Episode Details

In this Tech Barometer podcast segment, Mayank Gupta, director of product marketing at Nutanix, explains how intelligent FinOps tools automatically detect optimizations and take corrective action to manage cloud costs, track carbon footprints, and meet regulatory requirements in the AI era.

Find more enterprise cloud news, features stories and profiles at The Forecast.

Transcript:

Mayank Gupta: How can we make FinOps itself a lot more intelligent, now that we have Gen AI, can intelligence be built into the system where it automatically understands usage patterns and takes action to bring it down?

Jason Lopez: Generative AI is transforming how organizations manage cloud and IT spending. Although it brings new costs and it also affords new opportunities for automation. This is the Tech Barometer podcast, I’m Jason Lopez. On today’s edition: FinOps, bringing together finance, engineering, and business teams to manage cloud computing costs. This is increasingly important for optimizing IT resources in the age of AI.

Mayank Gupta: A lot of companies are getting into a hybrid multicloud environment as they’re using GenAI.

Jason Lopez: Mayank Gupta, Director of Product Marketing at Nutanix, tells The Forecast editor-in-chief Ken Kaplan how FinOps is evolving with better observability and automated cost controls.

Mayank Gupta: They are realizing that they have to actively track these dollars they cannot just use and run this forever without having some sense of accountability.

[Related: AI is Hungry for Data, But Can IT Infrastructures Keep Up?]

You used to just track your CPU storage. Now you’re tracking one more resource, which is your GPU. Similarly, when it comes to training and inference, whether you’re running on prem or whether you’re running on the public cloud, a lot of these FinOps tools have to have insights into your training cost and inferencing cost. Training models can be a very expensive proposition. So we are building that in the tool, and we’re building these new features into our cost governance models, where we are seeing something which has come into the industry, going to be there for the very long haul. Let’s bring in those models and have that. The other very interesting side of this business is, how can we make FinOps itself a lot more intelligent now that we have Gen AI? Can intelligence be built into the system where the system automatically, through agents or through intuition, through learning, understands, hey, this is the usage pattern. And if it’s deviating from them, let me take some action automatically and bring it down. How can we make the job of an IT admin or a FinOps admin easier, getting all these signals from different places? And now we have AI to help. We can distill these signals in something which is easily digestible. Even on the actions that need to be taken off, the user can say, hey, take these actions based on a set of best practices, and now Gen AI can help with them. So we are looking at, how can GenAI make it more effective?

[Related: Performance Engineering in the Age of AI]

Ken Kaplan: Let’s talk about maybe the biggest challenges of your customers, the biggest challenges that come to mind and you hear all the time.

Mayank Gupta: Certainly I think what’s happening a lot is customers have their on-prem infrastructure, which is their data centers. They might have stuff on the edge. They definitely have stuff on the public cloud. How do I have visibility across public cloud, ed