Top 8 AI programming languages you should use in 2025

Every AI project starts with a critical decision: which programming language to use. 

It affects how fast you can build, how well your system performs, and whether your solution can scale when it takes off.

The thing is, there’s no shortage of options. 

Python is the usual go-to and dominates research, but it’s not always the best for real-time AI. 

Julia is fast, but its ecosystem is still young. C++ offers control and efficiency, but at the cost of complexity.

That’s where this article comes in. We’ll break down the top AI programming languages and show you where each one fits best and where it can hold you back.

From the familiar (Python, Java, R) to the specialized (Julia, Haskell, Lisp), you’ll get a clear picture of which language makes sense for your project.

Let’s dive in!

AI programming languages: overview 

Before we dig deeper into each of these languages, here’s a quick rundown:

Top AI programming languages: overview

Next, we’ll cover each of these languages in more detail.

Top AI programming languages 

Here, we’ll cover each language in-depth – what it’s used for, the top AI libraries and packages, when not to use it, and its pros and cons.

Python 

Python is the go-to language for AI – and for good reason. 

Its simple, readable syntax makes it easy to pick up, whether you’re just starting out or you’ve been coding for years. 

But what really makes Python stand out is its huge ecosystem of AI and machine learning libraries.

With tools like TensorFlow, PyTorch, and scikit-learn, developers can build and train models much faster than if they were working in lower-level languages.

Python’s flexibility also plays a big role. You can easily integrate it with other languages and platforms, which makes it a solid choice for end-to-end AI workflows.

When it comes to applications, Python covers the entire spectrum of AI. It’s used in machine learning, deep learning, natural language processing, computer vision, and data analytics. 

In practice, this means everything from powering chatbots and recommendation engines to running predictive analytics systems and automation tools. 

If there’s an AI-driven solution out there, chances are Python is behind it.

Python AI libraries and packages 

• TensorFlow – An open-source deep learning framework from Google, widely used for building and training large-scale neural networks. Great for both research and production.
• PyTorch – A flexible deep learning library developed by Meta, popular with researchers thanks to its dynamic computation graphs and ease of use.
• scikit-learn – The go-to library for traditional machine learning tasks like classification, regression, and clustering. Excellent for quick prototyping.
• Keras – A high-level neural network API that runs on top of TensorFlow, making it easier to build and experiment with deep learning models.
• Pandas – A data manipulation and analysis library that simplifies handling large datasets. Often the first step before training AI models.
• NumPy – A fundamental package for numerical computing in Python, providing support for arrays and mathematical operations.
• NLTK – The Natural Language Toolkit, used for text processing tasks like tokenization, sentiment analysis, and part-of-speech tagging.
• spaCy – A more modern NLP library that’s optimized for production and handles tasks like entity recognition, dependency parsing, and text classification.
• OpenCV – An open-source computer vision library that supports image and video processing, object detection, and facial recognition.
• XGBoost – A high-performance library for gradient boosting, often used in AI competitions and production for predictive modeling.

When not to use Python 

• Performance-critical applications – Python is slower than languages like C++ or Julia. If you’re building AI for real-time systems (e.g. autonomous vehicles, robotics, or embedded devices), performance bottlenecks can be an issue.
• Mobile and edge AI – Python isn’t the best fit for mobile apps or edge devices with limited resources. Languages like Java, Swift, or C++ are more suitable.
• Large-scale enterprise systems – While Python can be integrated into enterprise environments, Java or Scala might be a better choice if system stability, concurrency, and scalability are your main priorities.
• Memory-heavy applications – Python consumes more memory compared to lower-level languages, which is problematic for massive datasets in constrained environments.
• Projects requiring strong type safety – Python is dynamically typed, which can lead to runtime errors in large, complex AI systems. Statically typed languages like Java, Scala, or C++ are more reliable.

Pros and cons of Python for AI 

C++

C++ has been a cornerstone of performance-critical software for a long time.

And that reputation carries over into AI. 

Its ability to work close to the hardware makes it one of the fastest languages available, which is crucial for applications that need real-time responses or to handle large-scale computations.

In AI, C++ is often used under the hood of popular frameworks – for example, TensorFlow and PyTorch have C++ backends for speed. 

While developers may not always write AI systems entirely in C++, its role in optimizing performance-heavy components is hard to ignore.

C++ shines where efficiency and low-level control matter most. 

Robotics, embedded AI, autonomous vehicles, and high-frequency trading systems often rely on C++ because delays of even a few milliseconds can make or break the application. 

It’s less common for rapid prototyping or research, but in production environments where performance is non-negotiable, C++ remains a strong choice.

C++ AI libraries and packages

• MLpack – A fast, flexible machine learning library built in C++, with bindings for other languages as well.
• Shark – An open-source library for machine learning, optimization, and neural networks.
• Dlib – A popular library with a wide range of ML algorithms and tools for computer vision and image processing.
• Caffe – A deep learning framework originally developed at Berkeley, widely used for image classification and convolutional neural networks.
• TensorFlow C++ API – The C++ interface for TensorFlow, enabling developers to run models at near-native speed.
• DyNet – A neural network library optimized for dynamic computation graphs, particularly in natural language processing tasks.

When not to use C++ 

• Rapid prototyping and experimentation – C++ is verbose and slower to code in, which makes it impractical when you need quick iterations.
• In a beginner or mixed-skill team – Its complexity and steep learning curve make it harder for teams without strong experience in systems programming.
• Projects relying heavily on AI research – Most research code, papers, and examples are in Python, so C++ can slow down experimentation.
• Data-heavy workflows – Tasks like data cleaning, exploration, or analytics are much easier in languages like Python or R.
• When productivity matters more than performance – If development speed is your priority, C++ can become a bottleneck.

Pros and cons of C++ for AI 

Java

Java is one of the most widely used programming languages in enterprise software, and it’s carving out its space in AI as well.

Its biggest strengths are portability, scalability, and reliability, which make it especially appealing for production-grade AI systems.

While not as popular as Python in research, Java has a solid ecosystem of machine learning and big data frameworks. 

It integrates well with enterprise environments and is often the language of choice when companies want to bring AI models into large-scale production.

Java is commonly used for natural language processing, fraud detection, recommendation systems, and large-scale data analysis. 

Thanks to its stability and ability to handle high concurrency, it’s also a strong fit for real-time business applications and financial systems that need AI capabilities.

Java AI libraries and packages 

• Deeplearning4j (DL4J) – The most popular deep learning library for Java, supporting neural networks, GPUs, and integration with Hadoop and Spark.
• Weka – A classic machine learning library with a wide range of algorithms for classification, regression, clustering, and visualization.
• MOA (Massive Online Analysis) – A framework for stream mining, ideal for real-time machine learning on evolving data streams.
• Java-ML – A lightweight machine learning library that provides implementations of common algorithms for clustering, classification, and feature selection.
• H2O.ai – A scalable machine learning platform with Java APIs, widely used in enterprise AI applications.
• Spark MLlib – While written in Scala, it integrates seamlessly with Java for distributed machine learning and big data AI workloads.

When not to use Java 

• Rapid experimentation and prototyping – Java is more verbose than Python, which slows down quick iterations in research or early-stage projects.
• Cutting-edge AI research – Most academic and open-source AI work happens in Python, so Java lags behind when it comes to cutting-edge tools and libraries.
• Small-scale or one-off projects – The setup and boilerplate code can be overkill when a lightweight language would do the job.
• Memory-constrained environments – Java’s garbage collection can introduce overhead that isn’t ideal for real-time or embedded AI.
• If you’re focused on statistical modeling – R or Python provide a better environment for pure data science and statistical work.

Pros and cons of Java for AI 

Julia 

Julia is gaining traction in AI because it combines the speed of low-level languages with the simplicity of higher-level ones. 

It’s designed for numerical and scientific computing, which makes it a natural fit for AI and machine learning tasks that need heavy computation.

One of Julia’s biggest strengths is performance. 

It can approach C++-level execution speeds while still feeling accessible to data scientists and researchers who might not come from a traditional software engineering background. 

On top of that, Julia was built with parallelism and distributed computing in mind, which makes it well-suited for large-scale AI projects.

In practice, Julia is increasingly used for deep learning, probabilistic programming, and data science.  Especially if you want to experiment quickly.

While its ecosystem is still smaller than Python’s, Julia continues to grow and is carving out a strong role in performance-driven AI.

Julia AI libraries and packages 

• Flux.jl – The most widely used deep learning library in Julia, offering a simple and flexible way to build neural networks.
• MLJ.jl – A machine learning framework that provides a consistent interface to a wide range of algorithms, similar to Python’s scikit-learn.
• Knet.jl – Another deep learning package, designed for dynamic computation graphs and GPU acceleration.
• Turing.jl – A powerful library for probabilistic programming and Bayesian inference.
• DataFrames.jl – Used for data manipulation and analysis, comparable to Pandas in Python.
• CUDA.jl – Brings GPU computing to Julia, which makes it possible to efficiently run large-scale AI workloads.

When not to use Julia 

• Small projects or prototypes – If you’re building a quick proof of concept, Julia’s smaller ecosystem and fewer ready-made tools can slow you down compared to Python.
• If you have a team with mixed skill levels – Julia isn’t as widely adopted, so finding developers who know it well can be harder. This can create onboarding challenges.
• Production environments – Julia is still relatively young compared to Python, Java, or C++. Its tooling and deployment options aren’t nearly as mature.
• Mobile or web-based AI applications – Julia doesn’t have strong support for mobile or front-end development, so it’s not a good fit if that’s your target environment.
• If you need community supprot – While it’s growing, Julia’s community is still much smaller than Python’s, which means fewer tutorials, libraries, and troubleshooting resources.

Pros and cons of Julia for AI 

R 

R is a language built with statistics and data analysis at its core, which makes it a natural fit for certain areas of AI.

It’s especially strong in statistical modeling, data visualization, and exploratory data analysis. If data understanding is just as important as model performance in your project, R can be a great choice.

In AI, R is often used for machine learning, data mining, and predictive analytics. 

It has a wide collection of packages that make tasks like regression, classification, clustering, and time-series analysis straightforward. 

Researchers and data scientists value R for its ability to produce clear, detailed visualizations that help interpret AI models and their results.

That said, R is less common in large-scale production environments. 

Instead, it tends to shine in research, academia, and industries like healthcare, finance, and bioinformatics where deep statistical insight is crucial.

R AI libraries and packages 

• caret – A comprehensive package that provides a unified interface for training and evaluating a wide range of machine learning models.
• randomForest – An implementation of the random forest algorithm for classification and regression tasks.
• xgboost – A high-performance gradient boosting library, widely used in competitions and real-world predictive modeling.
• nnet – A package for training single-hidden-layer neural networks.
• kerasR – An R interface to the Keras deep learning library, allowing access to TensorFlow models.
• mlr3 – A modern framework for machine learning in R, offering flexibility and support for advanced workflows.
• ggplot2 – While not an AI library per se, it’s essential for data visualization and understanding model results.

When not to use R 

• Large-scale production systems – R isn’t optimized for AI deployment at enterprise scale, languages like Java or Python are better suited for that job.
• Performance-critical applications – R can be slower than C++, Julia, or even Python for heavy computation.
• Mobile or embedded AI – R has almost no support for mobile or edge deployments.
• General-purpose programming – R is specialized for statistics and data analysis, so it’s less effective for building end-to-end AI workflows.
• If you’re focused on modern deep learning – While R connects to frameworks like TensorFlow and Keras, its ecosystem is still smaller and less active compared to Python.

Pros and cons of R for AI 

Scala 

Scala brings together object-oriented and functional programming, making it a strong option for AI if you’re working with big data and distributed systems. 

Running on the JVM (Java Virtual Machine), it inherits Java’s stability and scalability while offering more concise syntax and modern programming features.

Scala is especially popular in AI projects that rely on large-scale data processing. 

With its tight integration with Apache Spark, it’s often the language of choice for building machine learning pipelines that need to handle massive datasets. 

This makes it well-suited for industries like finance, e-commerce, and telecom where predictive analytics and recommendation engines are powered by big data.

While Scala isn’t as widely used as Python in AI research, it shines in production environments where performance, scalability, and concurrency are key. 

For companies that already run Java-based systems, adopting Scala can also be a natural step toward more modern AI development.

Scala AI libraries and packages 

• Spark MLlib – The core machine learning library for Apache Spark, offering scalable algorithms for classification, regression, clustering, and recommendation.
• Breeze – A numerical processing library for Scala, similar to NumPy in Python, used for linear algebra and scientific computing.
• DeepLearning.scala – A library that brings deep learning capabilities to Scala with a focus on functional programming.
• Smile (Statistical Machine Intelligence & Learning Engine) – A fast and comprehensive machine learning library for Scala and Java, covering NLP, computer vision, and data mining.
• TensorFrames – Integrates TensorFlow with Spark, allowing developers to run TensorFlow computations on Spark clusters.
• PredictionIO – An open-source machine learning server built on top of Spark and Hadoop, designed for scalable AI applications.

When not to use Scala 

• Small-scale projects – Scala’s setup and complexity can be overkill if you don’t need big data or distributed computing.
• Rapid prototyping – Python is faster for experimentation and has more prebuilt AI resources.
• If you’re new to functional programming – Scala’s learning curve can be steep, especially for developers used to strictly object-oriented languages.
• Mobile or embedded AI – Scala doesn’t have strong support for mobile or edge deployment.
• Research-heavy projects – most research frameworks and academic work are centered on Python, making Scala less convenient.

Pros and cons of Scala for AI 

Haskell

Haskell is a purely functional programming language known for its strong type system, immutability, and mathematical precision. 

In the context of AI, Haskell is a good choice if you value correctness, reliability, and expressive abstractions. 

With its declarative style, you focus on what the program should do instead of how to do it, making complex AI algorithms easier to understand and work with.

While Haskell isn’t as mainstream in AI as Python or Java, it has carved out a niche in research-heavy fields and projects that need high levels of safety and formal verification. 

For example, Haskell is often used in symbolic AI, theorem proving, probabilistic programming, and academic explorations of new AI paradigms.

Its ability to handle concurrency and parallelism also makes it attractive for certain computationally heavy AI workloads. 

However, Haskell’s smaller ecosystem and steeper learning curve compared to more popular AI languages limit its adoption in large-scale production environments.

Haskell AI libraries and packages 

• HLearn – A machine learning library for Haskell, designed around algebraic structures for scalable learning algorithms.
• Grenade – A purely functional deep learning library that supports building and training neural networks.
• HMatrix – A numerical computing library used for linear algebra, optimization, and matrix computations, similar to NumPy.
• TensorFlow Haskell bindings – Provides access to TensorFlow’s functionality from Haskell, though less mature than Python’s API.

When not to use Haskell

• Mainstream AI development – Most cutting-edge frameworks and research codebases are in Python, so Haskell lags behind in tooling and community resources.
• Large enterprise production systems – Limited support, fewer developers, and smaller ecosystem make it harder to maintain at scale.
• Rapid prototyping – Haskell’s steep learning curve and smaller set of ready-to-use libraries slow down experimentation compared to Python or Julia.
• Mobile, web, or embedded AI – Haskell doesn’t have strong support for applied AI outside of research or niche domains.
• If you have limited functional programming experience – Haskell’s purely functional paradigm can be challenging for developers used to imperative or object-oriented languages.

Pros and cons of Haskell for AI 

Lisp 

Lisp is one of the oldest programming languages in AI, dating back to the 1950s. 

It was originally designed for symbolic computation, which made it a natural fit for early AI research in areas like expert systems, natural language processing, and theorem proving. 

Many foundational AI concepts and tools were first developed in Lisp.

What sets Lisp apart is its flexibility. Features like homoiconicity (code as data) and powerful macros allow developers to create highly adaptable and expressive AI systems. 

This made Lisp especially popular in academic and research environments where experimentation and novel approaches were key.

Today, Lisp isn’t widely used in mainstream AI development, but it still has a niche following. 

Some researchers and developers appreciate its strengths for symbolic AI, prototyping, and projects that require highly dynamic behavior. 

While its ecosystem is small compared to modern languages, Lisp remains an important part of AI’s history and continues to influence the design of newer programming languages.

Lisp AI libraries and packages 

• CLML (Common Lisp Machine Learning) – A library offering implementations of common machine learning algorithms in Lisp.
• mgl – A machine learning library for Common Lisp, supporting neural networks and deep learning.
• Antik & GSLL – Numerical libraries that provide linear algebra and scientific computing functions, similar to NumPy in Python.
• AIMA Lisp code – Implementations of algorithms from the classic “Artificial Intelligence: A Modern Approach” textbook, written in Lisp.
• CLASP – A Common Lisp environment that integrates with LLVM and C++, used for performance-oriented AI and scientific computing projects.

When NOT to use Lisp 

• Mainstream AI projects – Modern AI frameworks and tools are rarely built with Lisp, so support is limited.
• Large-scale production systems – Lisp lacks the ecosystem and tooling maturity of Python, Java, or C++.
• If you don’t have Lisp expertise – The language has a steep learning curve and isn’t widely taught today, making it harder to build and maintain a team.
• Performance-critical applications – While Lisp can be optimized, languages like C++ and Julia are better suited for low-level efficiency.
• Mobile, web, or edge AI – Lisp has almost no support in these environments, making it impractical for modern applied AI use cases.

Pros and cons of Lisp for AI 

Pros: Extremely flexible, dynamic and adaptable programming, good for AI research

Cons: Not widely used, small modern ecosystem

How to choose the right AI programming language

With so many options available, the “best” AI language isn’t universal.

It depends on your goals, resources, and context. 

Here are the key factors to consider when deciding which one fits your project:

• Project stage – For prototyping and quick experimentation, Python is usually the best fit. And for production systems that need reliability and performance, languages like Java, C++, or Scala are a better choice.
• Performance needs – If your AI model requires real-time responses , C++ or Julia will serve you better than Python or R. But, for research and data-heavy analysis, Python or R can provide the speed of development you need.
• Ecosystem and libraries – Python has the broadest support with mature libraries for almost every AI task. Niche languages like Haskell or Lisp are better for specialized research, but they lack the extensive libraries and tooling of mainstream options.
• Team expertise – Choosing a language your team already knows can save time and reduce costs. Adopting a less common language like Julia, Scala, or Haskell requires extra training or hiring specialized talent.
• Deployment environment – For enterprise AI, Java or Scala work well thanks to their JVM ecosystem. For mobile or embedded AI, C++ is often the best choice. And for cloud-based AI workflows, Python and Julia both integrate easily.

The bottom line: there’s no one-size-fits-all language for AI (although Python comes close!).

You should start by clarifying your project’s needs, then balance performance, ecosystem, and team skills to choose the right tool.

AI programming languages: FAQs

Conclusion

AI opens the door to endless possibilities, but the language you choose will determine how far you can go.

Python will still be the first (and only) stop for most people in 2025, but performance-driven projects may lean on Julia or C++.

Enterprise systems often call for Java or Scala, while R, Haskell, and Lisp hold their ground in more specialized domains.

The key takeaway? Start with your project’s needs, not the hype.

Consider performance, scalability, ecosystem, and your team’s expertise before committing.

When you match the right language to the right problem, you give your AI project the best chance to succeed.

And if you’re into this kind of deep dive, head over to our engineering hub — there’s plenty more where this came from!