June 6, 2024

NodeJS VS Python: Which Technology Is The Best Option For AI Development?

Choosing between Python or NodeJS is a frequent dilemma in AI development. Let’s explore their advantages and disadvantages to find which technology is the best.

Written by
Serhii Uspenskyi

Table of Contents

Artificial intelligence development is a hot topic for many programmers worldwide nowadays. Some of them strive to build the next image, text, or image-generation tool that will blow away the public. Others - work on advanced computer vision solutions that will give true sight to vehicles and drones. No matter which solutions they work on, one of the main questions they often ask is: what programming language to choose for AI development?

According to Statista, Python was used by around 49% of developers in the last year, while Javascript was used by around 63% of engineers.

Today we want to talk about NodeJS and Python, as these are the most popular programming languages that are used for AI/ML development. While one is a programming language and the other is a runtime environment, some features tie them together. Each of these two programming technology has its advantages and disadvantages, as well as situations where they fit best in AI development. Our guide is all about leading AI programmers to make a good choice and providing tips for business owners on how to hire good AI developers today.

Python And NodeJS: A Comparison

Python is one of the world’s most popular programming languages. In the wider field, it’s a common element used in including web development, data analysis, automation, scripting, and cybersecurity measures. It’s a good choice for web projects that require a lot of data processing, need to make complex calculations or financial automation.

NodeJS is an open-source JavaScript runtime environment that’s based on the JavaScript programming language. Unlike Python, NodeJS can be used to develop the front and backend sides of applications with a unified stack. This open-source environment is perfect for the quick development of real-time web projects, such as chatbots and trackers.

These tech stacks seem similar on the surface, both being open-source and free-to-use. However, several crucial differences make them good candidates for different types of tasks related to artificial intelligence and machine learning programming.

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  1. Architecture

Architecture is a component that will define how the AI solution will be structured. The more efficiently its elements are placed, the better the result will be. Here’s how these stacks compare in this field.

Python. The programming language is synchronous and takes more time to run commands. Due to this fact, Python is a less-than-ideal choice for AI solutions like assistants or face recognition projects that require fast response time. However, this limitation can be addressed by leveraging the Python Asyncio library.

Node. This environment is a better choice for architecture development as it's event-driven and asynchronous. These traits make NodeJS a good pick for the fast development of AI products. Another great thing about NodeJS is its capability to process several requests simultaneously.

  1. Community

Knowledge - is our power. And it is especially important when we are talking about using knowledge while building AI products.  When working with a piece of technology, it's possible to come across issues and challenges. 

These problems can be quickly resolved with a bit of outside help. Programming communities are a wellspring of knowledge and enthusiasts who regularly come up with new features and release regular updates. Let’s take a look at their communities.

Python. The programming language has an older community of new and veteran developers.  There’s always someone to help them out with similar projects to be found on Python forums, dedicated websites, and platforms. 

NodeJS. Being not as old as Python, NodeJS also boasts a wide community of like-minded individuals. They can help out fellow developers with advice or offer them access to free tools for faster AI development.

For both Python and NodeJS, there is a good community guide on the Github platform.

  1. Error Handling

Programming can not be perfect and it can’t exist without its quirks, and mistakes that are bound to happen when working on things as complicated as machine learning and artificial intelligence. When it comes to Python and NodeJS, handling errors has several peculiarities.

Python. Python has strong troubleshooting through compact and clean code. Since the language doesn’t support parallel processing, debugging is a lot faster and easier.

Node. Node.js multithreading abilities lead to high performance but make it hard to handle errors as the environment processes multiple requests simultaneously.

  1. Extensibility

A sign of a robust AI solution is how easily it is to add new features and tools to its structure. The more data structures, frameworks, and functions an app can have, for example, blockchain solutions, the more versatile it is. Here’s how NodeJS and Python compare in this regard.

Python. This programming language has many frameworks that make it excellent for backend work on AI-based products. Django, FastAPI, Web2py, and Flask are some of the most robust available for Python.

Node. The environment also offers many frameworks for developers to work with: Sails, Express, and many others. They can also utilize Jasmine for testing, Babel for front-end development, and Log.io for project management.

  1. Libraries and Tools

Modern developers don’t spend a lot of time today on just pure coding all the time as it doesn’t make any sense. Libraries help speed up this process by offering pre-made code tailored for specific tasks. They often contain specific reusable functions and classes that can be used in developing various pieces of software. NodeJS and Python offer a different level of access to AI-related libraries.

Python. Python makes development easy thanks to a wide variety of frameworks and libraries made for AI and ML technologies. Programmers access tools like PyTorch, TensorFlow, and Keras that make it easier to tailor complex AI algorithms, such as computer vision scanning or image data processing.

NodeJS. Programmers mostly use Node.JS as a server-side and real-time application tool. It has access to a vast number of libraries on web design and development. Many of them are available for AI and ML technologies. 

  1. Learning Curve

Every programming language needs plenty of time to be learned and tons of practicing lessons are must have. Almost all languages offer a different approach to structure and task execution. When it comes to Python and NodeJS, there are some of the easiest programming techniques anyone can learn and work with. 

Python. Python is one of the simplest languages to learn, even for people who have never programmed before. It has an English-like syntax and is highly dynamic, making Python easy to adopt and adapt to.

NodeJS. NodeJS is based on the JavaScript programming language, so anyone with its knowledge can master the environment. Some of its more complex parts, like event-driven architecture, take a bit of time to learn but are worth the while.

  1. Performance And Speed

No matter what kind of AI solution you’re working on, it’s crucial that it doesn’t take forever to perform each task. Performance and speed are important features to look for in a tech stack, so how do NodeJS and Python compare in this characteristic?

Python. The problem with Python lies in the fact that it has a single flow of code. This means that the programming language processes requests slower than others. Additionally, its architecture prevents Python from multithreading, making it a considerable choice for AI apps that rely on speed.

NodeJS. Node processes multiple requests thanks to its non-blocking and event-driven architecture. The V8 JavaScript engine makes the environment an even more viable solution for AI development. NodeJS can be easily applied to make event-driven applications like network managing systems, analytics dashboards, and so on.

  1. Scalability

In order to be able to handle increasing numbers of users, requests, and data volumes, artificial intelligence products need to be scalable from the start. Python and NodeJS are a bit different here in terms of scaling opportunities.

Python. Python uses the Glober Interpreter Lock that doesn’t support multithreading. This issue can be addressed with imported libraries but doesn’t solve the flaw on a fundamental level. At the same time, Python can be used with a combination of other programming languages and it will fix this issue easily.

NodeJS. Node.JS app development frameworks support multithreading through asynchronous architecture, leading to highly scalable products. Apps can be upgraded by the simple addition of new models. Programmers can add resources to the nodes or add nodes to the app structure.

  1. Universality

A programming technology has to be universal to save developers time in adjusting code for different operating systems. NodeJS and Python display great characteristics in this field, with the latter having several downsides programmers should be aware of.

Python. Python is highly compatible with many operating systems. Programmers use it to build applications that work on Windows, MacOS, Linux, and UNIX without having to adjust the code for any of these systems. However, it's not suitable for mobile app development. At the same time, it can be used with hybrid mobile programming languages such as React Native and serve as a backend. We have a good example of such a duo - Employee Recognition System.

NodeJS. Like its counterpart, NodeJS can be used to make applications for various platforms. It’s used to handle frontend and backend development for Windows, MacOS, and Linux. Engineers may use it to make AI-enhanced web, mobile, and cloud apps. The environment is also used in creating IoT products.

AI Use Cases

Python and NodeJS have their pros and cons when it comes to different parts of development. But what about the practical applications of these technologies in working on AI solutions? Turns out, they are almost nothing alike! Check out the most common use cases for these technologies and find out for yourself.

Python AI Use Cases

  • Computer vision. Python is a common component in CV applications. Developers use it to teach machines how to process images and video, detect objects, and recognize faces. They have vast applications in logistics and quality control. At Springs, we used it in one of our complex logistics solutions to recognize trucks of a large enterprise.
  • Data analysis. Data scientists and analysts use the language for data exploration, cleaning, and preprocessing.
  • Natural language processing. Programmers often turn to Python to have software solutions that classify text, analyze sentiment, translate languages, and function as chatbots. SpacY and NLTK libraries are popular choices for these tasks.
  • Machine learning. Python is the primary language for making deep-learning models. Combined with PyTorch or TensorFlow libraries, it allows handling all stages of developing machine learning algorithms.
  • Predictive analysis. Python is a perfect choice to teach AI products about predictive analytics. Libraries like Prophet are a common component in time-dependent data analysis and forecasting.
  • Reinforced learning. This language is a key part of building reinforced learning algorithms. OpenAI Gym and other libraries provide environments where learning agents can be tested and developed.
  • Speech recognition. AI developers frequently use Python when working on text-to-speech applications, with the SpeechRecognition library being one of the most popular ones for this purpose. A great example of speech recognition is our NLP Mental Heath Helper which was developed for a big Spanish telecommunication company.

NodeJS AI Use Cases

  • API development. NodeJS is a good choice for RESTful APIs. This environment makes it easier to work on the backend of APIs that interact with AI models. We have worked with this technology in our AI Decision Maker project.
  • Data processing pipelines. NodeJS can be used in making data processing pipelines that handle data flow in A solutions. The environment can manage the coordination between different tasks in these systems.
  • Dashboard and visualization. A combination of NodeJS and frameworks like Vue and React is often used in making interactive dashboards and visualizations for AI products. It’s particularly useful to present insights to end-users. One of the best examples of such solutions is our AI Text-to-video generation tool that has a complex visualization builder.
  • Integration with JavaScript AI libraries. While NodeJS has fewer libraries than Python, the ones available in this environment can be integrated into web applications and services.
  • Microservices architecture. Programmers often create AI systems using this type of structure. The lightweight and fast NodeJS can develop microservices for specific AI tasks, making solutions easy to maintain and scale.
  • Prototyping and rapid development. NodeJS is a great tool to deliver fast prototypes and POC applications. Its flexible environment allows programmers to experiment with different algorithms and components. For example, we have delivered an MVP version of our own Project Management system by using Express in just within couple of months.
  • Real-time applications. This environment is a good choice when working on products like virtual assistants, chatbots, IoT applications, navigation systems, and other real-time applications.

Bottom Line

Python and NodeJS are powerful tools when it comes to developing AI solutions. When choosing a tech stack for your next project, use our guide to understand better which technology to go with. If you know someone who can benefit from this information, share our article with them.

It can be sometimes tricky to hire Python engineers or find a senior NodeJS developer with specific requirements, but we are here to help you and will be glad to provide a free consultation on this point. Don’t hesitate to reach us anytime!

Customer retention is the key

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Next steps to increase your customer retention

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