Python is usually the preferred option, thanks to its powerful machine-learning libraries that make the process easier.

In conclusion, both languages have their strengths. Always consider the nature of your project when choosing the right programming language.

However, if you’re still unsure about which language to pick for your project, let the OnGraph team assist you.

We focus on your business success and deliver the best results, so you don’t have to worry about purchasing hardware, building an in-house team, or handling the hiring process. OnGraph Python developers have you covered.