Model Licensing and Usage Rights

Imagine a small product team that wants its application to produce text from a language model. They have two broad routes. They can send requests to a model that someone else operates and pay for each use, or they can take a model and run it on infrastructure they control or rent. Both routes can produce useful answers. They carry different consequences, though, for who sees the data, who decides what the model may be used for, and what the bill looks like over time.

This article answers a practical question: when a team picks hosting or an API, which considerations should drive the decision? The answer depends less on technical convenience than on three questions that are easy to overlook at the start of a project. Where did the model's knowledge come from? What does the license allow? Which parties is the data allowed to reach?

The two routes differ in where the model runs and therefore where your requests travel. When you host a model, your application sends its requests to a model running on infrastructure that you operate or rent. When you use an API, your application sends its requests to an outside service, and the provider's model handles them. The diagram below traces both paths so you can see which systems receive your data and which party controls each step.

The distinction is not always clean. A model can be hosted on a large cloud platform and still be reached through an interface that looks like an API. The source also notes that certain models are reachable only through particular providers at a given time. As of the source's writing, GPT-4 models could be accessed only through OpenAI or Azure. A team's options are therefore shaped by which models are actually offered through which channels, not only by its general preference.

The source suggests thinking about the decision as a sequence of gates rather than a single preference. Each gate narrows the options, and a team moves through them in order. The diagram sequence described in the source reads from top to bottom, with a yes leading to hosting on a trusted cloud and a no pushing the team toward self-hosting or another provider.

A model's license is the set of terms and conditions under which the model can be used. It is tempting to read a license as a complete statement of permissions. The source warns that a license may restrict use in ways a quick reading misses, so open or accessible does not automatically mean unrestricted.

The clearest illustration in the source is a hypothetical model called Model X. Model X's own license may allow its outputs to be used to improve other models. But Model X was trained on ChatGPT outputs, so ChatGPT's terms of use also apply to the chain. If those terms do not permit this kind of use, then Model X has violated them, and the source concludes that Model X cannot be used. The license on Model X can be permissive and the model can still be unusable.

The source describes a related pattern in the open source world. It compares Model X's situation to the Elastic License, which forbids companies from offering the open source version of Elastic as a hosted service that competes with the Elasticsearch platform. A license can allow access to source material while still limiting who may commercially offer it.

Data privacy concerns the protection of sensitive information. In the hosting-versus-API decision, the central question is which parties will receive that information. Every request sent to an outside service crosses a boundary of trust, and a team's privacy policy often reduces to a list of organizations it is willing to trust with its data.

The source makes a specific observation. Some companies with strict data privacy requirements have said that they usually cannot send data to third-party services, yet they are comfortable sending data to models hosted on GCP, AWS, and Azure. For these organizations, the policy depends on which services they can trust. They trust large cloud providers but do not trust other startups.

Regulation adds another dimension. The source notes that regulations are evolving around the world and that requirements for auditable information about models and training data may increase. In that environment, commercial models may be able to provide certifications, which can spare a company the effort of producing that evidence itself. A hosting decision and an API decision can therefore differ in how much audit work falls on the team.

Many users want models to be open source because openness means more information and more options. The source then asks the harder question: what does the developer gain? Building a model can require very large investment, so a developer who releases it openly needs some reason to do so.

Per-use pricing from an API is the other side of the cost picture. An API bill grows as the team sends more requests. Hosting shifts the bill toward the infrastructure the team operates or rents. The source does not give price figures, so the useful comparison is structural: one route charges per use, and the other route ties cost to the infrastructure behind the model.

The source observes that the companies hit hardest by API costs are probably not the biggest companies. Large organizations may be important enough to service providers that they can negotiate favorable terms. Smaller companies usually have less bargaining power and feel the full weight of the usage bill. The source connects this to a broader habit in software infrastructure, where teams often choose the most popular tools the community has tested extensively. The lesson is that cost is not uniform. A route that looks reasonable for a large company with negotiating leverage can be a heavy burden for a small team.