Top programing languages to build AI apps

March 17, 2025 1:59 pm
by Ajanth

From virtual assistants like Siri to recommendation systems on Netflix, AI is powering a wide range of applications. If you're looking to build an AI app, one of the first steps is choosing the right programming language. In this blog, we’ll walk you through the top programming languages for AI development and explain why each of them is a great choice.

Top 7 programing languages to build AI

1. Python

Why Python for AI? Python is the most popular language for AI development, and it’s easy to see why. It’s simple, readable, and has a large collection of libraries that make building AI models much easier. Python’s syntax is clean, which makes it a great language for beginners and experts alike.

Python’s strong support for AI development comes from libraries like:

TensorFlow and Keras for deep learning.
NumPy and Pandas for data manipulation.
Scikit-learn for machine learning.
PyTorch for neural networks.

These libraries provide the tools needed to handle everything from data preprocessing to training complex neural networks.

Advantages of Python:

Easy to learn and use.
Strong community support.
Tons of pre-built libraries for AI tasks.
Cross-platform compatibility (works on Windows, Mac, Linux, etc.).

Disadvantages of Python:

Slower execution compared to other languages like C++.
Not ideal for mobile apps or real-time systems.

2. Java

Why Java for AI? Java has been around for decades and is known for its stability and scalability. While it may not be as beginner-friendly as Python, it’s a great choice for building large-scale AI applications. Java is especially strong in areas like natural language processing (NLP) and machine learning.

Java’s platform independence (the ability to run on any device with a Java Virtual Machine) makes it a popular choice for enterprise-level AI solutions.

Advantages of Java:

Excellent performance and speed
Strong support for large-scale AI applications.
Platform independence (write once, run anywhere).
Extensive libraries like Deeplearning4j for deep learning.

Disadvantages of Java:

More complex syntax compared to Python.
Requires more lines of code to accomplish the same tasks as Python.

3. R

Why R for AI? R is a language designed for statistical computing and data analysis. If your AI project involves heavy data analysis or statistical modeling, R is a great choice. It’s widely used by statisticians and data scientists, making it perfect for AI applications focused on data mining and pattern recognition.

R also has a rich ecosystem of packages like:

Caret for machine learning.
RandomForest for decision tree algorithms.
Nnet for neural networks.

Advantages of R:

Great for statistical analysis and data visualization.
Wide range of libraries for data-related AI tasks.
Active community of data scientists and statisticians.

Disadvantages of R:

Slower execution speed compared to Python and Java.
Less versatile for non-data-driven AI tasks.

<br>

4. C++

Why C++ for AI? C++ is known for its speed and efficiency, making it a great choice for AI applications that require high-performance computing. AI algorithms can be resource-intensive, especially when dealing with large datasets or real-time applications. This is where C++ shines. It allows developers to fine-tune algorithms to run faster and more efficiently.

C++ is often used in AI applications for:

Robotics and autonomous systems.
Real-time AI systems (like video game AI).
Low-level machine learning libraries like TensorFlow.

Advantages of C++:

Extremely fast and efficient.
Fine-grained control over system resources.
Strong support for high-performance AI applications.

Disadvantages of C++:

Steeper learning curve compared to Python.
Requires more lines of code to achieve similar results.

<br>

5. Julia

Why Julia for AI? Julia is a relatively new language that’s gaining popularity in the AI community. It was designed for high-performance numerical and scientific computing, making it perfect for AI and machine learning tasks. Julia offers a good balance between ease of use (like Python) and speed (like C++).

Julia has strong support for:

Linear algebra and matrix computations.
Parallel computing for large-scale AI projects.
Libraries like Flux.jl for deep learning.

Advantages of Julia:

Faster execution than Python, close to C++.
Easy to learn, with a syntax similar to Python.
Designed for high-performance AI and data science.

Disadvantages of Julia:

Smaller community and fewer libraries compared to Python or Java.
Not as widely adopted yet, so finding learning resources may be harder.

<br>

6. JavaScript (with TensorFlow.js)

Why JavaScript for AI? JavaScript is traditionally known for web development, but with the rise of TensorFlow.js, you can now build AI models that run directly in the browser. This makes JavaScript a great choice if you want to build AI-powered web applications.

JavaScript is also good for AI applications that need to be accessible on various devices without requiring installation. Its real-time execution in the browser opens up opportunities for interactive AI applications like:

AI-powered web apps.
Real-time image or text processing.
Web-based games with AI.

Advantages of JavaScript:

Can run AI models directly in the browser.
Large community and many libraries for web-based AI.
Ideal for interactive AI applications.

Disadvantages of JavaScript:

Slower performance compared to Python and C++.
Limited support for large-scale AI tasks.

<br>

7. Scala

Why Scala for AI? Scala is a language that runs on the Java Virtual Machine (JVM) and is known for being highly scalable and efficient. It’s especially useful for big data processing and AI projects that need to handle large datasets.

One of the main reasons Scala is popular in AI development is its integration with Apache Spark, a powerful big data processing engine. Scala makes it easy to work with distributed computing and machine learning at scale.

Advantages of Scala:

Excellent for large-scale AI projects.
Strong integration with big data tools like Apache Spark.
Fast execution speed due to JVM support.

Disadvantages of Scala:

Steeper learning curve than Python.
Smaller community compared to Python and Java.

<br>

Final Thoughts

When it comes to building AI apps, there’s no one-size-fits-all programming language. The best choice depends on your specific needs, project goals, and personal preferences.

Python is the go-to language for most AI tasks, especially if you're a beginner.
Java is a great choice for large-scale, enterprise AI applications.
R is perfect for AI projects that require deep statistical analysis.
C++ is ideal for AI applications that need top-notch performance and efficiency.
Julia is the best option for high-performance numerical computing.
JavaScript is perfect for building AI-powered web applications.
Scala is excellent for handling large-scale data and AI tasks.

No matter which language you choose, there are plenty of resources and communities available to help you along the way. The world of AI development is constantly evolving, so it’s important to keep learning and experimenting with different languages and tools.