Private, on-premise LLM deployment for IT teams
Open-source models hosted on-prem or in your VPC, with access control, audit logging and no per-seat fees. Your IT team keeps the keys.
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This page is for the IT lead, not the office manager. Suppose your organization has decided that client, patient or financial data cannot go to a third-party AI service. On-premise LLM deployment for IT teams is how you give staff a capable assistant anyway. We deploy open-source models on hardware you own or inside your VPC and connect them to your identity provider. Then we hand over an environment your team operates.
The stack is standard. An inference server running quantized open-source models (Llama, Qwen, Mistral, Gemma and others, chosen per workload). An API your internal applications can call. A chat front end for staff. RBAC and SSO against your directory, encryption at rest and in transit, and audit logs for every request. Kubernetes is optional. For many organizations, one or two well-sized GPU nodes with a simpler setup is the right answer. We say which before you buy hardware.
There is no per-seat fee and no vendor lock-in. When a better open-source model ships, you swap it. We document everything and stay on for support only if you want us to.
Who this is for
- IT teams at law firms, medical groups and finance companies with a data-residency requirement that rules out third-party AI services.
- Organizations that want open-source models under their own access controls rather than another SaaS contract.
- Teams that need audit logs of every prompt and response for compliance or internal review.
- Companies with existing on-prem or VPC infrastructure and GPU budget but no time to stand the stack up themselves.
- IT leads who want documentation and a handover, not a managed black box.
What you get
- Architecture and sizing: GPU, memory, storage and network for your models and expected load, on-prem or in your VPC.
- Deployment of open-source models with an inference server, a chat interface for staff and an API for internal applications.
- RBAC and SSO integrated with your identity provider; encryption at rest and in transit; audit logging of prompts and responses.
- Private document search (retrieval) over the shares and systems you approve.
- Runbooks, monitoring and update procedures, with Kubernetes if you want it and a simpler setup if you do not.
- A documented handover to your team, with optional ongoing support scoped in writing.
How it works
- 1Call
On a free 20-minute call with your IT lead we cover data-residency rules, user count, existing infrastructure and the first workloads.
- 2Scope
We write a fixed-price scope with the architecture, security controls, acceptance tests and the delivery date.
- 3Deploy
We stand up the environment on-prem or in your VPC, integrate SSO and logging, and run the acceptance tests with your team.
- 4Hand over
We deliver runbooks and documentation, train your operators, and hand off; ongoing support is optional.
How your data stays private
Everything runs inside your network boundary or your cloud account. Models, prompts, documents and logs stay on infrastructure you control, and nothing is sent to ModelSide or any third party. We work under an NDA. Our access is limited to what you provision for the engagement. It is removed at handover unless you keep us on for support. Every action we take is in the audit log. For healthcare organizations, the same commitment applies. Built to support your HIPAA obligations: patient data is processed on hardware you own, nothing is sent to ModelSide or any cloud, and we sign a Business Associate Agreement.
Questions we hear
Which models do you deploy?
Open-source families such as Llama, Qwen, Mistral and Gemma, quantized where it makes sense, chosen per workload. You are not locked to any of them; swapping a model is a routine update.
Do we need Kubernetes?
No. Many deployments run well on one or two GPU nodes without an orchestrator. The scope names the option that fits your load.
Can we run it in our cloud account instead of on-prem?
Yes. A VPC deployment keeps the same controls: your account, your keys, your logs, and no third-party AI service in the path.
How is this different from the AI workstation?
The workstation is one machine in an office, delivered ready, for teams without IT. Private deployment is a multi-user environment managed with your IT team, with SSO, access control and audit logs. Both follow the same rule: your data never leaves your environment.
Tell us what's slowing your office down
Bring your IT lead to a free 20-minute call and we will sketch the deployment before any scope is written.
Or call 516-423-7714. We reply within one business day.