Python vs Node js Choosing the Best Technology

Python vs Node.js: Choosing the Best Technology

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Python and Node.js are two very popular technologies in use among software developers worldwide.

Python is a fully fledged programming language that comes with its own runtime environment, whereas Node.js is only a runtime environment – a highly optimised runtime environment for JavaScript to run on servers and on local development machines.

One might wonder why the Python programming language would be compared to Node.js, which appears to be only a subset of what Python offers. The reason why is because Node.js has become one of the dominant runtime environments for server-side applications, and as a result, competes with Python on a number of use cases.

Node.js enables JavaScript to be used to code backend services, command line tools and development tools in general (more on these use cases will be discussed further down), and has done so since its initial release in 2009. Prior to Node.js, JavaScript was used primarily in the web browser, and little else.

Python on the other hand has lived on the server since its inception from the 1990s, and has survived through 3 major releases. It is still extremely relevant today, having been adopted for more contemporary use cases like deep learning applications as well as more traditional use cases like web scraping and general backend infrastructure.

This article will explore Python vs Node.js and where they stand today in terms of their popularity and where they are used, before comparing the technologies with a range of key metrics.

Let’s firstly understand why the two technologies are in their strongest positions today than at any point in their histories.

Table of Contents

Python Programming Language

Python Programming Language

Python is a dynamically typed, interpreted, general purpose programming language. As such, it has been used for many types of applications ranging from proxy servers and web scrapers to web servers, game engines and data science analysis tools.

What has made Python attractive is its read-like-English syntax, its shallow learning curve relative to competing programming languages like C++, Java, and indeed JavaScript, and its expressiveness (the ability to write complex logic with little code).

Python also comes with simple error handling – and since it is an interpreted language, it is well suited for rapid prototyping, as Python programs need not be rebuilt on every code change. The same can be said for Node.js, although the error handling mechanisms are different – as we’ll address later in the side-to-side comparisons.

Python has evolved over the years to stay relevant, which has been less than smooth sailing for the  language. Python 3.0 was initially released 13 years ago in 2008, with it taking this long for the vast majority (97% in 2021) to migrate from version 2.

Breaking changes to syntax were made in 3.0, in addition to a major under-the-hood overhaul, that caused difficulties for many projects to migrate. To further highlight this turbulent upgrade, in 2017 only 47% of Python developers were on version 3.0 or above.

Nevertheless, Python has stayed relevant and is the favoured technology for many developers, most notably in web development frameworks, data science, machine learning and reinforcement learning applications.

Python is the most dominant programming language after JavaScript (and HTML/CSS and SQL, which do not directly compete with Python or Node.js).

Node.js plays a large part in JavaScript’s status as the most used programming language – let’s explore why next.

Node js


Node.js is a fast and efficient runtime environment for JavaScript. It is famously built on Chrome’s V8 JavaScript engine that was first introduced in the Chrome web browser in 2008.

One of the key advantages that Node.js offers is its ability to run asynchronous code blocks, which were made a lot easier since the ECMA 2017 update that introduced the async and await keywords for JavaScript.

Node.js also promoted the CommonJS module standard for server-side applications, offering an effective way to import packages into a project, such as those from the public NPM package archive.

It is important to note that Node.js is still single-threaded, so asynchronous functions should not be mistaken for concurrent execution. More on this further down.

If the reader is interested in true concurrent programming, Google’s Go programming language offers a modern and elegant solution for multi-threaded, parallel programming. Read Iglu’s accompanying piece to learn more: Google’s Go Language, and Why You Should Learn It.

Nonetheless, Node.js is a very fast, efficient and appreciated runtime environment.

Node.js gives developers direct access to the file system via the fs module that is included in the standard library. There is also great database support with Node.js, so data processing and file management comes naturally.

Such support bolsters Node.js’s proposition for command line applications and web development tooling – which projects like Webpack, Babel, and a range of frontend libraries like React Native and Vue.js rely on for their development pipelines.

I mention React Native here to emphasise that Node.js facilitates native app development – iOS, Android, and others – in addition to server-side and front-end web applications. Node.js therefore spans a vast range of apps, and at many stages of each of those app’s development pipelines.

Like Python, Node.js can be installed across all major operating systems, and is a free, cross platform and open source technology.

The maintenance and support from both JavaScript and Python’s huge developer communities have kept both technologies relevant and on a continued evolution as more competitors spring up – Golang, Rust (cloud) and Julia (data science) being some of those competitors.

So where has this led to in 2022? We’ll now dive into some more technical comparisons of the two technologies.

Python vs Node.js Side-to-Side Comparison

Python vs Node.js: Side-to-Side Comparison

Given that Python and Node.js are fundamentally different technologies (a programming language versus a runtime environment), a number of the comparisons made in this section will also pertain to JavaScript code alongside the node runtime.

Likewise, we will also compare aspects of the Python runtime engine alongside the programming language itself. Node.js vs Python will now be put head to head on key metrics.

Speed and Performance

Performance is an extremely important metric for a development team, with bottlenecks of a programming language often deciding whether it is viable or not. 

However, determining performance is not as clear-cut as simply looking at open source speed comparison graphs.

BenchmarksGame clearly highlights that Node.js offers massive performance gains over Python in most of their tests. However, these tests are performed against specific algorithms, which will likely not predict the speed of your own projects.

Saying this, Python is generally dramatically slower than Node.js at raw performance as a backend technology.

These data may be conflicting for those who know that machine learning applications are primarily implemented in Python – a domain where efficiency and GPU support are paramount to process millions (or billions) of operations for larger neural networks.

Python is adopted in this field due to many of the core modules used for neural networks and common mathematical operations being written in C, C++ or other more performant languages, with Python APIs being wrapped by such operations – which are compiled to machine language.

At the time of writing, 34% of the NumPy package is written in C. Doing so gives Python programs a performance boost, while developers only ever need to call Python APIs.

Node.js also deploys this approach for optimisation, with many of the standard library modules being implemented in C/C++ – you can even import add-on modules written with C++ directly using CommonJS require statement just like a JavaScript module import.

The Node.js V8 engine is written in C++, making it somewhat natural to connect these C-based add-ons.

Execution speed is only one metric when measuring overall performance though. There are a couple of other key features to consider here:

  • Asynchronous processing support (even if single threaded).
  • Parallel or concurrent processing support (multi-threaded applications).

These not only affect the speed of a program but also whether it can scale. For this reason, scalability will be discussed next separately.

Python vs Node JS Performance Verdict

Node.js clearly is the more efficient runtime, but there are workarounds for Python and Node.js to speed up performance by implementing key functions in more efficient compiled languages (C/C++).

This has to be considered with a high level programming language such as Python and Node.js, giving teams the ease-of-use of simplified syntax and automatic memory management for key logic, while having the ability to wrap resource sensitive or otherwise-nonviable code in more suitable compilations.

Python vs Node JS Performance Verdict


Scalability is a complex problem that leans to many variables, ranging from language-specific features (asynchronous functions, multi-threading capabilities), how fast the program executes and the resources required to do so (as we discussed above), and how the language can adapt to a server-cluster that can scale up and down, with methods of keeping each server instance updated.

These are indeed many factors to consider. Many of these scaling methods did not exist when Python was initially released decades ago, so Node.js enjoyed the benefit of having these insights before its 2009 open-source release.

The ability to leverage multi-core processors, or to run on thousands of servers concurrently to load-balance and support millions of users, were problems that Python didn’t initially consider. As a result, it is more common to see such solutions implemented in JavaScript, Golang, or Java.

Both Python and Node.js programs run on a single thread, but Node.js offers more asynchronous support than Python does as a result of its event loop model (of which a clear technical explanation can be found here on the official website).

Similarly, both technologies now support asynchronous programming, which will be looked into in more detail next.

Asynchronous Programming with Node JS vs Python

Asynchronous functions allow a program to handle other processing while the asynchronous function is being executed.

Examples of such for Python / Node.js include database calls (that take longer the more complex the query), REST API calls, file management, image processing, and more – anything that can be achieved behind the scenes while the “main” program runs.

Programs can wait for asynchronous functions, commonly defined with the async keyword, to complete – with the await keyword allowing parent functions to halt execution until the function completes. So your program is flexible with which logic needs to wait, and which can continue to process.

Async and await support was introduced in Python 3.5 to complement new coroutine support, bringing the offering more inline with JavaScript’s support of the same operations (albeit with different implementations under the hood).

The asyncio library is commonly used with Python for asynchronous support, whereas Node.js typically does not require this extra layer of abstraction.

For scaling processes, Node.js has the upper-hand with tools like PM2 that were designed specifically for scaling Node.js programs.

Features include CPU scaling of clusters, easy configuration to define instances, automatic restarts on code changes, and more.

PM2 also supports Python processes, but overall support is far behind that of Node.js.

Node.js vs Python Scalability Verdict

Node.js in 2021 is ahead of the curve for scalability, but Node.js needs to perform well on scaling given that its use cases rely on such features more than Python’s.

Even though both technologies are single-threaded, V8’s event loop architecture makes async programming feel more natural with Node.js, which has given rise to event driven frameworks like RSJX that fully embrace the event driven programming paradigm with JavaScript (and other languages too).

Python continues to evolve with its coroutine support, making async programming possible for Python programs provided developers upgrade to version 3.5 or above.

Saying all this though, both technologies can be wrapped into containers and deployed to clusters using tools like Kubernetes, Ansible or cloud tools for horizontal scaling and for resource management.

Node.js vs Python Scalability Verdict

Libraries and Dev Tools

Both Python and Node.js have very robust ecosystems of IDEs, development tools and vibrant package archives. For this reason, there’s no clear winner in this category – mostly due to the large and passionate communities that Python and JavaScript have attracted.

With open source technologies such as the ones we’re discussing, the creation of tools surrounding the language often falls to passionate developers interested in streamlining their particular needs.

Statista clearly shows that Python and JavaScript have the largest number of developers worldwide of all programming languages, with some estimates at 8 million plus developers for each.

NPMJS is still the largest public repository in the world, hosting JavaScript packages for just about anything the language has to offer – and is the largest node package manager.

The success of NPM has prompted other tech giants to offer package hosting, such as GitHub Packages, in addition to private registries like Verdaccio (that also act as proxies to the public NPM archive).

Where JavaScript has a single unified platform in NPMJS, Python famously offers PyPi as a general package manager, in addition to the Anaconda platform that is tailored towards data analysis applications.

Command line tools in the npm node package manager, and pip or conda for Python, are all of a high quality specific to their audience’s requirements, allowing developers to manage the libraries they require from their corresponding archives.

Unsurprisingly, all major IDEs support Python and JavaScript development – and there is little to differentiate the technologies here. Amongst offerings, JetBrains host PyCharm; a development environment tailored for Python.

We’ve also become accustomed to cloud-based JavaScript editors such as JSFiddle or Playcode, that make it easy to communicate ideas and showcase demos online.

Another advantage of cloud-based editors is their access to backend infrastructure, provided by the service in question.

This is invaluable for machine learning developers with Python, for example, with services like Google Colab giving developers access to scalable GPUs and online editor via the popular Jupyter Notebook interface.

Node.js vs Python Libraries and Dev Tools Verdict

There is no clear winner in this category – both Node.js and Python have an extensive, robust and capable ecosystem of tools.

In addition to common support for IDEs, syntax highlighting, auto completion, virtual environments, etc, there are also platforms specific to a demographic such as with the Anaconda platform for Python.

Node.js package library support is also expanding: private package managers that act as proxies to the public NPM archive are good examples of this.

Node js vs Python Libraries and Dev Tools Verdict


We can define extensibility in two ways when comparing Node.js and Python:

  • The ability to extend the functionality of the language itself.
  • The ability to create libraries and frameworks to enhance functionality.

This article has already addressed the first point well – we understand that both Python and Node.js can be expanded with packages or modules, which can be part-coded in lower level programming languages like C or C++.

In addition to this, the amount of libraries available via the aforementioned package managers give a huge amount of functionality to the developer without even writing a line of code – in this respect Python and Node.js are very extensible.

Now, extensibility is further bolstered with the impressive amount of frameworks, more commonly termed libraries, that are designed for specific requirements. For Python, we have popular web development frameworks like Django, Flask, Pyramid, and Web2Py – all of which are designed to accommodate web services and backend development.

On the flip side, we have advanced frameworks like TensorFlow or PyTorch for the machine learning use case, which give Python additional abilities – communicating closer to silicon in order to process huge neural networks – or the Pygame library for game development – with Python indeed running a GUI for these games.

Node.js also shines in extensibility when the runtime provides command line tools for development pipelines. These have come in the form of tools like Webpack and Babel, which automate the bundling (a.k.a. compilation) of web apps and backwards compatibility respectively.

Generators are also common with Node.js, such as express-generator that initialises a web server with the necessary boilerplate code.

Node.js vs Python Extensibility Verdict

As a slightly biased Node.js developer, I have had more exposure to the tools that Node offers for web app development, in addition to how it facilitates advanced pipelines like React Native and Expo’s mobile development pipeline – Node.js is extremely extensible and impressive in this respect.

But so is Python, as we have seen, with its capability to generate GUIs, work with optimised machine code, and its large host of frameworks for web development and other specialised use cases.

Node js vs Python Extensibility Verdict

Error Handling

Python error handling is of the language’s most favoured characteristics, with its expandable and modular exception handling within try-catch blocks. With more advanced libraries like Robot Framework providing automation tools, bugs can be found and corrected in a controlled environment.

But it has to be said that the more complex a program is, the more bugs there will be found.Since asynchronous functions are used much more in Node.js vs Python, we can expect more complexity and therefore more errors.

To add to Node.js’s bug-free challenge, it supports undefined variables (variables with no value assigned), in addition to null variables.

Either of these cases is perfectly valid in Node.js – which prompted TypeScript to include strict null checks (disallow null values) as a configuration option.

Saying this though, we have backend technology such as for having real-time access to node process logs in your browser for event driven behaviour, and Jasmine for unit testing JavaScript.

Node.js vs Python Error Handling Verdict

Node.js is harder to code bug-free than Python is, but Node.js’s event driven architecture and focus on asynchronous programming makes it more likely for bugs to occur, giving an unfair advantage to Python for the potential amount of bugs to be found.

However, Python’s exception chaining and user defined exceptions make it more extensible with fewer lines of code. JavaScript’s error handling syntax is slightly more complex vs Python, providing Error objects to work with through try-catch statements.

For asynchronous error handling in JavaScript, promise rejections can be used in conjunction with catch statements, which are easy to read when using async and await keywords.

Node js vs Python Error Handling Verdict

Use Cases / Universality

By now we have ascertained that Python and Node.js are mostly used in very different circumstances, with some cross-over in the backend development space.

Python is heavily used in data analysis, AI, cloud and IoT devices. Given its extensibility, robust tooling and simple syntax  discussed above, one can see why Python has become so dominant in these categories.

The ability to leverage all the extensibility Python offers does require a high degree of skill however, with the developer requiring knowledge in lower level languages, compilation and package orchestration in addition to fluency of the Python programming language.

Python is indeed beginner friendly, but requires dedication to master. With a well-rounded programming background however, Python has large universality potential.

A similar story can be said for Node.js. Like Python, Node.js is found in the cloud, on local development machines, and even on hobbyist gadgets like the Raspberry Pi.

Node.js applications comprise web services, command line tools and development tools that pertain to bundling, compilation, and boilerplate generation.

Node.js also facilitates real-time web applications and data streaming with fantastic web socket support, an area Node.js excels in compared to Python.

Where Python is king of machine learning, Node.js is king of real-time web with the additional benefit of a unified JavaScript codebase for the frontend and backend in web projects.

Node.js vs Python Error Universality Verdict

Both technologies have excelled in Universality in recent times, with no real strong competition from other programming languages to derail their momentums.

With future technologies like the metaverse and AR experiences on the agenda of big tech companies, both Node.js and Python appear well-poised to fuel these innovations, the prior facilitating real-time experiences and the latter used for engineering increasingly sophisticated AI solutions to make these experiences as intelligent as possible.

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Node js vs Python Summary

Node.js vs Python: Summary

Admittedly, the choice between Python and Node.js is not as straightforward for all types of projects.

There are times where event driven Node.js is the obvious choice such as in large-scale web applications that have a high degree of server communication – chat bots, real time monitoring, REST APIs, GraphQL for more cutting edge solutions, are all areas where Node.js will be the better technology.

Command line tools are also well suited to Node.js given its robust standard library and easy integration with JavaScript libraries and development pipelines.

Python does have very successful web frameworks being used today, and is still the preferred choice for data processing over Node.js – web crawling, web scraping and general automation tasks have existed with Python since its early days.

But Python is maintaining its relevance through the huge growth of machine learning, reinforcement learning and natural language processing applications; its simple, expressive syntax and overall support for data science are keeping Python a very competitive and relevant language, even through its lack of multi-threading and slower runtime.

For a more visual summary, the reader can refer to Stack Overflow’s web frameworks and libraries findings. It is clear that JavaScript (and Node.js) dominate web frameworks, whereas Python based libraries have the largest chunk of “other frameworks and libraries“, consisting of scientific computing tools and machine learning frameworks.

Needless to say, both technologies are on an upwards trajectory and are both strong primary programming languages to learn for developers.

For students, consider your fields of interest before jumping onto either Python or JavaScript  – one technology will likely be more relevant than the other.

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