Private AI, every deployment mode
Private AI means running LLMs, RAG, agents, and assistants in an environment you control — so your data never leaves your perimeter and never trains someone else’s model. These guides cover every combination of deployment mode (on-premises, self-hosted, air-gapped, sovereign, private) and AI capability, with the architecture, compliance drivers, and TCO math for each.
On-Premises AI
For infrastructure and platform leaders who own data centers and procurement.
On-Premises LLM
The on-premises LLM conversation has flipped: open-weight models now match cloud flagships on most enterprise tasks, and GPU serving stacks like vLLM are boring, stable infrastructure.
On-Premises RAG
RAG is the one AI workload where on-premises is often *easier* than cloud: your documents are already inside the firewall, so indexing them locally avoids the exfiltration review that kills cloud RAG projects.
On-Premises Enterprise Chatbot
An on-premises chatbot succeeds or fails on adoption: if it is slower or dumber than ChatGPT, employees quietly go back to the public tool and your data leaves anyway.
On-Premises AI Code Assistant
Source code is the asset engineering leaders least want in a vendor cloud — it is the product itself.
On-Premises Copilot
Most copilot discussions assume the vendor’s cloud is a given; on-premises copilots reject that premise.
On-Premises AI Governance
There is a quiet irony in running your AI governance evidence — the audit logs proving your AI is controlled — on someone else’s cloud.
On-Premises AI Agent Platform
The category-defining guide — read the full pillar article.
Self-Hosted AI
For technical evaluators and platform engineers who want deployment control without vendor lock-in.
Self-Hosted AI Agent Platform
The self-hosted agent stack question is really a build-vs-operate question: LangChain-class frameworks give you parts, not a platform — no registry, no approvals, no audit.
Self-Hosted LLM
Self-hosting LLMs went mainstream through Ollama on laptops; the enterprise version is the same idea with different failure modes: concurrency, VRAM budgeting, model governance, and someone on call.
Self-Hosted RAG
Every component of a RAG pipeline has an excellent open-source option — embedders, vector stores, rerankers — which is exactly why self-hosted RAG projects sprawl.
Self-Hosted Enterprise Chatbot
A self-hosted chatbot is the highest-leverage first move in a controlled-AI program: one deployment ends the shadow-AI problem for every department at once.
Self-Hosted AI Code Assistant
Developers will route around any code assistant that feels worse than Copilot — so a self-hosted code assistant lives or dies on latency and model quality, not policy.
Self-Hosted Copilot
A self-hosted copilot inverts the suite-vendor model: instead of AI bolted to one vendor’s office tools, it is an assistant layer you deploy across whatever stack you actually run.
Air-Gapped AI
For defense, intelligence, critical-infrastructure and classified-environment teams.
Air-Gapped AI Agent Platform
Agent platforms are the hardest AI workload to air-gap: agents want tools, tools want networks.
Air-Gapped LLM
Running LLMs air-gapped is now routine defense practice: weights arrive as signed bundles, inference runs on enclave GPUs, and nothing ever calls out.
Air-Gapped RAG
Air-gapped RAG is the killer app of classified AI: enclaves hold enormous document corpora that keyword search barely penetrates.
Air-Gapped Enterprise Chatbot
Personnel in classified and OT environments do the same drafting, summarizing, and searching as everyone else — with no AI allowed.
Air-Gapped AI Code Assistant
Classified software programs write and maintain enormous codebases with zero access to Copilot-class tools — a growing productivity gap against unclassified peers.
Sovereign AI
For European and public-sector leaders accountable for jurisdictional control of data and AI.
Sovereign AI Agent Platform
Agent platforms concentrate operational knowledge — workflows, decisions, approvals — which is why sovereignty matters more for them than for a lone model endpoint.
Sovereign LLM
Sovereign LLM strategy is converging on a clear architecture: open-weight models you possess, served in-country, wrapped in routing and evaluation you operate.
Sovereign RAG
The corpus a RAG system indexes is often the crown jewels — legislation drafts, citizen records, supervisory correspondence.
Sovereign AI Governance
EU AI Act enforcement makes governance evidence a regulated artifact in its own right — and evidence held on foreign-controlled infrastructure inherits foreign legal exposure.
Private AI
For security and data-protection leaders who need AI without exposing company data.
Private AI Agent Platform
Agents amplify the privacy problem chatbots created: they do not just read your data, they act on it across systems.
Private LLM
"Private LLM" is the query of a buyer who has decided the data question matters more than the model question — correctly.
Private Enterprise Chatbot
The private chatbot is the direct answer to the most common AI incident of this decade: employees pasting confidential material into public tools.
Private AI Code Assistant
For software companies, source code privacy is not a compliance checkbox — the codebase is the company.
Private Copilot
Suite copilots see everything — mail, documents, chat — which makes them the largest single privacy grant most enterprises have ever given a vendor.
Private RAG
The category-defining guide — read the full pillar article.
Choosing a deployment mode
What is private AI?
Private AI is the practice of running AI systems — LLMs, RAG, agents, chatbots, code assistants — in an environment you control, so prompts, documents, and outputs never leave your perimeter and never train third-party models. It spans on-premises, self-hosted, sovereign, and air-gapped deployment modes.
What is the difference between on-premises, self-hosted, sovereign, and air-gapped AI?
On-premises means your own data center and hardware. Self-hosted means your team operates the stack wherever you choose (including private cloud). Sovereign adds jurisdictional control — in-country hosting free of foreign legal reach such as the US CLOUD Act. Air-gapped is the strictest: no connection to the public internet at all, with updates moved by controlled offline transfer.
Which deployment mode should we start with?
Most enterprises start private (single-tenant or private cloud) to stop shadow AI quickly, then move to on-premises as volume justifies hardware. Sovereign is the target when a regulator or ministry requires jurisdictional control; air-gapped applies to classified and OT networks. The same VDF AI platform runs in all four, so the choice is not a migration trap.
Does private AI cost more than cloud AI?
At low volume, cloud AI is cheaper; at steady enterprise volume the economics invert. Fixed infrastructure replaces per-seat and per-token meters, and LLM routing cuts model costs 40–60% — typical hardware payback lands within 9–18 months for heavy workloads.
Plan your on-prem AI deployment
Book an architecture call and we will scope a private, on-prem AI deployment for your environment — integrations, hardware, and governance included.