The AI Factory: A strategic overview with Hewlett Packard Enterprise
Many organizations find that their AI initiatives, despite early promise, fail to deliver a positive ROI. This can be traced to “token economics”—the complex and often unpredictable costs associated with consuming AI models, particularly in the public cloud. This session will dissect these hidden costs and the architectural bottlenecks that lead to runaway spending and stalled projects. We’ll then present a comprehensive overview of HPE Private Cloud AI, a full-stack, turnkey solution designed to provide predictable costs, superior performance, and total control. We will explore how its integrated hardware and software—from NVIDIA GPUs and HPE servers to a unified management console—enable a powerful and predictable path to production, turning AI from a financial gamble into a strategic business asset.
The presentation highlights the often-overlooked costs associated with AI initiatives in the public cloud, citing examples like over-provisioning, lack of checkpointing, and inefficient data usage. The speaker emphasizes that many companies experience significantly higher operational costs than initially anticipated, with one example of an oil and gas company spending ten times more than projected. While some companies may not be overly concerned with these cost overruns if the AI models deliver results, HPE contends that this isn’t sustainable for most organizations and that there are cost savings to be found.
HPE’s solution, Private Cloud AI, offers a predictable cost model and significant savings compared to cloud-based alternatives. These cost savings, averaging around 45%, are most pronounced with larger systems managed within the customer’s own data center, though co-location options are also available with slightly higher overhead. Furthermore, HPE’s solution addresses the hidden costs associated with building and managing an AI infrastructure from scratch, including the need for specialized teams and resources for each layer of the technology stack.
Beyond cost considerations, HPE’s Private Cloud AI provides greater control over data, mitigating concerns about data privacy and usage in downstream training cycles, which is important considering inquiries into the training data used for some AI models. The solution offers flexible purchasing options, including both CapEx and OpEx models, with HPE GreenLake enabling reserved capacity and on-demand access to additional resources without upfront costs. This combination of cost-effectiveness, control, and flexibility positions HPE Private Cloud AI as a compelling alternative to the public cloud for AI deployments.
Presented by Mark Seither, Solutions Architect, Hewlett Packard Enterprise. Recorded live on September 11, 2025, at AI Infrastructure Field Day 3 in Santa Clara, California. Watch the entire presentation at https://techfieldday.com/appearance/hpe-presents-at-ai-infrastructure-field-day-3/ or visit https://hpe.com/private-cloud-ai or https://techfieldday.com/event/aiifd3/ for more information.
Transcript
PCI solutions architect with HPE, the cloud makes it easy, right? Sure. Kind of.
You don't care about money. Money, right? And but that's the truth because at the end of the day, every single one of these points is something that's true and legitimate, right?
People over provision you resources and they don't pay attention to it. They'll put long running jobs up with no check pointing, right? And so if that job fails 10 hour job, it fails in the ninth hour, you lost all that work and you gotta go rerun it again, right?
You've got things like the data piece is crazy because no one, it seems in the cloud is a good corporate citizen. If anyone's ever noticed that people will always do the worst possible thing that they can do. Mm-hmm.
So I've seen people serving static webpages from DynamoDB, like very expensive storage for doing nothing, right? And people like, well, why did you do that? I look cool.
Right? That doesn't make a lot of sense. And so there's not a lot of great corporate citizenship and that extends into people spin up instances and guess what?
They never kill them. Right? So those are big problems because when you start realizing that like I have a oil and gas company that I met with, you would know them.
Um, and they said our operational costs are 10 times what we thought they were gonna be. 2 million in the cloud. Instead it's costing us about $12 million a year.
They also said the same breath that we don't care 'cause it worked. I thought that was interesting. But not every company is making that type of money where they can just not care about 10 x cost over what they thought it was gonna be.
Um, and so these are very important conversations to talk about and to have and uh, this is the only one of these, I promise, right? But the IDC report about HP's position, right? In this private cloud market, um, is very strong, right?
For our private cloud, these manufactured AI systems ultimately, and I have a lot of slides that, um, they're draft slides, so I couldn't bring them up and show you. I tried to distill into something that I can talk to you about. Um, the other ones I'm sure I'll be able to talk to about soon as somebody says yes.
Um, there's cost savings to be had most of the cost savings. It gets better with the larger systems over what you would have, but you can expect to see somewhere between like a 30 and 60% reduction averaging around 45 to per 45 ish percent in cost savings over doing this in the cloud. 3 70 billion model full bore for X amount of time, right?
These are some of the assumptions that we can make. But when you go apples to apples for what do it cost to run these models against this hardware in the cloud versus doing it with us, it always works out in our favor. It works out most in our favor when it's something that you manage inside of your own data center, if you wanna do it with like colocation, right?
Because you can, we have colocation partners, you can do it there. It adds about a 10% overhead, but there's still lots of cost savings to be had there. And some of the places of savings that like you might not see, especially if you're doing this against like let's talk, you know, you're gonna build it and DIY it and do it yourself.
You have to have the teams and the resources to be able to do all of this stuff, right? So from the infrastructure layer all the way to people who are gonna install, configure all the software, you've gotta have teams to support each piece of that stack. We can get you away from all that and get you away from the development time that it takes to do all of that.
These are, yeah, these are my charts. One of the charts, and I have 32 minutes to probably show you guys 32 minutes worth of a very compelling demo that I built, and I'm proud of it. So in closing, for this component, this piece of it, uh, fast time to value complete control over the security data sovereignty.
We haven't touched on that a whole lot, but I am less than a hundred percent sold that all of these companies that are very heavily incented to continue to grow and learn with their models, I'm less than inclined to take their word that my data is not gonna end up somewhere downstream in a, you know, following training cycle. Mm-hmm. I know they come out with those agreements that say they're not gonna use my data.
There's, uh, I think it was today actually, I saw, I read there's a, uh, there's an inquiry being launched into some of these models and the trading behind them. So it very much better for me to just have it all within my four walls and I know it's gonna stay there. It's on this box that I can physically look, look at, see, or it's in a colo, um, predictable costs.
This can be bought, interestingly, it can be bought either CapEx or opex, right? So you can do this as a subscription where you buy, and then you have flexibility for capacity with GreenLake, right? So with our GreenLake construct, if you're familiar with it, is essentially you can have res like a, like reserve capacity, but then you can have extra space that you can bump into that you don't pay for until you use it.
So those GPU CPUs that aren't turned on until you need them, or how, where do you get that capacity from? Yeah. Like we would put extra capacity on the floor forward here.
Okay.
