Private LLM Deployment
Open-source large language models hosted in your own data centre or cloud account, with document search (RAG) so answers come from your data.
About our private LLM deployment
Many businesses want generative AI but cannot send customer or internal data to a public AI service. A private LLM solves that. Our private LLM deployment service runs open-source models such as Llama and Mistral inside your own environment. Your prompts and documents stay with you. We add document search (RAG), so the model answers from your own policies, manuals and records. Access control and audit logs are built in from day one.
What's included in our private LLM deployment
How we deliver private LLM deployment
We begin with one clear use case, such as an internal help desk or document search. We test a few models against your real questions. Then we pick the one that best balances answer quality, speed and cost. We deploy it with vLLM on your hardware or cloud, connect it to your documents, and secure it with your existing logins. Once it proves its value, we roll it out to more teams.
Tools and technology for private LLM deployment
Outcomes of our private LLM deployment
- Sensitive data stays inside your environment
- Answers grounded in your own documents
- Predictable running costs instead of per-token bills
Frequently asked questions about private LLM deployment
What is a private LLM?
A private LLM is a large language model that runs in your own data centre or cloud account instead of a public AI service. Your prompts, documents and answers stay inside your environment.
What hardware do we need for a private LLM?
It depends on the model size and how many people use it. Smaller models run well on a single GPU server. Larger models, or heavy use, need more GPUs. We size it with you, and our AI infrastructure team can build the servers.
Is a private LLM as good as the big public AI services?
For focused business tasks it is often good enough, especially when it answers from your own documents. For very broad tasks, the largest public models can still be stronger. That is why we test models on your real questions before you commit.
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