# Air-Gapped AI: How to Run LLMs Fully On-Premises with Docker, OpenShift, and Kubernetes > A practical guide to deploying LLMs in fully isolated, no-egress environments — container orchestration, model serving, private knowledge bases, and security hardening for government, defense, and finance. **Category:** Guide **Author:** NeuralSeek Team · **Published:** June 18, 2026 **Canonical:** https://neuralseek.ai/ai-grounded/air-gapped-ai-run-llms-on-premises-docker-openshift-kubernetes **Section index:** https://neuralseek.ai/ai-grounded For the organizations with the strictest data requirements — government, defense, intelligence, and regulated finance — the cloud isn't an option. Sensitive data can't leave the building, let alone the network, which rules out every API-only model and most managed platforms. The answer is air-gapped AI: running large language models entirely on your own hardware, inside a perimeter with no outbound connectivity. Very few platforms support it and almost no one has written a clear guide to doing it well. This is that guide — a practical walkthrough of deploying LLMs fully on-premises with Docker, OpenShift, and Kubernetes, covering orchestration, model serving, knowledge base setup, and security hardening. ## What 'air-gapped' actually means Air-gapped means the deployment has no path to the public internet — no model API calls, no telemetry, no package downloads at runtime, no exceptions. Everything the system needs is mirrored inside the perimeter ahead of time: container images, model weights, embeddings, and dependencies. This is stricter than 'on-prem' or 'private cloud'; it's a hard isolation boundary that you can prove to an auditor. The architecture below is built around that constraint from the first layer up, not bolted on afterward. ## Container orchestration: the foundation Start with the platform that schedules and scales your workloads. Docker packages each component into a portable image; Kubernetes (or Red Hat OpenShift, its hardened enterprise distribution) orchestrates those images across your own servers and GPUs. Crucially, you run a private container registry inside the perimeter and mirror every image into it, so pods never pull from an external source. OpenShift adds security-conscious defaults — restricted SCCs, integrated image signing, and built-in policy — that make it a natural fit for regulated environments. ## Model serving: open weights, in-cluster Because you can't call a hosted model, you serve open-weight LLMs yourself. Inference servers like vLLM or Hugging Face's TGI run as in-cluster services with GPU scheduling, batching, and autoscaling. Model weights are loaded from on-prem storage you've pre-staged — never downloaded at runtime. Size the GPU pods to your model and concurrency needs, and expose inference only over the internal cluster network so nothing is reachable from outside the perimeter. ## Knowledge base: retrieval that never leaves An LLM is only as useful as the data it can ground against, so stand up a private retrieval stack: a vector database, an embedding model served in-cluster, and an ingestion pipeline that indexes your documents. Every step — embedding, storage, retrieval — happens inside the perimeter, so sensitive source material is never transmitted anywhere. This is the difference between a generic on-prem chatbot and a system that can actually answer from your classified or regulated knowledge. > In an air-gapped deployment, security isn't a feature you add — it's the boundary every other decision has to respect. ## Security hardening: make it defensible Isolation gets you most of the way; hardening proves it. Lock down east-west traffic with Kubernetes NetworkPolicies so only the components that must talk to each other can. Enforce image provenance with signing and admission control so no unverified image ever runs. Manage secrets through a sealed in-cluster store, not environment variables. And turn on comprehensive audit logging so every inference and configuration change is attributable. The goal is a posture you can hand to an auditor and defend line by line. ## The governance layer most guides skip Running the model is the infrastructure problem; governing it is the harder one — and it doesn't disappear just because you're air-gapped. You still need to stop the model from answering when retrieval is weak, isolate one corpus from another, redact secrets, and log every exchange for review. NeuralSeek deploys fully inside the same perimeter via Docker, OpenShift, or Kubernetes and adds exactly that layer: a minimum confidence floor, corpus isolation, prompt logging, and a built-in bake-off to choose among the open-weight models you've staged — all without a single outbound call. The infrastructure makes on-prem AI possible; the governance makes it safe to actually use. **Govern your on-prem deployment** - [Corp Filter](https://neuralseek.ai/ai-grounded/corp-filter) - [Minimum Confidence %](https://neuralseek.ai/ai-grounded/minimum-confidence-percent) - [Prompt Logging](https://neuralseek.ai/ai-grounded/prompt-logging) - [Built-in LLM bake-off](https://neuralseek.ai/ai-grounded/llm-bake-off) --- From NeuralSeek's AI Grounded — practical, web-verified guidance on building governed, grounded enterprise AI. NeuralSeek is the model-agnostic, governed AI platform you own: any LLM (swap with no rebuild), your data in your own tenant (cloud or on-prem), 118 guardrails enforced before any action, one container that runs anywhere.