Consultants and analysts say that CIOs likely have little choice but to accept that AI pricing is going to continually rise, and switching to another supplier, even if possible, likely won’t sidestep these increases.
On top of July’s 30% price hikes across almost all of its product lines, Nvidia is reportedly preparing to raise prices of servers, including those powered by Vera Rubin and Grace Blackwell chips, by 15%, due to skyrocketing memory prices.
That 15% increase for systems being delivered in early 2027, reported by Bloomberg and other business media, is seen as part of a series of expected price hikes throughout AI deployments.
Scott Bickley, advisory fellow at Info-Tech Research Group, said that he sees Nvidia’s move as one that is only passing along its own rapidly increasing costs. But rather than price gouging because of its close-to-monopoly market control, Bickley’s calculations suggest that Nvidia is likely eating some of its costs, and is only passing along a fraction of them to its largest customers.
But not all AI-related costs are increasing; per-token prices appear to be dropping, he said. That gives CIOs a potential strategy to manage costs by pushing approaches that will reduce the reliance on memory.
“The workload cost is going down per token while the underlying hardware and infrastructure costs are going up,” Bickley said. “If you are directly building out your own clusters, this is an automatic uplift to an already egregiously expensive solution. If you are buying your own hardware, you’re going to have to suck it up. You are not going to negotiate your way out of this.”
But, he added, CIOs should also be able to get more mileage out of the clusters they are currently running, via techniques such as model routing, compression, and batch processing.
Gaurav Gupta, VP analyst at Gartner, noted that the Nvidia price hikes are reflective of the many pricing increases throughout the AI environment.
“Memory prices are going up, especially HBM and LPDDR5, but there are other aspects, like leading-edge foundry wafers, advanced packaging, and other component shortages,” Gupta said, adding that those issues generate “longer lead-times, which typically translates to higher prices.” And he does not expect the situation to improve any time soon.
“In the current environment of strong demand and limited supply, we expect this situation to continue in the near to mid-term,” he said. “This means higher costs for those deploying these servers/systems and for those renting compute in the cloud, including software vendors/model builders, and others.”
Flavio Villanustre, CISO for the LexisNexis Risk Solutions Group, agreed.
“This is a supply and demand problem, and it’s likely to reach a plateau and eventually improve once memory production ramps up to meet the current demand due to AI, but I don’t think this will happen in the next few months,” Villanustre said. “For now, CIOs will need to contend with the current market conditions.”
But he also agreed with Bickley’s suggestion that CIOs try to squeeze more value from the RAM they already have.
“Some AI model vendors are adapting their models to run better in memory constrained environments,” Villanustre noted. “For example, Gemma E4B and similar models by Google now use a hierarchical tiered model that allows them to pull only the necessary parts of the model into memory instead of holding the entire model in RAM. These models have a much larger effective number of parameters than the memory that they require.”
And, added Mike Wilkes, enterprise CISO at Aikido Security, this means that the Nvidia price hikes may do some good if they convince enterprises to adopt a more thoughtful approach to AI deployments.
“Enterprises have spent the last few years treating frontier-model tokens almost as an infinitely elastic utility, sending workloads to the biggest model whether or not the task required frontier-level reasoning,” he said. “Higher infrastructure and token costs should force much better workload discrimination.”
He observed that the right enterprise AI architecture is increasingly hybrid: reserve 10% for frontier model consumption for problems that genuinely require it, while pushing classification, extraction, summarization, routine agent actions, and other bounded workloads toward small language models (SLMs) and open-weight models running on infrastructure that the enterprise controls.
“That gives CIOs leverage against price gouging or unilateral price setting,” he pointed out.
