You can deploy basic AI features in a shared hosting environment, but there are important limitations to consider. At AEHEA, we don’t recommend shared hosting for anything beyond lightweight AI inference tasks, and even then, only under specific conditions. Shared hosting is designed for static websites or small dynamic applications not for running resource-heavy models, persistent background processes, or anything that needs control over the system environment.

In most shared hosting setups, you don’t get access to the command line, GPU acceleration, or the ability to install Python packages freely. These restrictions make it nearly impossible to deploy frameworks like TensorFlow, PyTorch, or Hugging Face Transformers, which are essential for running most modern AI models. Even if you manage to upload a pre-trained model, you might not be able to run it due to CPU limits, process timeouts, or memory constraints enforced by the host.

However, there is one workaround. If your AI logic lives elsewhere on a cloud API or your own AI server then your shared host can act as a frontend. The site collects user input, sends it to a remote model for processing, and displays the result. This approach works for chatbots, text classifiers, or any prediction system where real-time inference is handled outside of the shared environment. You’re essentially using shared hosting as a relay, not as the computation engine.

At AEHEA, when we encounter shared hosting limitations, we usually recommend transitioning to a low-cost virtual private server or a container-based deployment. These options are still affordable but offer full control over the environment. That control is essential when dealing with model storage, inference speed, and the tools needed to handle AI workloads safely and effectively. Shared hosting can display the output of AI, but it’s rarely suitable for doing the actual work.