A private, self-hosted LLM in your own cloud or on your own servers
Many companies want the benefits of large language models without sending sensitive data to a third party. DoneDeploy builds private AI infrastructure: self-hosted open models such as Llama, Mistral and Qwen served with vLLM or Ollama on Kubernetes GPUs, in your own cloud account or on-premises. A single gateway gives your teams one way in to private and hosted models, with retrieval on your own documents, access control and monitoring built in.
From design to implementation, and everything after:
What goes wrong without it
Data you cannot send out
Contracts, customer data or regulation rule out sending some information to public AI services.
Every team wiring up its own AI
Different teams use different models and keys, with no shared view of access, usage or cost.
Answers that ignore your knowledge
General models do not know your documents, products or procedures, so their answers miss what matters.
What we do
- Choose open models such as Llama, Mistral, Qwen, DeepSeek or Gemma for your tasks and hardware
- Serve them with vLLM, TGI or Ollama on Kubernetes with NVIDIA GPUs, in your cloud or on-premises
- Set up a gateway that routes requests to private models or to hosted models such as Claude or GPT where policy allows
- Build RAG on your own documents and data with pgvector
- Add access control, audit logs, rate limits and per-team usage tracking
- Monitor latency, errors, GPU use and cost with OpenTelemetry and Grafana
What you get
- Model serving and gateway infrastructure as code in your Git
- A document of model choices, data flows and who can access what
- Dashboards for usage, latency, GPU use and cost
- Runbooks for model updates, scaling and recovery
From first call to continuous improvement
Free first consultation
We learn what you want private AI for, which data is involved and where it must stay.
Design
We compare models, hardware and hosting options against your quality, cost and data needs.
Fixed-price proposal
We agree the architecture, the models to start with and the price.
Build
Engineers set up serving, the gateway, retrieval and monitoring, and test with your real cases.
Operate and improve
We review quality, capacity and new open models over time and propose changes.
Llama
Mistral
Qwen
vLLM
Ollama
Kubernetes
NVIDIA GPUs
pgvectorRelated articles
From Idea to AI Feature in Six WeeksHow an AI project runs end to end in six weeks: discovery, design, prototype, infrastructure, rollout, then measuring and improving.
CI/CD for Models and PromptsEvaluation gates, model registries, canary releases and rollback: applying CI/CD discipline to prompts and models in LLM applications.
Fine-Tuning or Prompting? A Decision Guide for Small TeamsWhen prompting, RAG or fine-tuning is the right way to adapt a language model, and what each one costs a small engineering team.Frequently asked
What is a private or self-hosted LLM?
It is an open model that runs on infrastructure you control, in your own cloud account or data centre, so prompts and documents do not leave your environment.
Do we need our own GPUs?
Not necessarily. GPUs can be rented in your cloud account, and smaller models can run on modest hardware. We size the setup to your workload.
Can we still use hosted models like Claude or GPT?
Yes. A gateway can send each request to a private or hosted model according to rules you set, for example keeping sensitive data on private models only.
Tell us what you run and what you want to change. We reply personally, and the first consultation is free.
Let's talk »
