Private LLM: how to run a self-hosted or on-premise LLM on your own data

What is a private LLM?

A private LLM is a language model your company runs where you decide: inside your own cloud account or on your own hardware. Your prompts, documents and outputs stay inside that boundary, and nothing is sent to a shared public endpoint. The model can be an open-weight model you host yourself, or a managed model served inside your own cloud account.

Self-hosted LLM vs API: cost and control

A public API is the fastest way to start: no servers, pay per token, the newest models. A self-hosted LLM costs more to set up and run, but you control where data goes, which model version runs and how much each request costs at volume.

• Choose an API when volume is low or spiky, data is not sensitive, and you want the strongest general model.
• Choose self-hosted when data cannot leave your environment, volume is steady and high, or you need a fixed model version for audit.
• Many teams mix the two: a private model for sensitive steps and an API for the rest.

On-premise LLM vs your own cloud

An on-premise LLM runs on servers you own, which suits teams with strict data residency rules or existing GPU hardware. Running in your own cloud account (AWS, Azure or GCP) gives the same data boundary with less hardware to manage, private networking and the option to use managed models inside that account. Most teams start in their cloud and move on-premise only when a rule requires it.

Model options for a private LLM

• Open-weight models you host yourself, such as Llama, Mistral, Qwen, Gemma or Phi, sized to the task and your hardware
• Managed models served inside your own cloud account, such as Claude on AWS Bedrock, OpenAI models on Azure, or Gemini on Google Cloud Vertex AI
• Small fine-tuned models for one narrow job, like classifying email, which can be cheaper to run than a large general model on that job

We pick by task, data rules and cost per request, and we test candidates on your own examples before choosing.

Security and data: what stays private

• Private networking: the model is reached over private endpoints inside your VPC, not the public internet
• Your keys and accounts: model and cloud costs are billed to your account, and access runs through your identity provider
• Redaction before inference where needed, for example removing personal identifiers from documents first
• Logging and audit trails on every request, with observability that stays in your environment

We do not make compliance claims on your behalf; we build to the controls your security team sets.

How Apex36 sets up a private LLM

1. 1Use case and data review: what the model must do, what data it touches and which rules apply.
2. 2Model and hosting choice, with a cost model per request and per month.
3. 3Deployment in your account with infrastructure as code (AWS CDK or your tool) and private networking.
4. 4Evaluations on your examples, guardrails and LLM observability before launch.
5. 5Handover: runbook, dashboards and code your team owns.

Private LLM pricing

A private LLM deployment starts at $8,000. That covers one use case, one model setup in your cloud account and an evaluation on your data. More use cases, on-premise hardware, fine-tuning or high-availability setups move the price up. Model and cloud costs are billed to your own accounts. You get a fixed quote in writing after the data review.