Self-hosted vs. cloud LLMs: What's the difference?

The difference, in a nutshell.

With a cloud-based LLM, you send your question to the provider’s servers and receive the answer back. You don’t have to manage anything, and you have immediate access to the most powerful language models. With a self-hosted LLM, the model runs on hardware that you manage, within your own network. Your data never leaves your environment, but you’re responsible for the infrastructure and maintenance. It's not a matter of right or wrong. It's a trade-off between convenience and power on the one hand, and control and ownership on the other.

Benefits of the cloud.

Cloud models are the de facto standard, and for good reason. You can get started within a day, pay as you go, and automatically get the latest, most powerful models. No server management, no need to buy GPUs, and no scaling issues. For most organizations, this is the fastest route to working AI. A common concern you often hear is data. It’s a valid point to consider, but with enterprise versions of cloud-based LLMs, it’s contractually stipulated that your data won’t be used to train the model. Provided it’s properly configured, the cloud is therefore a perfectly acceptable solution for many applications.

Advantages of self-hosted.

Self-hosted solutions offer greater control. Your data remains within your own environment, you aren’t dependent on a vendor’s uptime or pricing, and you can control what happens right down to the model level. For organizations with strict privacy requirements, sensitive data, or heavy compliance obligations, this is sometimes the deciding factor. On the other hand, self-hosted solutions are more expensive and more complex to set up and maintain. You need computing power and in-house expertise, and the open-source models you can run yourself often lag slightly behind the very latest cloud models.

When do you make a choice?

The rule of thumb: start with the cloud, unless there’s a compelling reason to go self-hosted. That reason is usually compliance or the sensitivity of the data, not a general feeling that it’s “safer.” What’s secure depends less on where the model runs and more on how you manage access and logging, and for that, governance via MCP is more important than the hosting choice itself. Our own production environment runs primarily in the cloud, with self-hosted as an option when requirements call for it. That honesty is part of who we are: we don’t push a self-hosted solution if the cloud is sufficient, and vice versa.