Sep 15, 2026 · 10 min · 5 segments
What does it take to move from AI experimentation to a production-ready environment? Recorded at VMware Explore 2026, Vijay Ramachandran, Vice President of Products for the VMware Cloud Foundation…
Vijay RamachandranGuest
John NicholsonHost
Pete FlechaHost
Obviously, the big word on town is AI Factory, and I want to dive into that a little bit.
There's a difference between the VMware AI Factory and what you find with other vendors, right? And what we hear from customers and what I've been talking to customers about and what they complain to me about is AI, much like Kubernetes in the past, is becoming a siloed infrastructure deployment.
And, you know, with you know, getting an AI factory from other vendors, you know, essentially you're feeding into the siloed nature of the deployment.
And so what we have done is slightly different, which is we have our AI factory, which is completely software-defined, works on any hardware underneath.
It could be Dell, it could be Lenovo, it could be Supermicro, any hardware, any GPU, NVIDIA, AMD, any GPU.
And our AI factory delivers zero to one operations, meaning going from bare metal to serving models through a software automation.
And I think that really brings AI factory into a VCF managed you know, deployment model, where it doesn't have to be a silo, just another workload that's being managed centrally by the IT team.
We provide the tools to bring it up, we provide the tools to manage it, we provide the tools to upgrade it, and so it sort of falls into the same VCF management model.
training you came here and learned about VCF lifecycle will carry forward in terms of lifecycle.
learned about VCF lifecycle, the same training you learned to troubleshoot VCF, the same training that you learned to operate D2 operations and upgrade and patching, same things apply.
That's fun because there's been other attempts at people forming silos and then watching it like, oh, I'm going to build a silo just for databases.
Obviously, the big word on town is AI Factory, and I want to dive into that a little bit.
There's a difference between the VMware AI Factory and what you find with other vendors, right? And what we hear from customers and what I've been talking to customers about and what they complain to me about is AI, much like Kubernetes in the past, is becoming a siloed infrastructure deployment.
And, you know, with you know, getting an AI factory from other vendors, you know, essentially you're feeding into the siloed nature of the deployment.
And so what we have done is slightly different, which is we have our AI factory, which is completely software-defined, works on any hardware underneath.
It could be Dell, it could be Lenovo, it could be Supermicro, any hardware, any GPU, NVIDIA, AMD, any GPU.
And our AI factory delivers zero to one operations, meaning going from bare metal to serving models through a software automation.
And I think that really brings AI factory into a VCF managed you know, deployment model, where it doesn't have to be a silo, just another workload that's being managed centrally by the IT team.
We provide the tools to bring it up, we provide the tools to manage it, we provide the tools to upgrade it, and so it sort of falls into the same VCF management model.
training you came here and learned about VCF lifecycle will carry forward in terms of lifecycle.
learned about VCF lifecycle, the same training you learned to troubleshoot VCF, the same training that you learned to operate D2 operations and upgrade and patching, same things apply.
That's fun because there's been other attempts at people forming silos and then watching it like, oh, I'm going to build a silo just for databases.
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