The Short Answer
Private AI means running AI models on infrastructure your organization controls — your own cloud, on-premise, or air-gapped — so prompts, outputs, and training data never leave your environment. The strongest version goes past private to owned: on ibl.ai you own all the code and the data, run it model-agnostic across any LLM, and pay with no per-seat pricing.
Public AI (ChatGPT, Gemini, Copilot) processes your data on the vendor's servers under their terms. Private AI inverts that: the model comes to your data, the audit trail is yours, and you can inspect the system down to the source.
What Are Private AI Models?
Private AI models are language models deployed inside an environment the organization controls, instead of called over a public multi-tenant API. The weights run on your hardware or your cloud tenant, and inference happens where your data already lives.
They come in two forms: open-weight models (Llama, Mistral, NVIDIA Nemotron, Qwen) that you can host outright, and commercial models (Claude, GPT, Gemini) accessed through a private deployment such as a VPC or a hyperscaler's isolated tenant.
The decisive question is not which model, but who controls the runtime around it. A private AI platform that is model-agnostic lets you run any of them — and switch as the frontier moves — without re-architecting.
Private AI vs. Public AI: What's the Difference?
Public AI is multi-tenant SaaS: your prompts and documents are sent to the vendor, processed on shared infrastructure, and governed by the vendor's data policy and retention terms.
Private AI keeps the data on infrastructure you control. The model is deployed into your environment, the logs and audit trail are yours, and nothing is used to train someone else's foundation model.
For regulated organizations the gap is concrete: public AI requires trusting a data-processing agreement; private AI removes the third-party custodian entirely. That is why financial services, healthcare, government, and legal buyers increasingly require it.
Can Private AI Be Integrated With Existing Enterprise Systems?
Yes — and integration is now the main requirement, not an afterthought. A private AI platform connects to the systems where work already happens: identity (SSO/SAML), data stores, the LMS or SIS, CRMs, ticketing, and internal knowledge bases.
The pattern that works is an owned platform with connectors and an agent runtime, rather than a standalone chatbot. Because ibl.ai ships as source code you deploy in your own environment, integrations run inside your network — the AI reaches your systems without your data reaching a vendor.
Retrieval, agents, and guardrails (NVIDIA NeMo) all run in the same controlled boundary, so a private deployment behaves like part of your stack, not a bolt-on.
How to Deploy Private AI (Cloud, On-Premise, Air-Gapped)
There are three deployment shapes, in increasing order of control:
Your own cloud (VPC): the platform runs in your AWS, Azure, or GCP tenant. Fastest to stand up, and data stays in accounts you own.
On-premise: the platform runs in your own data center. Right for organizations with existing infrastructure and strict residency rules.
Air-gapped: no network path to the outside world — required for classified, IL5/IL6, CJI, or the most sensitive workloads. The model weights and all data live entirely inside the enclave.
ibl.ai supports all three from the same codebase, so the deployment can move from VPC to on-premise to air-gapped without changing platforms.
Frequently Asked Questions
Is private AI the same as self-hosted AI?
Mostly, yes. Self-hosted AI is one way to achieve private AI — you run the platform on your own infrastructure. The broader idea of private AI also covers private deployments inside an isolated cloud tenant.
Does private AI mean I can only use open-source models?
No. A model-agnostic private platform runs open-weight models (Llama, Nemotron, Qwen) and commercial models (Claude, GPT, Gemini) via private deployment — and lets you switch between them.
Is private AI more expensive than public AI?
At small scale, public per-seat SaaS can be cheaper. Above ~100 users, a flat-licensed or self-hosted private platform is typically far cheaper because cost tracks usage and GPU, not headcount.