Why Python Is the Language of AI.

What is Python?

Python is a widely used programming language known for its readability. The code resembles natural language, with minimal frills, allowing developers to work faster and spot errors more easily. Python is used for a wide range of tasks: web applications, automation, data processing, and, increasingly, AI and machine learning. It is precisely this versatility, combined with the vast number of available libraries, that makes the language popular, from simple scripts to complex AI systems.

Why Python and AI are often mentioned together.

Python wasn’t designed for AI, but it has become the standard for it. The reason is practical. AI research revolves around experimentation: quickly testing an idea, adjusting the model, and testing it again. Python lends itself perfectly to this, because the code is close to natural language and requires little fuss. A researcher or developer can focus on the problem, not on the quirks of the language. When the major AI libraries were subsequently built in and around Python, this created a self-reinforcing cycle: more libraries attracted more users, and more users attracted more libraries.

What role does Python play in AI and machine learning?

Python is the glue that connects an idea to a working model. It brings together data processing, model training, and application in a single environment.

Libraries that make a difference.

Python's strength in AI lies largely in its libraries. TensorFlow and PyTorch are the two most commonly used frameworks for building and training neural networks, the foundation of modern AI. scikit-learn focuses on classical machine learning, such as prediction and classification. pandas and NumPy process and structure the data used to train a model. Together, they form a complete toolkit, so a developer doesn’t have to start from scratch.

From prototype to production.

Building an AI model is one thing; getting it to run reliably in a real-world application is another. Python makes the transition from experiment to product manageable, because the same model you build in research can also run in production with the right approach. However, there’s more to it than just the model itself: monitoring, security, and integrations with existing systems determine whether an AI application holds up in practice.

Python, frameworks en AI-services.

To expose an AI model, you often use a Python framework. FastAPI is a popular choice here: a modern framework focused on fast APIs, with automatic documentation and support for asynchronous processing. This allows you to make an AI model accessible as a service that other systems can send requests to. A front-end or a back-end in another language—such as PHP and Laravel, which Cube uses to build web applications—can then communicate with the AI model via that API. This way, your entire application doesn’t have to run in Python; you use Python exactly where AI or heavy data processing is needed.

Python vs. PHP: When Should You Use Which One?

It’s not a matter of the best language, but of the right language for each task. PHP and Laravel are ideal for web applications, portals, and platforms: proven, maintainable, and built for the web. Cube uses these to build the lion’s share of its custom software. Python comes into play as soon as data, machine learning, or AI is at the heart of the problem. In practice, they work together: the web application in PHP, the AI component in Python, linked via an API. The user doesn’t notice any of this; they see a single, fully functional product. You can read more about the difference from back-end development in a separate article.

Python and AI in Custom Software Development.

Adding AI to software is rarely a matter of simply plugging in a model and calling it a day. The value lies in integrating it with your own data and processes, and that’s precisely where things often go wrong if the foundation isn’t right. Cube uses AI as a tool on top of software that’s already running in production, not as a standalone experiment. A Data Management Platform serves as the infrastructure to which AI can connect: it is not an AI tool itself, but the layer that makes your data accessible so that an AI application can work with it securely and purposefully. For organizations where data is not allowed to leave their own environment, Cube runs open-source AI models that are self-hosted on infrastructure within the EU.