Nutanix CEO Rajiv Ramaswami said the vendor spent $20 million building its own AI cluster to help manage token spend tied to internal software development costs, a platform that it’s also now using to underpin some of the vendor’s new agentic AI customer offerings.
Ramaswami during a recent press briefing said the vendor invested that money into its own graphic processing unit (GPU) cluster where it’s now starting to run its open weight models.
“We expect that we will be able to handle a good chunk, up to maybe 80% of our internal needs by this hosted model, hosted on our own clusters, hosting open weight models,” Ramaswami said, adding that “for the remaining we will still go use the best frontier models that are out there.”
Ramaswami explained that Nutanix is using the cluster internally for writing code and coding across the software lifecycle, which includes quality assurance (QA) testing and front-end design. This stack also underpins the vendor’s recently launched Agentic Gateway platform.
“We started out using standard frontier models … Copilot, Cursor, Claude, and the usage has exploded, and so have the costs,” Ramaswami said, adding that the internal AI cluster has allowed the vendor to slash AI-generated software development “on a per-token basis,” and that Nutanix expects the investment to have paid off within one year.
Ramaswami noted that this return on investment is key as AI-related token costs have started to soar and is increasingly “front and center” of “every conversation” the vendor is having with customers.
“Every customer I talk to, every CIO I talk to, is trying to figure out how to manage and control their AI deployments and manage the use of tokens and provide the ability for their teams to go run AI without constraints, but at the same time managing costs,” Ramaswami said.
“I think this is what heavy users outside and other companies are also going to start to realize that they can actually mix-and-match open-weight models on their own clusters or rented clusters and not have to pay on a per-token basis, and frontier models for the best and greatest, and optimize their use while also encouraging more use,” Ramaswami said.
The tokenomics journey
Nutanix’s internal move is similar to what other vendors have started to implement over the past several months.
Cisco chief product officer Jeetu Patel recently told SDxCentral that the vendor’s AI Defense product was “built 100% with AI. No human lines of code,” and that Cisco expects approximately 70% of the networking giant’s product portfolio will be fully AI-generated by the end of next year.
Patel later told attendees at Cisco’s annual Live event that token management, also known as “tokenomics,” is part of a broader need for vendors and organizations to gain perspective on token use and demand going forward. Patel specifically noted the need to link token value with results.
“When you start thinking about where the risk lies, it would be when the cost of tokens and the value derived from the tokens actually have a distance, and that’s the thing that we, as an industry, have to be really careful of is you want to make sure that you’re economically generating tokens that create the necessary and desired output from an end-result perspective, on the core metrics that you, as a business, are trying to go out and measure, because that is going to be extremely important, that is in equilibrium with the cost of the tokens,” Patel explained. “If your costs go out of whack, but the benefit is not there, that’s when you will actually see some pullback.”
Patel added that this is why it’s important for an organization to get an early handle on their AI usage.
“I feel like across the board right now, the first phase that you get in AI is you have to get good with using it, which means you have to get familiar with it first. That consumes tokens,” Patel said of that initial stage. “Once you get familiar, then you get good and that’s when you start creating good outcomes. And once you get good, that’s when you start seeing very strong accretions of value with your company.”
The market overall is “still in phase one,” Patel noted. “Uniformly, all companies haven’t gotten good at this yet. We have to get good. Once you start getting good, you start seeing value and outputs get accreted in a very quantitative way.”
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