What Is an Open-Source Model?

An open-source model is an AI model whose weights, and often its code, are publicly available to download, run, modify, and build on. This lets anyone use the model on their own hardware and adapt it, in contrast to a closed model accessible only through a provider's service.

How an open-source model works

An open-source model is released with its trained weights made public, so anyone can download it and run it themselves rather than calling a provider's API. From there it can be hosted privately, fine-tuned on your own data, or modified, giving full control over how and where it runs.

The term is used loosely. Many "open" models release the weights but not the training data or full training code, which is why "open-weight" is often the more accurate label. True open source, with everything needed to reproduce the model, is less common. Either way, the defining feature is that you hold and run the model yourself.

Open-source vs closed-source models

The difference is access and control. An open-source model can be downloaded and run yourself; a closed-source model is used through a provider's service, with the weights kept private.

Why open-source models matter for AI

Open-source models matter because they give teams an alternative to depending on a single provider. Running a model yourself means more control over privacy, cost, and customization, and no risk of a service changing or disappearing under you, though you take on hosting and maintenance in return. The gap in capability between the best open and closed models has narrowed, making open models a practical choice for many uses. At Custom AI Studio, we choose open or closed models to fit a client's needs, and build systems the client owns and controls either way.