Closed AI Vs Open AI: What’s Really Behind The Kitchen Door?

Picture two restaurants. One hands you a finished dish and keeps the recipe locked in the kitchen; the other gives you the ingredients and method, letting you make changes yourself. Neither approach is exactly better, but they give diners very different levels of control. AI models work along a similar divide, and most people using them never need to think about how much of the technology behind the interface they can actually access
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The industry uses terms such as closed AI, open-weight AI and open-source AI to describe these differences. They can sound interchangeable, but they aren’t – once you know the difference, it becomes a lot easier to understand what users can access, change and control.

The Locked Kitchen: What Is Closed AI?

In a closed setup, the company behind the model keeps control over the underlying technology. Users usually interact with it through a product or application programming interface (API), which allows different software applications to communicate with each other, rather than accessing the model’s trained weights. Stanford HAI describes closed-source software as technology whose underlying code isn’t available for the public to view, modify or use.

The provider keeps the model under its control and decides how people can access and use it. So basically; you get the finished dish, but not the recipe or access to the kitchen.

The Open Kitchen Has A Few More Doors

On the open side, two terms are easy to mix up: open-weight and open-source. IBM explains that model weights are the learned parameters that determine how a trained model interprets new data and makes predictions. When those weights are public, developers can download and run the model themselves.
Open-source AI goes a step further. Under the Open Source Initiative’s (OSI) Open Source AI Definition, users must be able to use, study, modify and share the system, with access to the components needed to make those changes.

So, having the weights doesn’t mean someone has access to everything behind the model. The weights might be available to download, while the training data or code used to create them remains private.

Where Do The Two Kitchens Differ?

What developers can actually access varies between the two. With a closed model, the provider keeps the cooking process under its control and gives users access to the finished system. An open-weight model can give developers access to the trained weights, giving them more choice over where the model runs and how they adapt it.

Customisation depends on the model and its licence – some closed models offer provider-supported changes, while open-weight models can allow developers to modify or fine-tune them when the licence and technical setup permit it.

Open-source AI also gives developers more information about how the model was built. The weights reveal more about the finished model, but not necessarily the training data or code used to produce them.

Why Let Someone Else Run The Kitchen?

Closed AI can suit businesses that want to use an AI model without taking responsibility for running the underlying system themselves. The provider handles the infrastructure and makes the model available through its products or APIs, which can reduce the technical work a business needs to take on.

That doesn’t exactly make a closed model safer or more accurate – it means more of the technical control and infrastructure sit with the provider rather than the business using the model.

Why Keep The Ingredients?

Open-weight approaches can give businesses more control over where a model runs and how it is adapted. Depending on the model and its licence, developers may be able to fine-tune it, run it on infrastructure they control or integrate it into their own systems.

OpenAI’s gpt-oss models are a current example. OpenAI describes gpt-oss-120b and gpt-oss-20b as open-weight reasoning models that can run on infrastructure controlled by the user or through hosting providers. They can also be fine-tuned using open tools, while the Apache 2.0 licence allows broad use, modification and redistribution subject to the models’ usage policy.

Which Kitchen Makes Sense For A Business?

The choice comes down to what a business actually needs from its AI system. A company that wants something ready to use without managing the underlying infrastructure may prefer a provider-managed model, while one that needs more control over deployment or customisation may look more at an open-weight approach.

Businesses also need to check what has actually been made available, where the model can run, what can be changed and what the licence allows.

Who Controls The Kitchen?

Closed AI keeps more control with the provider, while open-weight approaches can give developers greater control over how a model is accessed, adapted and deployed. Open-source AI takes this further by setting requirements around the freedoms and components needed to study, modify and share a system.

In other words, it’s less about whether an AI model is simply “open” or “closed”. It’s about how much of the recipe you actually get, what you’re allowed to do with it and who remains in control of the kitchen.