Too many requests! You have made too many requests!! No more requests!!!
– How many times do you have to hear that before you get really angry?Amazon Chime API’s request throttling tested our patience like this. But all we ever wanted was to make a simple text chat app work! In this article, you’ll find out why Chime was so unkind to us, what we did to turn things around, and how you too can follow the path we forged.

If you regularly think about how to optimize your business operations by speeding them up, increasing their accuracy, reducing related expenses, and eliminating risks associated with the human factor, perhaps the introduction of artificial intelligence will be the best option. 

Specifically, according to the CompTIA IT Industry Outlook 2024 report, today, about 22% of companies are actively seeking to integrate AI into most of their technological solutions and processes, and 45% of companies are considering its implementation. If you are still in the second category, it's time to figure out which programming language will be able to form the basis of your new highly intelligent software tools.

What Is Artificial Intelligence (AI)?

The term "Artificial intelligence" describes software algorithms and systems that are aimed at solving problems typical to the human mind – such as learning, as well as pattern generation and recognition. Such algorithms are based on colossal amounts of disparate data collected from various sources. Thus, these algorithms form self-learning software solutions capable of analyzing this data and extracting valuable insights from it.

Programming Languages ​​Most Often Used for AI in 2024

It is worth noting that AI-powered solutions may not be based on any software language – this can be explained by the high resource consumption of the AI algorithms that form them, as well as the lack of high-level functions. At the same time, there are seven languages that are most often used in AI programming.


This programming language appeared long before the popularization of AI development. However, thanks to its low entry threshold and extensive compatibility, its community quickly grew, and today, Python is considered one of the three most relevant languages worldwide.

As for the tools for artificial intelligence programming, there are a lot of Python-compatible frameworks, libraries, and APIs, aimed at simplifying and speeding up this process. Among them are TensorFlow, PyTorch, Keras, SciPy, NumPy, and many others. Using them, developers can create high-performance, reliable, and scalable solutions that can solve even the most complex research problems.


R, being a statistical programming language, is great for data analysis and visualization. Even though developing artificial intelligence and machine learning solutions is not the primary use case for R, it is still adept at handling very large numbers. So, where popular Python may fall short, particularly in Data Science and Data Analysis solutions, R may be a better alternative.

Additional benefits of this developer tool include support for functional programming, vector computing, and OOP, as well as a number of packages specifically created for AI and ML development (for example, gmodels, RODBC, and OneR).


This portable and scalable programming language is actively used in the development of highly intelligent corporate applications, especially for mobile platforms. In addition to compatibility with popular tools for ML and AI programming, such as the Deep Java Library for deep learning, OpenNLP for natural language processing, and Neuroph for building neural networks, Java is easy to write and debug program code.

It is worth noting that this language is object-oriented and scalable. It also simplifies data visualization and supports Swing and SWT (Standard Widget Toolkit), which allow developers to create attractive and complex graphics and interfaces.


C++ is considered an extremely powerful language for AI programming and can greatly benefit developers when creating games and embedded systems. Like Python, C++ is a mature language, which does not detract from its advantages, such as flexibility and high performance. C++ has several libraries for machine learning and neural networks that help complex algorithms run faster (including MapReduce, mlpack, and MongoDB). In general, many software engineers prefer this language for building projects that require high speed, as it interacts with training modules in a production environment.

Actually, that’s why this low-level language used for AI is primarily a tool for creating complex banking software, cloud systems, and intelligent speech recognition solutions.


JavaScript is a scripting language used to add interactivity to web pages. Even though it is not as popular as the AI programming languages ​​described above, it can be extremely helpful in implementing solutions for Data Science, one of the most promising areas for using JS.

In addition, JavaScript is the most popular programming language in the world, so you won’t have any problems finding the right AI development tools and frameworks that are compatible with it. The popularity of this coding language for AI can be explained by several factors: reliability, stability, abundance of high-level functions, libraries, and tools, as well as fairly low requirements for other technical skills of developers (it is likely that you will not have any difficulty finding dozens of AI tools that support JS, which will cover the whole scope of work related to your AI project) .


This machine learning language is concise and expressive, and is often used in big data solutions. Scala is a multi-paradigm language specifically designed to express common programming concepts in a simple, convenient, and type-safe manner.

Scala is commonly used in the development of machine learning algorithms and pattern detection in large data sets, and all this – thanks to the built-in Smile data processing library, extensive compatibility with AI frameworks and libraries (in particular, BigDL, Breeze, DeepLearning.scala, and so on), as well as its object-oriented and scalable nature.


Julia is the best programming language for AI powered scientific solutions and technical projects that require statistical data processing. This is a relatively new language with dynamic typing, parallel programming, and JIT compilation that has proven itself well due to its high speed (Julia-based projects are rarely inferior in performance to their C++-based counterparts), flexibility, and a mature ecosystem.

Many of the tools built into this language use polymorphic dispatch, which makes switching between them much faster than in other languages. It is also worth noting that Julia projects can be integrated with TensorFlow.jl, MLBase.jl, and MXNet.jl.

The Programming Languages That Are Not Recommended for AI

Along with the extensive list of languages for AI development, there are also those that are better used for solving other problems. These are usually niche and low-level languages, such as PHP and Ruby (scripting languages that do not have the needed flexibility and speed to simplify the development process), C (a low-level language that lacks almost all the functions needed in AI development), and Fortran (despite its use for mostly scientific purposes, it has poor functionality).

Ultimately, a seasoned software engineer will be able to cope with any AI development task using any language; another thing is that, given the expected scope of work, the languages that we described in the previous paragraph are much better suited for these tasks.

If you are looking for an experienced team that will launch the digital transformation of your business processes through custom-made AI and ML solutions, feel free to contact us.