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Private AI, explained

What is private AI?

A plain-language guide to private, on-premise AI: what it is, how it differs from ChatGPT and the cloud, what it costs, and who it is for.

Private AI is artificial intelligence that runs entirely on hardware your business owns and controls, rather than on a third party's cloud. The model, your data, and every query stay inside your own network, so nothing is sent to an outside provider to be processed.

Key points
  • The AI model runs on servers you own, not a vendor's cloud.
  • Your prompts and documents never leave your network.
  • Most business tasks run on open models you can self-host, so a frontier model is rarely needed.
  • You pay a flat cost instead of per token, and you own the hardware.
  • It is not only for regulated industries; any business with data worth protecting benefits.

What does "on-premise" mean for AI?

On-premise means the AI runs on servers physically inside your own building or data center, on infrastructure you own. The model weights and your documents sit on your storage, and inference happens on your local GPUs. For the most sensitive cases it can be fully air-gapped, with no route to the public internet at all. See how it works.

How is private AI different from ChatGPT or cloud AI?

With cloud AI like ChatGPT, Claude, or Gemini, your prompts and files leave your network to be processed on servers you do not control, and you pay per token. Private AI keeps everything in-house on hardware you own, for a flat cost, with no provider that can change terms or restrict a model. See the full private AI vs ChatGPT comparison.

Do you need a frontier model to run private AI?

Usually not. Around 80% of typical business tasks run well on open models you can host yourself, and a smaller model fine-tuned on your own data often beats a generic frontier model on your specific work. Read why you probably do not need a frontier model.

What can businesses do with private AI?

The same things they would use any AI assistant for, but on their own data: answering questions over their documents, summarizing and analysis, drafting and correspondence, an internal help desk, and workflow automation. See the full list of use cases.

Is private AI only for regulated industries?

No. Regulated firms in healthcare, finance, legal, and defense feel the need most sharply, but any business that drafts, researches, supports, or handles sensitive information benefits, and would rather own its AI than rent it. Read why any business would want private AI.

How much does private AI cost?

Managed plans start at $499/mo and cover the build, management, and support, with no per-token metering. Hardware passes through close to cost (roughly $20k to $180k by tier) and you own it outright. See pricing or run the numbers.

Glossary

Private AI terms, defined.

Private AI
Artificial intelligence that runs on hardware your business owns and controls, so your data never leaves your network.
On-premise AI
AI deployed on servers inside your own building or data center, instead of a third party's cloud.
Air-gapped
A system with no connection to the public internet, fully isolated for maximum security.
Open-weight model
An AI model whose parameters are published, so you can run, own, and fine-tune it yourself, such as Llama, Qwen, or Mistral.
LLM
Large language model — the kind of AI that understands and generates text.
RAG
Retrieval-augmented generation — letting a model answer using your own documents by pulling the relevant passages at query time.
Fine-tuning
Further training a model on your own data so it is better at your specific work.
Inference
Running a trained model to get an answer; the everyday act of using the AI.
FAQ

More questions, answered.

Is private AI secure?
Yes, and arguably more secure than cloud AI, because your data never leaves your network. There is no third-party API in the path of a query, so prompts and documents are never sent outside. Access control, encryption, audit logging, and optional air-gapping harden it further.
Can small businesses use private AI?
Yes. A single modern GPU can serve a whole team for private chat and document work, so you do not need a data center to get started. The system scales from one box to a company-wide deployment.
How long does it take to set up private AI?
Typically a few weeks from spec to a running system: sizing the hardware and model, installing the stack on-premise, fine-tuning on your documents, securing it, and handing your team a working interface.
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