Self-Hosted LLMs: Running Your Own Models On-Premises
When running an open-weight model on your own GPUs beats a cloud API, what it takes operationally, and when it is honestly overkill.
What is a self-hosted LLM?
A self-hosted LLM is an open-weight model, like Llama or Qwen, running on GPUs you control instead of behind a vendor's API. Your AI assistant sends prompts to your own hardware, gets answers from your own hardware, and no third party sees any of it.
It is the deepest form of model sovereignty, and also the most operational work. Which is why the honest first question is not "how do we self-host?" but "do we need to?"
When does self-hosting make sense?
Three triggers cover most real cases. If none of them apply to you, a cloud model endpoint in an EU region is probably the better deal: less work, broader model choice, no hardware to size.
Prompts may not leave the network
Some environments rule out external model APIs entirely: classified work, defence supply chains, legal holds. A self-hosted model is the only configuration where a prompt never touches infrastructure you do not own.
No non-EU operator in the chain
A cloud model endpoint in an EU region still has an operator behind it. If your mandate says the whole chain sits under EU jurisdiction, a model on your own hardware closes the last gap.
Predictable cost at high volume
Cloud APIs meter per token, which is great at low volume and painful at sustained high volume. Self-hosting flips that: fixed capacity cost, no meter. The crossover point depends on your usage, and it is worth calculating before you commit either way.
What do you need to run one?
Four things. None of them exotic, all of them real.
GPU capacity
Open-weight models need GPUs, either in your data centre or rented from a European provider. Sizing them to your workload is the step people underestimate most, in both directions.
A serving layer
The model runs behind an OpenAI-compatible endpoint, using serving software like vLLM. To the assistant platform, your own model then looks exactly like any cloud provider.
A model update rhythm
Open-weight models improve fast. New versions arrive as releases you pull in and evaluate on your own schedule, which is more work than a cloud provider upgrading behind the scenes, and more control.
Someone who owns it
Self-hosting is an operational commitment, not an install step. Monitoring, capacity, upgrades. Plainsight can run this layer for you, but somebody has to.
Are open-weight models good enough?
For the work an internal assistant actually does, yes. Most assistant answers are grounded in your own documents through retrieval, and in that setup the quality of the retrieval matters more than the raw size of the model. An open-weight model with good retrieval beats a frontier model with bad retrieval, every day of the week.
And you do not have to choose once for everyone. The platform assigns models per department, so legal can run on a self-hosted model inside the network while marketing uses a cloud model with web search. Same assistant, different brains, each matched to the sensitivity of the work.
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