Private LLM & Custom AI Development

What is a private LLM?

A private LLM is a large language model that runs on infrastructure you control. The model weights, your prompts, your documents and the logs stay on your own servers, in your own cloud account or on an air-gapped network. Nothing is sent to an outside model provider, and nothing is used to train someone else's model.

LLM.co builds every private LLM on open-weight models such as Llama, Qwen, Mistral and gpt-oss. You hold a copy of the weights, you choose when to upgrade, and the model keeps working if a vendor changes its terms, its prices or its catalog.

Private LLM vs. a local LLM on one workstation

Tools such as Ollama and LM Studio make it easy to download an open-weight model, often from Hugging Face, and run it as a local LLM on a single laptop or workstation. That is a good way to find out whether an open-weight model can handle a task, and nothing you type leaves the machine.

A private LLM for a whole organization needs more than one person's computer: a self-hosted server that many staff can reach, sign-in through your identity provider, permissions that match your source systems, audit logs, monitoring and a plan for model upgrades. LLM.co builds and runs that layer around the same kind of open-weight models you can test locally.

Private LLM development and custom AI, under one team

A private LLM on your own hardware is only useful once it is connected to your data and your workflow. Custom AI development is the work of building that system around one specific job in your business. It reads your records, follows your rules and writes its output into the systems your staff already use, such as the ERP, CRM, claims platform or document management system.

A custom system built on a public API often fails the security review before launch. A private LLM with nothing built on it sits idle. Doing both in one engagement avoids each problem: the system that wins the demo is the same private LLM deployment that clears your security review.

Who needs a private LLM for enterprise data

A private LLM suits organizations whose most useful data is also their most sensitive. That includes law firms and legal departments, healthcare providers, banks and insurers, government and defense contractors, manufacturers, utilities and professional services firms. It also suits any company whose contracts or policies forbid sending client data to a third party.

It makes financial sense once usage is broad and steady. Per-seat and per-token fees grow with every user. A private LLM on dedicated hardware has a fixed cost that does not.

Where a private LLM can run: on-premises, private cloud or air-gapped

The same private LLM deployment ships to three places. On-premises, it runs on GPU servers in your own data center. In your cloud account, it runs on dedicated GPUs in AWS, Azure or GCP under your keys and network rules. Air-gapped, it runs with no internet route at all and takes updates on signed media.

Every private LLM we deploy ships with single sign-on, document-level permissions and audit logging to your SIEM before wide release, so private AI fits the controls your security team already runs.

Open-source LLMs and the stack behind a private LLM

The open-weight models often called open-source LLMs, such as Llama, Qwen, Mistral and gpt-oss, are the base of every private LLM we deploy. The weights are files on your storage, so your team can inspect them, pin a version and keep running it for as long as it serves the work.

Each self-hosted LLM is served with vLLM on dedicated GPUs. A gateway in front of the models exposes one OpenAI-compatible API with keys, quotas, routing and audit logging, so existing tools can switch from a public API with little change. Quantization often lets a model fit on fewer or smaller GPUs with no meaningful change in evaluation scores.

Private LLM security and data privacy

An enterprise AI subscription with a no-training clause still sends each request to a vendor's servers, under the vendor's retention policy and release schedule. With a private LLM, requests stay on your network, and the model changes only when your team approves the change.

Access runs through single sign-on with Okta or Microsoft Entra ID, with role- and document-level permissions. Each prompt, retrieved passage, answer and action is logged to your SIEM. The controls are designed to support the SOC 2, HIPAA, GLBA or CMMC program you already run, and we provide the architecture and data-flow documentation your reviewers ask for.

How a private LLM deployment works with LLM.co

Engagements start with a two-week discovery sprint to rank use cases, check the data and model the cost of a private LLM against your current API spend. A focused first system usually reaches production in eight to twelve weeks. Every phase ends with a go / no-go decision, and you own the code, prompts, evaluation sets and any trained weights.