Turnkey or build
HPE Private Cloud AI or HPE GPU servers: which route for AI agents?
Choose HPE Private Cloud AI when one organisation wants a private AI system working quickly with the least assembly. Choose HPE GPU servers when you are building a larger or shared estate to your own design, such as a national platform or a service provider's AI cloud. Both keep data on infrastructure you own, and the same agent platform can run on either.
The two routes
HPE sells private AI infrastructure in two forms. HPE Private Cloud AI is a turnkey system, co-engineered with NVIDIA, that arrives integrated and is managed through HPE's own software. HPE GPU servers are the building blocks of an HPE AI Factory design, which the customer or a partner assembles to its own specification.
The choice is often presented as small against large. That is too simple. The real difference is who does the assembly and how much of the design is fixed.
Side by side
| HPE Private Cloud AI | HPE GPU servers | |
|---|---|---|
| What you buy | An integrated private AI system | Servers, storage and networking to your design |
| Who assembles it | HPE | You or a partner |
| Base software | Included by HPE and NVIDIA | You choose and install it |
| Team you need | Small | A platform engineering team, or a partner acting as one |
| Best fit | One organisation, a defined set of use cases | Large, shared or multi-customer estates |
| Flexibility | Set configurations | Built to specification |
| Data location | Your own premises | Your own premises |
When Private Cloud AI is the better choice
- You want the system working without building a platform team first.
- One organisation will use it, for its own departments.
- You prefer one vendor to be responsible for the integrated stack.
- Your use cases are known and fit the configurations on offer.
When GPU servers are the better choice
- You are building a national or group-wide estate that many bodies will share.
- You are a service provider that will sell AI services to your own customers.
- You need a design the set configurations do not cover.
- You already run a platform team and a cluster, and want to add capacity to it.
Five questions that decide it
- Who will use it? One organisation points to the turnkey system. Many separate bodies or customers point to servers.
- Who will run it? If you have no platform team and do not want one, the turnkey system removes that need.
- How fixed are your needs? Known use cases suit set configurations. An evolving national programme suits a design you control.
- How will it grow? Adding one more system is simple. Growing a shared estate is a design question from the start.
- Who is accountable? One vendor for the stack, or your own architects with partners.
Sovereignty is the same question on both
Neither route is more sovereign than the other by default. Both sit on your premises. What decides sovereignty is who operates the system, who holds the keys, which law applies to the operator, and whether the system keeps working with no outside service. Ask those questions of either route.
What does not change
On both routes the data, the models and the logs stay on infrastructure you own. On both routes the hardware alone does not give the business an agent it can use. Something has to sit above it where agents are built, governed and used.
This matters for the decision. If the layer above the hardware is the same on both routes, the choice between them stops being a choice about what your staff will experience. It becomes a choice about procurement, operations and scale.
Starting on one and moving to the other
Organisations often start with a turnkey system for one department and later build a shared estate. The move is easy if the agents, knowledge and policies were built on a platform that runs on both. It is a rebuild if they were tied to one stack.
Common mistakes
- Choosing servers for flexibility, then discovering nobody is available to assemble the software.
- Choosing the turnkey system for one department, then being asked to host five more bodies that must be kept apart.
- Treating the hardware decision as the whole decision, and leaving the agent layer for later.
Where Katonic fits on each
Katonic AI is a partner in the HPE Unleash AI program. On HPE Private Cloud AI, Katonic connects to the models the system serves and adds the agent platform. On HPE GPU servers, Katonic supplies the software layer above the hardware, including model serving, the gateway and governance. The ten use cases Katonic has published run on either route.
Read next
- Ten use cases on HPE →
- What software do HPE GPU servers need? →
- What is HPE Unleash AI? →
- Use case: shared GPU platform for ministries →
- HPE Private Cloud AI (hpe.com) →
Questions and answers
Which route is more sovereign?
Neither by default. Both run on your own premises. Sovereignty depends on who operates the system, who holds the keys and whether it can run without an outside service.
Can we start on one route and move to the other?
Yes, if the agent platform runs on both. Agents, knowledge and policies built on one can then be carried to the other.
Does Katonic run on both?
Yes. Katonic runs on HPE Private Cloud AI and on HPE GPU servers.
Is HPE Private Cloud AI only for small deployments?
No. It comes in several configurations. The deciding factor is less the size than whether one organisation or many will use it, and who will run it.
Can a service provider use HPE Private Cloud AI?
A provider that hosts many separate customers is usually better served by an estate built on HPE GPU servers, where it controls the design and the separation between customers.