Python, Java, or C++? Which programming language is best for AI?

Each of these programming languages can be the best for artificial intelligence — the only question is what task you are using it for. In this article, together with Yevhen Kasyanenko, an expert and team leader at KISS.software, we analyze where Python is truly irreplaceable, when it is worth looking at Java, when C++ is indispensable, and what makes Rust so attractive.

Python is the main tool for machine learning

Python has remained the most popular programming language for artificial intelligence for many years. It is chosen by researchers, engineers in corporations, and startups alike. The simplicity of the code, the huge number of libraries, and the active community have made it the standard in machine learning and data analysis.

Why is Python popular in AI?

There are several reasons why Python has established itself in this role:

• The entry threshold is relatively low. The code is concise and easy to read. Even a novice developer can quickly figure it out and put together a working prototype.
• An ecosystem of convenient solutions. Thousands of ready-made libraries mean that you rarely have to reinvent the wheel. Need to process images? Use OpenCV. Want to build a neural network? TensorFlow and PyTorch are at your service. Working with data? Pandas can handle any tables.
• Community. Python brings together one of the largest communities of developers in the field of artificial intelligence. This means that you can always find help, documentation, or a working example on forums, GitHub, and specialized chat rooms.

The best Python AI libraries

It is libraries that have made Python a leader in AI. Here are the main tools that almost every project needs:

• TensorFlow and PyTorch are the flagships for working with neural networks. The first was created by Google and is well optimized for mobile devices, clouds, and GPUs. The second is from Facebook and is more flexible and convenient for quick experiments and research.
• Scikit-learn is a library for classical machine learning. It is suitable for tasks such as classification, regression, or clustering, when there is no need to build deep neural networks.
• Pandas and NumPy are the basis for working with data. Pandas allows you to work with tables and clean data conveniently, while NumPy provides fast calculations with multidimensional arrays. Considering that in real projects most of the time is spent on data processing, these libraries solve this problem, making the process as painless as possible.

Limitations of Python

Despite all its advantages, Python has its weaknesses:

• Speed. It is an interpreted language and is slower than C++ or Java. This can be critical in real-time tasks, so key libraries are written in C/C++ and simply connected to Python.
• GIL (Global Interpreter Lock). This feature limits threading: Python cannot execute multiple threads in parallel in a single process. To work around this, multiprocessing is used or computational tasks are moved to C/C++.

“In real projects, Python is sometimes combined with other languages: prototypes and model training are done in this programming language, while computational cores are optimized, for example, in C++ or integrated via Java,” notes Yevhen Kasyanenko.

Java – stability and reliability for AI in business

Python is good for experiments and quick MVPs. But when a project grows into a full-fledged system that needs to run stably for years, Java comes into play. This language is valued for its predictability and stability. Banks, telecoms, insurance, and medical services choose it when building AI solutions that need to run for years without failure.

Why Java is chosen

• Multithreading. Java skillfully distributes the load between processor cores, manages memory, and monitors performance. The application can run for months without restarting, maintaining a stable speed.
• Scalability. The system can be adapted to business growth without rewriting the entire code. So it’s write once, run anywhere.
• Large ecosystem. Java has time-tested tools for machine learning and data analysis, so developers don’t have to reinvent everything from scratch.

Basic tools

• Deeplearning4j turns Java into a platform for deep learning. The library supports GPU computing and integrates easily with Apache Spark for big data processing.
• Speaking of Spark, Apache Spark MLlib has become the standard for machine learning on big data. You can process terabytes of information using clustering and classification algorithms directly in a distributed environment.
• Weka remains a classic for academic research and quick experiments. Its simple interface and visualization of results make it popular in universities.

Where Java is weaker

You have to pay for reliability. Java is verbose—what can be written in ten lines in Python may require fifty here. In addition, strict typing and mandatory class declarations slow down prototyping.

Performance is also not ideal. The virtual machine adds overhead, and Java may not be fast enough for real-time tasks.

“If you need an AI solution for a large business where reliability, support, and long-term operation are important, Java, despite its shortcomings, is one of the most logical choices,” says our expert.

C++ – for maximum speed and control

If Python is a convenient tool for experimentation and Java is the choice for reliable enterprise solutions, then C++ is needed where every millisecond is critical. This language is loved for its speed and precise control over resources. In robotics, driverless cars, or image processing systems on medical equipment, C++ is simply indispensable.

When to choose C++

If the following points are important for solving your task with artificial intelligence, you should choose the C++ programming language:

• Maximum performance. C++ code runs faster than Python or Java, which is important for real-time systems.
• Full control over memory. Here, the developer manages the resources themselves, which allows them to create solutions for limited environments such as embedded systems.
• Reliability for complex calculations. Video processing, sensor operation, and simulation algorithms require this level of precision.

The most commonly used tools

To work with AI in C++, developers most often rely on time-tested libraries. They save months of manual work and allow you to focus on the logic of the task rather than low-level implementation. The main tools are as follows:

• OpenCV. A classic in computer vision. This library can do everything from simple image processing to complex video analysis. It is used to build face recognition systems, drone autopilots, and even medical diagnostic tools.
• TensorFlow C++ API. Allows you to take pre-trained models (often in Python) and implement them directly into C++ applications. This approach provides hardware performance while maintaining the flexibility of modern neural networks.
• MLpack. Suitable for classic machine learning. It implements fast algorithms for tasks such as classification, clustering, or prediction—all with an emphasis on speed.

Where C++ is more difficult

C++ requires more development time. You can’t just “throw together” a prototype in an evening: you need to work carefully with memory and take into account many technical details. Therefore, the entry threshold is higher than for Python.

“If a project needs to run quickly, without delays, and use resources as efficiently as possible, C++ is the most reliable choice,” notes Yevhen Kasyanenko.

Rust – a young player with big ambitions

Rust is not yet as popular in AI as Python, Java, or C++, but it is increasingly in the spotlight. It is valued for helping to write fast and secure code. This language is literally built around the idea of reliability: it does not forgive memory errors and immediately catches them at the compilation stage. For developers who want their system to run stably and without crashes, this is a significant advantage.

What makes Rust interesting for AI

• Memory safety. The compiler strictly controls ownership and eliminates leaks at the build stage. For AI systems where stability is critical (for example, in medical devices or IoT), this is a huge plus.
• High performance. Rust runs almost as fast as C++, but eliminates many of its pitfalls.
• Suitable for multithreading. Rust was designed with modern tasks in mind, so it excels at parallel computing. The language allows you to use the power of multi-core processors without unnecessary complications, while protecting against typical errors that often occur when working with threads.

What libraries do they work with

The Rust ecosystem is still in its infancy, but there are already some interesting tools available:

• tch-rs – a wrapper over PyTorch that allows you to use its capabilities in Rust.
• rust-ml – a set of basic machine learning algorithms.

Where Rust still falls short

• Few libraries. Compared to Python, for example, the choice is very limited.
• Difficult to get started. The memory control system takes time to learn, especially for those who are used to more flexible languages.

Rust is rarely chosen for first experiments with AI. But if the task is to create a reliable, secure, and fast solution, it can be an excellent alternative to classic languages.

How to choose the best programming language for AI

There is no universal answer like “just use Python and that’s it.” Each language is needed for its own tasks. Based on our experience, we can generally say the following:

• Python is the best choice for quick experiments. If you need to quickly assemble a prototype or test an idea, Python will do it faster than anyone else.
• Java is all about stability. When it comes to corporate systems with millions of users, where reliability and scalability are important, it is most often chosen.
• C++ is for when speed is critical. Robots, drones, and real-time algorithms need millisecond response times, so C++ is indispensable here.
• Rust is a newcomer to this race, but it has a bright future. It is valued for its security and high performance, especially in IoT and cybersecurity.

“Many people want to hear a simple answer – which programming language is best for artificial intelligence. But in reality, it all depends on the specific task. If you need to quickly assemble a prototype, there’s no better choice than Python. When it comes to large corporate systems, Java usually wins out. And if the project has to work in real time, it’s difficult to do without C++. That’s why I always say: don’t ask “which language is the best in general,” but “which language is the best for my specific task,” notes Yevhen Kasyanenko.

Want to implement AI in your business the right way?

At KISS, we always look at the bigger picture: language and technology alone mean little if the solution is not useful. Our goal is to build AI that really works for your business, rather than gathering dust as a beautiful prototype.