---
title: Private AI vs public AI: what is the difference?
description: Private AI vs public AI compared: who runs the model, where your data lives, who sees prompts, plus on-premises, sovereign and air-gapped AI explained.
canonical: https://privatesuperintelligence.si/guides/private-ai-vs-public-ai/
last-updated: 2026-10-07
---

# Private AI vs public AI: what is the difference?

Public AI is a model you reach over the internet on a provider's shared servers, under the provider's terms for storing and using what you send. Private AI runs on infrastructure you or a contracted operator control, so your prompts, files and outputs stay inside a boundary you set. The choice comes down to who can see your data, who maintains the system and how much capability you need on day one.

## Definitions

Five terms come up when you compare options. They overlap, and a single deployment can fit more than one.

- **Public AI** is a hosted assistant or API that serves many customers from the same infrastructure. ChatGPT, Gemini and Claude in their consumer forms are examples.
- **Private AI** is the approach VMware described when it launched its Private AI program in 2023: an architecture that "enables privacy and control of corporate data, choice of open source and commercial AI solutions, quick time-to-value, and integrated security and management."
- **On-premises AI** runs the model on hardware in your own building or data center. It is one way to deliver private AI.
- **Sovereign AI** describes control at the level of a nation or jurisdiction. NVIDIA frames it as countries "building and running artificial intelligence using national infrastructure, data, talent and business networks, in ways that fit their language, culture and regulations."
- **Air-gapped AI** runs on a system with no network path to the outside. NIST's glossary defines an air gap as an interface where two systems "are not connected physically" and any logical connection "is not automated (i.e., data is transferred through the interface only manually, under human control)."

| | Public AI | Private AI (hosted) | On-premises AI | Sovereign AI | Air-gapped AI |
|---|---|---|---|---|---|
| Who runs the model | The AI provider | A dedicated operator under contract, or your team in a private cloud | Your IT team | A national or regional operator, or you within that jurisdiction | Your team, inside an isolated network |
| Where data lives | Provider's shared cloud | A tenant or region reserved for you | Your own servers | Inside a defined country or legal zone | Inside the isolated network, moved in and out by hand |
| Who can see prompts | The provider, under its retention and training policy | You and the operator, as the contract allows | You | You and the in-country operator | You |
| Typical cost and effort | Low setup, pay per seat or per token | Moderate, with a subscription or committed spend | High, with GPUs, staff and upgrades | High, often funded at the state or enterprise level | Highest, with manual updates and physical controls |
| Best for | General tasks with non-sensitive data | Firms that want privacy without running hardware | Regulated workloads with in-house AI staff | Governments and firms bound by data residency law | Defense, critical infrastructure and the most sensitive records |

## How public AI providers treat your data

Policies differ by product tier, so read the terms for the exact plan you use. OpenAI's help center states that "when you use our services for individuals, such as ChatGPT and Codex, we may use your content to train our models," and that you can opt out under Settings > Data controls. The same page says OpenAI does not use inputs or outputs from ChatGPT Business, ChatGPT Enterprise, ChatGPT Edu or its API to improve its models by default. OpenAI's enterprise privacy page adds that deleted conversations leave its systems within 30 days unless the law requires it to keep them.

Those terms are a policy promise. Your data still sits on someone else's servers, and the provider still processes every prompt.

## The trade-offs

**Capability.** Public frontier models tend to lead on reasoning and breadth, and you get each upgrade the day it ships. A private deployment runs the model you install. Open-weight models have narrowed the gap, and NVIDIA's CEO Jensen Huang argued on July 24, 2026 that "open models strengthen safety and cybersecurity, accelerate innovation and diffusion, and enable sovereignty." In the same post he wrote that the world needs both frontier closed models and frontier open models.

**Cost.** Public AI costs little to start and scales with usage. Private AI moves spending toward fixed costs: GPUs or reserved cloud capacity, storage, networking and the people to run them. At high, steady volume the fixed model can cost less per query. At low volume it costs more.

**Maintenance.** With public AI the provider patches, scales and monitors the system. With private AI those jobs fall to you or your operator, including model updates, security fixes and capacity planning.

**Security.** Public AI exposes your data to the provider's staff, subprocessors and any breach of their systems, within the limits of their policy. Private AI shrinks that circle to your own environment, which shifts responsibility to your controls. A private model with weak access rules can leak as much as a public one.

## What air-gapped AI means, and its limits

An air-gapped AI system has no live connection to the internet or to your wider network. Updates, model weights and documents cross the gap on physical media, under human control. For a firm handling classified, legal or family records, that removes the remote attack paths a connected system faces.

The gap has limits. A CISA advisory on Stuxnet reported that "USB drives appear to be a primary infection mechanism" and urged organizations to establish technical measures to disable USB drives. ESET researchers reviewed 17 malicious frameworks built to breach air-gapped networks and found that all of them used USB drives to move data in and out. An air gap also cuts the model off from live email, calendars and market data, so the assistant works from whatever you carry across. You trade convenience and freshness for isolation, and you still need strict rules for removable media.

## Decision guide: which to choose when

- **Choose public AI** for drafting, research and coding on material you would be comfortable posting on a shared drive, and pick a business tier whose terms exclude training.
- **Choose hosted private AI** when you handle client, deal or personal financial data and you want the privacy without hiring a GPU operations team.
- **Choose on-premises AI** when regulation or policy forbids data leaving your facilities and you have staff to run the stack.
- **Choose sovereign AI** when law requires data and processing to stay within a country or region.
- **Choose air-gapped AI** when the cost of any exposure outweighs the loss of live data and fast updates.

Many organizations mix these. You might use public AI for marketing copy and a private or air-gapped system for board papers and client records.

## Private superintelligence vs hosting a private model

Installing a private LLM settles one question: where inference runs. Your data can still leak through the parts around the model. Chat logs persist on a vendor's servers, messages travel over third-party channels, an agent acts with broader permissions than you meant to grant, or backups land in a region you did not choose.

[Private superintelligence](/what-is-private-superintelligence/) treats privacy as a property of the whole system. It covers memory and retention, communication, permissions over what the AI can access and do, model inference, data storage and deployment, so each layer has its own boundary.

Private SuperIntelligence from Mitosis Labs is an AI concierge for enterprises, family offices and private clients built on six privacy layers: retention and deletion, communication, access and actions, model inference, data storage and deployment. The provider retains nothing and trains on nothing, and deployments can be isolated and air-gapped. Mitosis Labs limits each intake, and the current intake is fully subscribed, so you can [register interest](/#waitlist) for a future place.

## Frequently asked questions

### What is private AI?

Private AI is AI that runs on infrastructure you or a contracted operator control, so your prompts, documents and outputs stay within your boundary. It can run on premises, in a dedicated cloud tenant or on an air-gapped network.

### Is ChatGPT public AI?

Yes. ChatGPT runs on OpenAI's shared infrastructure. OpenAI says it may train on content from its individual plans unless you opt out, and that it does not train on Business, Enterprise, Edu or API data by default.

### What is the difference between on-premises AI and cloud AI?

On-premises AI runs on hardware you own in your own facility, and your team maintains it. Cloud AI runs in a provider's data center, either on shared infrastructure or in a tenant reserved for you.

### Is sovereign AI the same as private AI?

No. Sovereign AI is about keeping data, compute and governance inside a nation or jurisdiction, while private AI is about keeping them inside one organization. A system can be both when a private deployment also sits within the required country.

### Can an air-gapped AI system be hacked?

Yes. Researchers have documented malware that crosses air gaps on USB drives, Stuxnet being the best-known case. Strict controls on removable media and on who can carry data across the gap reduce that risk.


## Related guides

- [Is ChatGPT safe for confidential information?](/guides/is-chatgpt-safe-for-confidential-information/)
- [What is zero data retention AI?](/guides/zero-data-retention-ai/)
- [How to keep company data private when using AI](/guides/keep-company-data-private-with-ai/)
- [Private AI for family offices](/guides/private-ai-for-family-offices/)
- [What is private superintelligence?](/what-is-private-superintelligence/)

## Sources

- [Announcing the launch of VMware Private AI, VMware Cloud Foundation Blog (Broadcom)](https://blogs.vmware.com/cloud-foundation/2023/08/22/introducing-vmware-private-ai-foundation/)
- [How Nations Are Deploying AI for Strategic Priorities, NVIDIA Blog](https://blogs.nvidia.com/blog/what-is-sovereign-ai/)
- [Air gap, NIST Computer Security Resource Center Glossary](https://csrc.nist.gov/glossary/term/air_gap)
- [How your data is used to improve model performance, OpenAI Help Center](https://help.openai.com/en/articles/5722486-how-your-data-is-used-to-improve-model-performance)
- [Enterprise privacy at OpenAI, OpenAI](https://openai.com/enterprise-privacy/)
- [Jensen Huang on open models, X, July 24, 2026](https://x.com/JensenHuang/status/2080643682408321103)
- [Stuxnet Malware Mitigation (Update B), CISA](https://www.cisa.gov/news-events/ics-advisories/icsa-10-238-01b)
- [Jumping the air gap: 15 years of nation-state effort, ESET WeLiveSecurity](https://www.welivesecurity.com/2021/12/01/jumping-air-gap-15-years-nation-state-effort/)