What does self-hosted AI mean?
Self-hosted AI means running a model's inference yourself, on hardware you control, instead of sending requests to a third party's API. It's a spectrum rather than one setup: fully self-managed on hardware you own, a dedicated machine you rent but administer yourself, or a managed but single-tenant service where a vendor operates the hardware exclusively on your behalf. What all three share is that no other customer's workload runs on the same GPU as yours.
The three points on the spectrum
At one end is hardware you own outright: GPUs in your own server room or a colocation cage, where you handle everything from power and cooling to model updates. This gives full control and, over time, the lowest marginal cost per hour of use, but it requires capital up front and someone on staff who can keep the hardware running. See our comparison of on-premise vs cloud GPUs for startups for when that trade-off pays off.
In the middle is a dedicated rented machine: hardware owned by a provider but allocated entirely to you for as long as you're renting it, with no other tenant's process sharing the GPU. You install and configure the inference engine yourself, but you don't own or maintain the physical box. This is the model GPUwerk runs: a DGX Spark rented at $0.79/hour, with 128GB of unified memory, from EU-Central, where you have full SSH access to install whatever inference stack you want.
At the other end is a managed but single-tenant service: a vendor operates the hardware and often the inference engine too, but the underlying GPU is still dedicated to one customer rather than shared across many. You get less operational burden than fully self-managed, while keeping the hardware-isolation property that a shared multi-tenant API doesn't offer.
What self-hosting is not
Calling an API, even one billed as "private" or offering a dedicated-capacity tier, isn't self-hosting if the underlying compute is still operated end-to-end by the vendor and you never touch the machine. The defining question is whether you (or a vendor acting exclusively on your behalf) control what model runs, how it's configured, and what happens to the requests, not whether you personally rack the server. See our explainer on what is a private LLM for how that distinction plays out with vendor terminology specifically.
What self-hosting requires you to own
Wherever a deployment sits on the spectrum, self-hosting shifts some set of responsibilities from a vendor onto you. At minimum, that means picking a model and an inference engine, keeping the model updated as better versions ship, and monitoring that the service is actually up and responding correctly. Further toward the owned-hardware end, it also means capacity planning, hardware maintenance, and often a person on call for the box itself. None of this is unusual system administration work, but it's real work that a managed API abstracts away entirely, and it's worth being honest about before committing to it.
Why teams choose to self-host
The recurring reasons are cost at sustained volume, control over which model version runs and when it changes, and keeping prompts and outputs off infrastructure operated by a third party you don't control. None of these apply equally to every workload: a team with occasional, bursty usage often does better on a managed API, where someone else absorbs the operational overhead of keeping the model running. Self-hosting tends to make sense once usage is steady enough, or data handling requirements strict enough, that the operational work is worth taking on. A dedicated rented machine is usually the middle ground: no hardware to own or maintain, but full control over the software stack, at $0.79/hour on a GPUwerk Spark.