Often the question of which model provider to use has already been decided for you. For example, if your organization works with Azure, Google Cloud, AWS, or IBM, that's usually the provider you start with.

Whether you already have a provider or not, hosting effects things like data security and control, so it's worth understanding the pros and cons of different options. Let's explore them now.

There are three main hosting approaches. First, there are models that run within platforms like Microsoft, Google, or Databricks.

Then there are models hosted on local servers, either on premises or in a private cloud. And finally, we have cloud APIs.

A cloud API for LLMs lets you send prompts to the model and get responses back without hosting or managing the model yourself.

Now that the key options are outlined, we can focus on each of them in greater detail. Let's start with platform hosting.

This is often the simplest option. If your organization uses Azure or Google Cloud, their models integrate smoothly with existing systems.

The benefit is trust: company-approved platforms keep data secure and compliant. The trade-off? Strong integration but less flexibility as choices are usually set by policy.

If compliance and integration matter most to you, platform hosting means providers like Microsoft or Google are going to be strong candidates for your organization.

Next, local hosting. This gives you maximum control when it comes to privacy. It suits organizations handling sensitive data such as health records or financial history.

You can freeze versions for stability and avoid disruptive updates.

Local hosting is also an option when there's low or no connectivity. But what's the downside?

Well, that comes down to things like high infrastructure costs and the need for technical expertise.

Some organizations may also choose smaller models for specific tasks to reduce complexity.

Finally, there's cloud APIs. As the name suggests, these APIs access models on the cloud and can therefore offer more flexibility and portability.

They let you experiment, integrate quickly, and avoid heavy upfront investment.

APIs have made generative AI widely accessible, but your data leaves your environment, costs can rise over time, and you depend on network connectivity.

Nevertheless, if agility and fast experimentation are priorities, providers with strong API access are going to stand out.

Companies should bear in mind that, where available, they need to create a corporate account to ensure the LLMS are safe from things like retraining.

Remember, hosting isn't just technical, it's strategic. It affects security, compliance, cost, and control.

When you evaluate AI providers, don't just look at model performance. Ask yourself how hosting aligns with your organization’s priorities and which option gives you the right balance, flexibility, and integration.

Criteria 1: How are models hosted

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