By Assembling a GPU cloud from component parts requires continuous operational investment that compounds over time. Enterprises that take the DIY route must maintain infrastructure automation, Kubernetes orchestration, service provisioning, billing systems, and customer-facing portals simultaneously while also trying to run a core business. Most teams underestimate what day-two operations actually cost.
In this interview on TFiR, Richard Borenstein, SVP of Growth and Business Development at Mirantis, breaks down the mindset shift enterprises need to make when approaching AI infrastructure and explains how the k0rdent AI platform removes the build-it-yourself burden with a fully pre-assembled, turnkey GPU cloud.
Guest: Richard Borenstein, SVP of Growth and Business Development at Mirantis
Show: TFiR
Here is what every platform engineer and enterprise infrastructure leader needs to know.
Technical Deep Dive
Q: What mindset shift do enterprises need to make before building AI infrastructure?
Richard Borenstein, SVP of Growth and Business Development at Mirantis, says enterprises must stop evaluating AI infrastructure as a product decision and start thinking in terms of ecosystem. The shift is from asking which components to buy to asking which partner allows the team to focus entirely on core business outcomes. Enterprises are increasingly recognizing that carrying the full infrastructure burden themselves makes it impossible to serve customers and grow the business at the same time.
“Stop thinking in terms of products, stop thinking in terms of the next shiny object, and start thinking in terms of ecosystem.” — Richard Borenstein, SVP of Growth and Business Development, Mirantis
Q: What does Mirantis k0rdent AI actually deliver and how does it work?
k0rdent AI is described by Borenstein as a turnkey GPU cloud, a cloud-in-a-box that integrates infrastructure automation, Kubernetes, service provisioning, and customer-facing cloud services into a single pre-assembled platform. The platform is designed so that enterprises and operators can activate it without assembling components themselves. The result is a hyperscaler-like cloud experience that is fully packaged and ready to monetize from day one.
“k0rdent AI provides that turnkey GPU cloud, kind of cloud in a box, that already has integrated the infrastructure automation and the Kubernetes and service provisioning and customer-facing cloud services into that single platform.” — Richard Borenstein, SVP of Growth and Business Development, Mirantis
Q: What customer-facing capabilities does an enterprise AI platform need to include?
Borenstein identifies the customer service portal as a critical and often underestimated component of enterprise AI infrastructure. A complete platform must cover service ordering, team management, billing, and inventory. Without these capabilities in place, the external customer experience breaks down regardless of how well the underlying infrastructure performs.
“That customer service portal that delivers a full service cloud interface, service ordering, team management, billing, inventory, these are the things that they need to have in order to be successful.” — Richard Borenstein, SVP of Growth and Business Development, Mirantis
Q: What is the separation of concerns model in enterprise AI infrastructure?
Borenstein frames the architectural decision in straightforward terms: the infrastructure provider builds and operates the hard part, and the enterprise operator focuses on the business so customers can consume it. This separation makes the internal experience easier for operators and makes the external experience more valuable for customers. Pre-assembling the full stack eliminates the integration work that otherwise consumes engineering capacity indefinitely.
“We can build and run the hard part and you can focus on your business so your customers can consume it.” — Richard Borenstein, SVP of Growth and Business Development, Mirantis
Q: Why do enterprises always move from DIY to managed platforms and is AI infrastructure different?
Borenstein observes that the DIY-to-managed cycle has repeated across every major technology generation. Enterprises begin by wanting to own and assemble everything, then discover that maintaining that ownership prevents them from focusing on business outcomes. AI infrastructure is following the same pattern. The difference is the speed at which the realization arrives and the complexity of the components being assembled.
“At first you’re like I’m going to own this, and then you realize there is really no way for me to focus on my business if I’m actually doing the nitty gritty and trying to piece this together not only for day one but for the long term.” — Richard Borenstein, SVP of Growth and Business Development, Mirantis
Resources and Documentation
- Mirantis, vendor platform page for k0rdent AI and related GPU cloud and Kubernetes infrastructure offerings
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👇 Click to Read Full Raw Transcript
Swapnil Bhartiya: Thank you so much. No, you’re absolutely right there. One more thing I want to ask is that let’s say you are at an event, you walk and met an enterprise team because it’s as much about technology of course culture part we have been talking about a lot. But I also feel that when it comes to AI you also need a mindset shift as well. If you’re advising an enterprise team that wants to build AI infrastructure this year, what is one mindset shift they need to make? First stop thinking in terms of products, stop thinking in terms of the next shiny object and start thinking in terms of ecosystem. What would that be?
Richard Borenstein: I think I mentioned it a little bit earlier, but I’ll expand on it which is they need to and are starting to think about the top end of the stack, how they’re going to empower AI to serve their corporate goals. And that’s where we come in is that k0rdent AI provides that turnkey GPU cloud kind of cloud in a box that already has integrated the infrastructure, automation and the kubernetes and service provisioning and customer facing cloud services into that single platform. So we really anticipate and are seeing them understanding that with the right partner they can actually focus on their core business. They don’t have to carry that burden. It gives both operators and customers that hyperscaler like cloud experience, fully packaged, automated, kind of ready to monetize. And they also want to get that customer facing experience right. So that customer service portal that delivers a full service cloud interface, service ordering, team management, billing, inventory, like these are the things that, that they’re, they need to have in order to be successful. And so we bring that, that customer facing mentality and capability to bear as well. And it offers enormous value because they can already have all of this pre assembled. That makes the internal experience much easier, the external customer facing experience much more valuable. And overall the platform just delivers kind of that clear separation of concerns. We can build and run the hard part and you can focus on your business so your customers can consume it. And I think that’s the shift we’ve gone through this phase of I’m going to build it myself, I’m going to package it together as we’ve seen in previous technology cycles before, right? So I feel like, you know, at first you’re like I’m going to own this and then you realize there is really no way for me to focus on my business if I’m actually doing the nitty gritty and trying to piece this together not only for day one. But for the long term.
