What is a self-hosted AI / private LLM platform?

A self-hosted AI platform runs entirely on infrastructure you control — your own servers, your private cloud (AWS, Azure, GCP), or a fully air-gapped network. No prompts, documents, or embeddings are sent to a third-party vendor's cloud. Your data never leaves your walls, and you own the code and the models.

How does ibl.ai let you own the model and data?

You receive the full source code of the ibl.ai platform under a perpetual license, and you deploy it where you choose. The platform is model-agnostic, so you can run open-source LLMs (Llama, Mistral, Qwen and others) privately, or connect commercial models with your own keys. The code, the data, and the model weights stay yours.

Self-hosted vs. ChatGPT, Claude, Gemini or Copilot — what's the difference?

ChatGPT, Claude, Gemini and Copilot are rented: they run on the vendor's cloud, lock you to that vendor's models, and bill per seat or per token. Your data leaves your environment. With a self-hosted ibl.ai deployment you own the platform, run any model, keep data in your environment, and pay a flat usage-based fee instead of per-seat pricing.

Can the platform run air-gapped or on-premise?

Yes. ibl.ai can be deployed on-premise or fully air-gapped, with local models and zero external API calls. This is designed for regulated and high-security environments — see Air-Gapped AI and On-Premise Deployment for the architecture.

Is a self-hosted deployment compliant (HIPAA, FedRAMP, FERPA)?

Because data stays inside your perimeter and every interaction is logged, self-hosting maps directly to HIPAA, FedRAMP, FERPA, and similar regimes. The same private-AI model applies across regulated sectors — healthcare, financial services, government, and education.

What does self-hosted AI cost versus per-seat SaaS?

Self-hosting replaces per-seat licensing with flat, usage-based pricing, which is typically far cheaper at scale because cost no longer rises with every new user. Use the AI Cost Calculator to compare your per-seat spend against an owned deployment.

Self-Hosted AI & Private LLM Platform

Run a model-agnostic AI platform on your own servers — on-premise, air-gapped, or in your private cloud. Your data never leaves your walls.

You own the code, the data, and the models, with no per-seat fees. It is the sovereign alternative to renting AI from a single vendor.

Any LLM, Private or HostedOpen-source or commercial — your choice

You Own Code & DataPerpetual license, zero lock-in

On-Premise or Air-GappedZero external API calls

No Per-Seat PricingFlat cost — up to 85% lower at scale

Build And BuyProduction agents plus engineers

See Build vs. BuyWhy you don't have to choose

What Is a Self-Hosted AI Platform?

A self-hosted AI platform runs on infrastructure you control instead of a vendor's cloud. Prompts, documents, and embeddings stay inside your network.

A private LLM means the model itself runs in your environment — an open-source model you host, or a commercial model accessed through your own keys and tenancy.

The ibl.ai platform packages both: a full agentic stack you deploy on your servers, with the model, the code, and the data under your ownership.

Own the Code, Data, and Models

You receive the complete platform source under a perpetual Full Code License — the same code ibl.ai runs in production.

Deploy it, modify it, audit it. Your data never trains anyone else's model, and there is no vendor that can revoke your access or change your terms.

Model-Agnostic: Run Any LLM, Including Private Ones

The platform is model-agnostic. Run open-source models such as Llama, Mistral, or Qwen privately on your own GPUs.

Or connect commercial models — Claude, GPT, or Gemini — through your own accounts when you want them. Intelligent routing lets you switch models anytime, with no rewrite, via Agentic OS.

On-Premise, Air-Gapped, or Your Cloud

Deploy on-premise, in your private cloud (AWS, Azure, GCP), in GovCloud, or fully air-gapped with local models and zero external API calls.

See Air-Gapped AI for the isolated-network architecture, or On-Premise Deployment for hosting it inside your own data center.

Self-Hosted AI vs. ChatGPT, Claude, Gemini & Copilot

The hyperscaler assistants are rented. A self-hosted ibl.ai deployment is owned. Here is how the two models compare on the dimensions that decide ownership.

Model choice

Any LLM — open-source or commercial, private or hosted. Switch anytime.

Locked to one vendor's models.

Where it runs

Your servers — on-premise, air-gapped, or your own cloud.

The vendor's cloud — your data leaves your walls.

Ownership

You own the code, the data, and the models.

You rent access; the vendor owns the platform.

Cost at scale

Flat, usage-based pricing — no per-seat lock-in.

Per-seat and per-token SaaS pricing.

Agents

Production agents out of the box, plus the tools to build your own.

A chat assistant — you build and host your own agents.

Private AI for Regulated Industries

When data cannot leave your perimeter, self-hosting is the answer. Every prompt and response stays inside your environment and is fully logged for audit.

The same private-AI and model-ownership model runs across all eight sectors ibl.ai serves — including healthcare (HIPAA), financial services, and government (FedRAMP).

Lower Cost at Scale — No Per-Seat Lock-In

Per-seat SaaS pricing punishes growth: every new user adds cost. An owned deployment replaces that with flat, usage-based pricing.

At scale this runs up to 85% lower. Compare your current per-seat spend on the AI Cost Calculator.

Build and Buy — You Get Both

You don't have to choose between building from scratch and buying a locked SaaS tool. You get a production-ready platform you own from day one.

ibl.ai engineers can work alongside your team to connect your data sources and build your agents through Forward-Deployed Engineering. See the full build vs. buy breakdown.