Private LLMDeployment
Keep prompts and documents inside your own environment by running an open model you control, with the sizing, security and quality evidence to justify the choice.
Private LLM Deployment: what the work involves
Some organisations cannot paste client files, patient notes or financial records into a public chatbot, and many have been told by legal or a regulator that data must stay in the building or in the country. Staff use consumer tools anyway, because the sanctioned option does not exist. The result is either a ban that people ignore or an uncontrolled leak, and the leadership has no clear picture of which risk is worse.
DevKey designs and deploys a private model service as an engineering project. We start with requirements: data classes, concurrent users, languages, latency and which tasks the model must perform. We shortlist open-weight models such as Llama or Mistral, benchmark them on your own tasks, and size the GPU hardware accordingly. The model is served behind an authenticated gateway with logging, rate limits and retrieval connectors, and we document the update, backup and monitoring procedures your team will inherit.
Core features
Requirements and risk workshop
We map data sensitivity, user numbers, tasks and compliance constraints, then write down what the private model must do and what it need not.
Model selection by benchmark
Candidate open models are tested on your real prompts and documents, with quality, speed and memory use compared side by side.
Hardware and capacity sizing
GPU memory, throughput and concurrency are estimated from the chosen model and quantisation, including options that fit a modest budget.
Secure serving gateway
An authenticated API with per-team keys, rate limits, audit logs and prompt-level data controls sits in front of the inference engine.
Retrieval over internal data
Documents are indexed in a store inside your network, with permission-aware retrieval so the model sees only what the user may see.
Operations runbook
Updates, model swaps, backups, monitoring and incident steps are documented, and your administrators are walked through them.
What we get right before launch
Quality gap versus frontier models
Open models have improved a great deal but may trail the best hosted models on complex reasoning and Urdu. We quantify the gap on your tasks, and sometimes recommend a hybrid design in which only non-sensitive requests use external APIs.
Hardware, power and staffing
GPUs are costly to buy, cool and keep available, and someone must patch and monitor them. We compare owned servers, rented dedicated GPUs and private cloud, including the people cost, before you commit.
Security does not come free
Running a model on-premises moves the risk; it does not remove it. Access control, network isolation, prompt logging policy and protection from injected instructions in documents all still need design and testing.
Tools and technology
- Llama and Mistral models
- vLLM
- Ollama
- Docker
- Kubernetes
- pgvector
- LlamaIndex
- FastAPI
- Prometheus and Grafana
Common questions, answered
Is a private model as good as ChatGPT?
For many defined tasks such as summarising, extraction and internal question answering, open models are good enough. For complex reasoning or fluent Urdu, hosted models often still lead. We benchmark on your tasks and show the gap.
What hardware do we need?
It depends on model size, user load and latency needs. Smaller quantised models run on a single workstation-class GPU, while many concurrent users need more. We size it from measurements, not guesses.
Can it run fully offline?
Yes. Air-gapped deployments are feasible, though model files, software updates and any retrieval content must be delivered through controlled media or a transfer process, which we plan for.
Which licences apply to open models?
Open-weight does not always mean unrestricted. Licences differ on commercial use, user thresholds and permitted purposes. We review the licence of each candidate against your intended use before selection.
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Ready to start your Private LLM Deployment project?
Tell us what you need and we will come back with a clear scope, timeline and the questions worth answering before any build starts.