Local (on-prem) AI deployment
We run a language model on your organization’s own infrastructure, so your data stays in-house both during the rollout and in production.
When you need it
When running it locally pays off
A local deployment makes sense where sensitive data or a closed network rules out a public AI service, or where the organization needs to keep control of how the model is used in-house.
Sensitive data
Many organizations cannot send sensitive data to a public AI provider. The reason may be regulatory or commercial, and often it is simply a matter of trust.
Closed network
The model can also run on a fully isolated (air-gapped) network with no internet connection.
Your own logs
Logs are written in the organization’s own systems, so auditability stays in your hands.
The process
How a local deployment comes together
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Use case
We review what the model is needed for, what data it will work with, what the existing infrastructure already offers, and what response time, accuracy, and availability the organization expects.
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Model and hardware
We pick the open-weight model that fits the use case and adapt it to the hardware through quantization. We size the GPU server to match, as a workstation-class or server-class machine, and on request we procure and install it.
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Deployment, access, and logging
We install the model and the software that serves it in the organization’s data center or server room. We configure who can access it, and we log who asked what and when, and what answer they got.
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Interface or integration
Staff reach the model through a browser-based interface, or we provide an API gateway that developers can use to connect it to existing systems.
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Handover
We document how monitoring and alerting work, what to do in an incident, and how updates are carried out.
Deliverables
What you get at the end
A language model ready for use on your own server, and the documentation needed to operate it.
items handed over
- Model and hardware selection for the use case
- Deployment with access management and logging
- User interface or integration
- Operations documentation
Related services
What pairs well with a local deployment
A running model often gets a custom application built around it, and its security needs checking too. It also helps to map which AI tools staff are already using.
Secure AI solution development
An internal knowledge base search, integration with existing systems, or an AI agent, designed with permission management and logging.
Security review of LLM and AI agent infrastructure
Architecture and configuration review of language models, AI agents, and RAG systems based on the OWASP LLM Top 10.
Shadow AI assessment with an AI usage policy
We find out which AI tools staff use and with what data, then write a usage policy based on what we find.
Frequently asked questions
The questions we hear most
Is a locally run model good enough for business tasks?
Open-weight models are good enough for most business tasks, such as summarization, search, classification, and document processing.
Does a local deployment remove every AI risk?
No. Prompt injection and permission issues remain wherever the model is deployed. We covered them in detail in our article on the security risks of enterprise AI adoption.
How is a system cut off from the internet updated?
In an air-gapped environment we follow a separate installation and update process. We document it together with the security boundaries and hand it over.
Who runs the system after handover?
Your own team, using the documentation we hand over. Ongoing operation after the rollout can be arranged under a separate agreement.
