Go (also known as Golang) and Python represent two distinct programming philosophies. Each coding language has its own strengths and weaknesses, and choosing between them largely depends on the specific context. This article explores the key differences between the two, highlighting where their unique characteristics offer advantages or disadvantages across various types of projects.

What do the Go and Python languages involve?

Go (Golang) proudly defines itself as an idiomatic language. But what exactly does that mean?
Idiomatic: using, containing, or denoting natural expressions to a native speaker.
The primary goal of the Go programming language is to produce clear, direct, and readable code that avoids surprises. It discourages annotations that perform operations behind the scenes, and instead of large, pre-written abstractions, it favours simple, purpose-specific code.
Python, on the other hand, is a true Swiss Army knife: its main focus is on rapid development and prototyping. Whether it’s adding two numbers in the console, automating a task, or building a portal with an admin interface and a REST API, few solutions are faster than Python language – and it comes with a vast array of battle-tested libraries.
To put it simply: in Go, you tend to write more code by hand; in Python, you spend more time reading through library documentation.
Let’s look at a basic example of fetching data from a REST API.
In Python, according to common practice, this is all it takes when using the requests library:
import requests

def fetch_data(url):
    response = requests.get(url)
    if response.status_code == 200:
        return response.json()
    else:
        return None

if __name__ == "__main__":
    url = "https://jsonplaceholder.typicode.com/posts"
    data = fetch_data(url)
    print(data)
In contrast, the Go programming language includes the http package as part of its standard library, and for most Go developers, this is more than sufficient for typical use cases. Here’s how the same code looks in Go, without any external dependencies:
package main

import (
    "encoding/json"
    "fmt"
    "net/http"
)



type Post struct {
    UserID int    `json:"userId"`
    ID     int    `json:"id"`
    Title  string `json:"title"`
    Body   string `json:"body"`
}

func fetchData(url string) ([]Post, error) {
    response, err := http.Get(url)
    if err != nil {
        return nil, err
    }
    defer response.Body.Close()

    decoder := json.NewDecoder(response.Body)

    var posts []Post
    if err := decoder.Decode(&posts); err != nil {
        return nil, err
    }

    return posts, nil
}

func main() {
    url := "https://jsonplaceholder.typicode.com/posts"
    posts, err := fetchData(url)
    if err != nil {
        fmt.Println("Error fetching data:", err)
        return
    }


    for _, post := range posts {
        fmt.Println(post.Title)
    }
}
At first glance, it may appear unnecessarily long, but a closer inspection reveals two key characteristics that make the code more verbose:
  1. Explicit error handling: wherever there’s a possibility of failure, you’ll notice an if statement that - in this case - terminates the function and returns an error variable. This makes the origin of errors easier to pinpoint and forces the developer to handle them deliberately, rather than throwing an exception and relying on some other code to catch and handle it, as is common in other languages.
  2. Strong typing: unlike the Python example, this Go code will only work if the JSON returned matches the expected Post type. This allows certain errors to be caught earlier, particularly when changes occur in the API.

Golang vs Python development: 6 key criteria for comparison

1. Compilation, interpretation, and performance considerations

Python is an interpreted language, meaning that any machine intended to run Python code must have a Python interpreter installed, and the interpreter processes the source code directly. In the age of Docker and virtualisation, this is generally not an issue. However, it is worth noting that - similarly to PHP - if you're running an application on an on-premise server, the source code must also reside on that server.
Another drawback lies in the runtime environment’s size. A typical mid-sized Python application packaged in a Docker container - along with all its necessary dependencies - can easily exceed 1 GB. In contrast, a compiled Go application of similar scope usually takes up just a few megabytes, and even when containerised, the final image rarely surpasses 30–40 MB.
Performance is another important aspect. A compiled application will, by definition, always execute faster than an interpreted one. Of course, it is entirely possible to write poorly optimised code in Go as well, but for performance-critical applications, this difference should be taken into account. The Benchmarks Game project has examined this topic in great depth.
As an example, consider the fannkuch-redux algorithm. The “original code” refers to a basic, single-threaded transliteration of the algorithm into Go and Python without optimisation. Meanwhile, the “optimised code” version aims to exploit each programming language’s advantages and make use of parallel execution - while preserving the original logic of the algorithm.
  Original Code Optimised Code
Go 46.13 s 8.34 s
Python 2 259.59 s
(~ 37 minutes)
302.37 s
(~ 5 minutes)
When it comes to catching errors, it makes a considerable difference whether an issue is discovered during compilation or only surfaces at runtime. Anyone might push a last-minute change without testing it first - though of course, they shouldn’t. In such cases, if something breaks, a Go CI/CD pipeline will typically catch it before deployment, thanks to the compile step. Python, on the other hand, offers less protection by default and only helps avoid such issues if the pipeline is carefully designed and robust.
That said, the interpreted nature of the Python language brings its own advantages. The interactive console, where you can test snippets of code on the fly during development, can significantly speed up the process - especially when fine-tuning small parts of the code, like adjusting a regular expression.

2. Required developer skills

What does a developer need to effectively contribute to a project?
  • Knowledge of the programming language
  • Familiarity with the libraries and frameworks in use
  • Domain knowledge - understanding the project’s goals, terminology, methodology, models, and business logic
Applications written according to the philosophy of the Go programming language have a notable advantage in long-term support: they significantly reduce the need for extensive library and domain-specific knowledge compared to Python programming projects. In a typical Python development environment, many libraries and framework features are used to abstract away much of the underlying logic - allowing the developer to focus mainly on the business logic. In Go, there is far less abstraction, so a new developer can easily get a clear understanding of how the application works by stepping through the code line by line for each use case.
Go code relies far less on external dependencies. It contains less boilerplate and consists more of custom-written logic tailored to the project’s specific goals. This generally results in less technical debt over time and code that is easier to maintain.
Naturally, Go also has a wide range of libraries available. The key difference lies in how deep and extensive those libraries are.
Let’s look at a concrete example. When writing a web application in Python, using the Django framework is almost inevitable. Django provides built-in ORM, user authentication and authorisation, and a full-featured CRUD admin interface where models can be listed and edited. Add Django Rest Framework, and you also have a complete API for your frontend or mobile app.
To achieve similar functionality in Go, multiple libraries and significantly more manual development are required. You can use GORM for ORM support, and if the standard http package doesn’t suffice, there are several web server and request/response handling libraries available, such as Gin, Fiber, or Chi. Each of these sits as a lightweight layer on top of the standard http package, and unlike Django, they don’t offer an admin interface out of the box. While there is a Django-like framework for Go called GoAdmin, it has limited support and poor documentation.

3. Object-oriented programming in Go and Python

Python is object-oriented and makes full use of OOP features, which it also actively encourages. In larger applications, this often proves advantageous, as it simplifies the introduction of higher-level abstractions and code reuse. However, this can also lead to overly complex codebases.
The Go programming language is a structured language and supports many modern language features, such as interfaces. But Go adheres to a philosophy that “a little copying is better than a lot of abstraction,” which contrasts sharply with the typical enterprise mindset. Code written in Go is often easier to maintain, and developers can get up to speed with a project more quickly.
It also supports an evolutionary coding style - allowing the codebase to grow according to actual needs, rather than being built around abstractions intended for hypothetical future use cases.
Here’s how two simple classes look in Python, using inheritance and instantiation:
# Define a parent class called "Animal"
class Animal:
    def __init__(self, name, age):
        self.name = name
        self.age = age

    def eat(self):
        print(f"{self.name} is eating.")

    def sleep(self):
        print(f"{self.name} is sleeping.")

# Define a child class called "Cat" that inherits from "Animal"
class Cat(Animal):
    def __init__(self, name, age, color):
        super().__init__(name, age)
        self.color = color

    def meow(self):
        print(f"{self.name} says meow.")


# Create an instance of the Cat class
my_cat = Cat("Whiskers", 3, "gray")

# Use the methods
my_cat.eat()
my_cat.sleep()
my_cat.meow()
This can also be done in Go, but beyond the syntax, the underlying principle and methodology differ.
package main
import "fmt"

type Animal struct {
	Name string
	Age int
}

// Define methods for the Animal struct
func (a *Animal) Eat() {
	fmt.Printf("%s is eating.\n", a.Name)
}

func (a *Animal) Sleep() {
	fmt.Printf("%s is sleeping.\n", a.Name)
}

// Define a child struct called "Cat" that embeds the Animal struct
type Cat struct {
	Animal
	Color string
}

// Define a method for the Cat struct
func (c *Cat) Meow() {
	fmt.Printf("%s says meow.\n", c.Name)
}

func main() {
	// Create an instance of the Cat struct
	myCat := &Cat{
    	Animal: Animal{
        	Name: "Whiskers",
        	Age: 3,
    	},
    	Color: "gray",
	}

	// Use the methods
	myCat.Eat()
	myCat.Sleep()
	myCat.Meow()
}
The main distinction is that, instead of inheritance, Go uses composition - here, the Animal struct is embedded within the Cat struct. When calling the Eat, Sleep, or Meow functions, it may appear as though we’re invoking a method on a struct, but this is purely syntactic sugar. Functionally, it’s equivalent to passing the myCat variable as a parameter to the function.

4. Architectural flexibility

The Go programming language is flexible, but by design, it encourages a microservices-based project structure. Go code is easily modularised, while still allowing straightforward use of shared components - such as data models, repositories, and so on. This makes it a natural fit for building scalable, cloud-native applications.
Python, by contrast, supports virtually any architectural style - from simple standalone scripts and microservice-based applications to large, monolithic systems.

5. Running tasks concurrently

Go provides native support for concurrent execution, and the language includes built-in tools that make managing concurrency straightforward.
Python also offers ways to run tasks in parallel, but due to the Global Interpreter Lock (GIL), multithreading often doesn’t result in actual performance gains at the thread level.
Here’s a basic example of running tasks concurrently in Go:
package main

import (
   "fmt"
   "sync"
   "time"
)

func square(n int, wg *sync.WaitGroup, results chan<- int) {
   defer wg.Done()
   fmt.Printf("Calculating square of %d\n", n)
   time.Sleep(1 * time.Second)
   results <- n * n       // Send the result to the channel
}

func main() {
   numbers := []int{1, 2, 3, 4, 5}
   var wg sync.WaitGroup 
   results := make(chan int)

   for _, n := range numbers {
       wg.Add(1) // Add to the WaitGroup
       go square(n, &wg, results) // Launch goroutine
   }

   for result := range results {
       fmt.Println("Result:", result)
   }
}
It’s worth highlighting the go keyword, which executes any subsequent function call on a separate concurrent thread. In the example above, true parallel execution can be achieved without needing a WaitGroup or Channel. This is particularly valuable in web services - for instance when sending emails. There’s no need to integrate a message queue or wait for the SMTP server’s response; you simply prepend the email-sending function with the go keyword.
In Python, the code is shorter, but it won’t result in true parallelism because the Global Interpreter Lock (GIL) only allows one thread to execute at a time on the CPU. As a result, the code below will only offer performance improvements if the time-consuming task is I/O-bound rather than CPU-intensive.
import multiprocessing
import time

# Function to be executed in parallel
def square(n):
   print(f"Calculating square of {n}")
   time.sleep(1)  # Simulate a time-consuming task
   return n * n

if __name__ == "__main__":
   # Create a list of numbers to compute the square
   numbers = [1, 2, 3, 4, 5]

   # Create a pool of workers
   with multiprocessing.Pool(processes=3) as pool:
       # Map the function to the numbers list
       results = pool.map(square, numbers)

   print("Results:", results)
  

6. Package management

One of the most significant differences between Go and Python in terms of package management lies in Go’s built-in ability to fetch packages directly from Git repositories using language-level tools. This eliminates the need for separate package managers or registries. As a result, the source code of imported packages is easily accessible within the IDE, which simplifies development.
A common scenario - wanting to modify how a particular element from a library is used - becomes trivial to solve in Go. You can simply fork the repository, make the necessary changes, and redirect the import path to your version of the package.
Go also allows a project to import multiple major versions of the same package simultaneously, which adds flexibility to dependency management.
Python, by contrast, takes a completely different approach, relying on separate tools - such as pip, uv, or conda - for package management. This fragmentation can introduce its own set of challenges.
The most widely used and default tool, pip, does not enforce best practices: version control is often disconnected from the actual environment. It’s easy to accumulate a large number of packages, many of which are indirect dependencies. There’s no built-in support for lock files, which complicates environment reproducibility and management. Additionally, resolving dependency conflicts manually can be a considerable challenge - especially for beginners unfamiliar with the interplay of Python’s various tooling systems.
Several third-party tools - such as pipenv, uv, and poetry - have emerged to fill the gaps left by the default toolkit. However, these tools are often incompatible with one another, making it even harder for developers to choose the right setup. This fragmented ecosystem presents an ongoing challenge: unlike the standard pypi.org and pip setup, conda represents an entirely separate world, particularly prevalent in scientific computing and data processing.
To make matters more complex, most Python tools require Python itself to be installed first, leading to a so-called “toolception” problem - tools are needed just to manage development tools. While a recent trend has seen some tools rewritten in Rust, offering statically linked binaries, the complexity of managing the Python coding language ecosystem remains a persistent hurdle.

Python or Go: which language is better suited to which projects?

Go and Python programming languages reflect two distinct programming philosophies, and accordingly, they are best suited to different types of projects.
The Python language is used much more broadly for desktop applications and projects involving mathematics or artificial intelligence. Thanks to its simpler syntax and ease of learning, the Python coding language is more accessible to professionals outside traditional software development (such as mathematicians). In addition, its extensive ecosystem of libraries makes it a practical choice for enterprises when performance is not the main concern.
  • Some examples: TensorFlow, YouTube, Dropbox, Spotify
The Go programming language, on the other hand, is recommended for more scalable, performance-critical applications where maintainability and long-term support are priorities. It is especially common in the web backend space and widely used in containerisation tools, databases, and DevOps platforms.
  • Some examples: Kubernetes, Docker, Prometheus, Terraform, Caddy, ImmuDB
In conclusion, in complex projects, there is often room for both Go and Python code. In fact, within a microservice architecture, it may even be worth considering using different languages for different services, rather than relying on a single one across the entire system.
Choosing between Go and Python for your project? We can help!

Deciding between Go and Python for your project can be challenging. Our team of experts is here to guide you through the decision‒making process, drawing on our extensive technical knowledge to find the best fit. We prioritise aligning the tech stack with your unique requirements and project objectives. Let’s work together to find the optimal digital development approach for your success.
John Radford, Client Service Director

Let's talk about

your project

Drop us a message about your digital product development project and we will get back to you within 2 days.
We'd love to hear the details about your ideas and goals, so that our experts can guide you from the first meeting.
John Radford
Client Services Director UK