Private AI cloud
What is a private AI cloud, and what software does it need?
A private AI cloud is AI infrastructure and software that one organisation controls, running on its own premises or in a facility it chooses, and offered to its teams as a service. To be useful it needs more than GPUs: model serving, a gateway, knowledge, an agent platform, guardrails and a place for staff to work.
Private AI cloud and public cloud AI
| Question | Public cloud AI | Private AI cloud |
|---|---|---|
| Where do prompts and data go? | To the provider | They stay inside your boundary |
| Who operates it? | The provider | You, or an operator you appoint |
| Which models? | The catalogue the provider offers | Open-weight models you choose and host |
| How is it paid for? | Per token or per hour | Capacity you own, shared across teams |
| Can it run disconnected? | No | Yes, if built for it |
Who builds one
- Banks, insurers and pension funds, whose data cannot leave.
- Governments and public-safety agencies, often with air-gapped needs.
- Telecom operators and managed-service providers, who sell it on to customers.
- Large groups that want one shared AI service for every business unit.
The software it needs
| Layer | What it does |
|---|---|
| 1. Cluster | Schedules GPUs and runs containers |
| 2. Model serving | Loads models onto the GPUs and answers requests |
| 3. Gateway | One controlled way in: access, budgets, logging |
| 4. Knowledge | Lets agents use your documents and data, with permissions |
| 5. Agents | Where agents are built and run |
| 6. Guardrails and evaluation | Keeps agents inside policy and tests them before release |
| 7. Use | Where staff actually work with agents |
Hardware vendors deliver the first one or two layers. The rest is where most projects stall, because each layer is a separate tool to choose, integrate and run.
Ways to get the infrastructure
- A turnkey system, such as HPE Private Cloud AI, delivered as one product.
- GPU servers you build into your own estate, following a reference design.
- Capacity in a local provider facility, operated for you.
A buyer checklist for the platform
- Does it run entirely inside your boundary, with no outside service at run time?
- Does it cover layers three to seven, or will you integrate several products?
- Can each department have its own access, budget and cap?
- Does every agent run under the same access rules, guardrails and audit trail?
- Can you choose and change the models?
- Does it work on more than one kind of infrastructure, so you are not locked to a hardware choice?
Where Katonic fits
Katonic is a platform for building, running and governing AI agents on infrastructure you own. It supplies the layers above the hardware: the AI Gateway, the Knowledge Engine, Katonic Studio for building agents, guardrails, evaluation and audit. It runs on a turnkey system or on your own GPU servers, and can run with no outside connection.
Read next
- The seven software layers, in detail →
- Budgets and chargeback for departments →
- Running agents air-gapped →
- Sovereign AI platforms: an evaluation guide →
Questions and answers
Is a private AI cloud the same as on-premise AI?
Close. On-premise says where it runs. A private AI cloud also means it is offered as a shared service to many teams, with access, budgets and metering.
Is a private AI cloud the same as sovereign AI?
Not by itself. Sovereignty also depends on who governs the system, who operates it, who holds the keys and who can switch it off.
What is the difference between a private AI cloud and HPE Private Cloud AI?
Private AI cloud is the general term. HPE Private Cloud AI is a specific product from HPE, one way to get the infrastructure for one.
Do I need my own data centre?
No. The infrastructure can sit in a facility you choose, such as a local provider, as long as you control who operates it and where the data is.
What does the software layer cost compared with the hardware?
It varies by scale and scope. The common mistake is to budget for hardware only and discover the software and integration work afterwards.