AI Programming Languages: Which Ones Are Used and Why?
AI is becoming increasingly accessible, but the choice of programming language is crucial for what you can actually build. Here’s a practical guide to choosing the right language for AI development.
Python
Python is arguably the most important programming language for AI and machine learning. This is largely due to its vast ecosystem of libraries and functionality. Almost anything you can imagine building already has a Python library that can help you.
Data Analysis in Python
Python offers several essential libraries for data analysis and visualisation, such as NumPy, Pandas, and Matplotlib. These are core tools for analysing data, generating statistics, and creating visualisations.
Machine Learning in Python
Scikit-learn is the standard choice for classical machine learning. It’s used for everything from forecasting to customer segmentation. You can also build neural networks—for example, for image recognition—using frameworks such as PyTorch, Keras, and JAX.
Generative AI
Python also plays a major role in generative AI.
With libraries like LangChain, you can build AI agents powered by large language models (LLMs). These agents can handle a wide range of tasks, from automating customer support to planning meetings.
Using libraries such as LlamaIndex, you can connect private data to your agents, making it easy to build internal chatbots that answer questions based on internal documents.
TypeScript
TypeScript was not particularly relevant for machine learning in the past, but it has gained significant traction as LLMs have become more prominent. Support for TypeScript across AI frameworks is growing, and the language is becoming increasingly popular.
For example, LangChain, LangGraph, and LlamaIndex are all available for TypeScript. This gives you everything you need to build RAG applications and AI agents. Combined with other TypeScript frameworks, you can also build full web interfaces around your AI solutions.
Using AI in Production
When AI models move from experimentation to large-scale production systems, different requirements arise around performance and integration. This is where languages such as Java, Go, C#, or C++ become valuable.
These languages often offer advantages over Python in terms of performance and robustness, and are commonly used in systems where AI is just one component of a large, complex architecture. Many organisations choose to train models in Python but deploy them in production using these languages.
Which Language Should I Choose?
Your choice should be guided by your goals:
Exploration & Data Analysis: Choose Python. Its ecosystem is unmatched for testing ideas, analysing data, and learning the fundamentals.
Web & Applications: Choose Python or TypeScript. Both are strong options for building modern applications that leverage AI models.
Large-Scale Production: Prototype in Python, but consider deploying in your existing system language (such as C# or Java) for optimal performance and stability.
