By Kevin Cochrane, CMO of Vultr
The biggest barrier to adopting agentic AI in the enterprise used to be the agents’ live-wire novelty. Now, it’s the cost to run them.
Spurred by aggressive global demand for capacity across GPUs and CPUs, the great AI buildout is commoditizing AI compute, precluding millions of growing businesses from the opportunity to modernize their infrastructure and advance their AI initiatives. Every leg of the infrastructure supply chain is racing to accommodate demand, but constructing data centers, installing hardware, and making that hardware operational is a lengthy, expensive, and uncertain process. Specialist AI infrastructure providers are now rushing to fill the gap. To do so, they’ll have to provide the capital, capacity, and capabilities to quickly build and scale AI infrastructure that supports not only frontier model developers, but also broader enterprise adoption globally.
According to a new survey from Deloitte, 74% of enterprise leaders expect to redesign at least half their business processes around agents over the next four years. Unsurprisingly, that same survey found that 67% of leaders are worried about cost and complexity holding them back.
As enterprises reinvent their IT infrastructure for agentic workloads, one golden metric could spell the difference between agentic readiness and a failed investment: performance per dollar per agent.
Despite the name, it’s a bit more complicated than “bang for your buck.” Performance is dynamic, and the performance demands of agentic AI are unprecedented for even the most technologically advanced enterprises. With new possibilities, use cases, and risks emerging almost daily, the agentic playbook is being written – and rewritten – in real time. Ensuring continuous performance at a sustainable cost gives enterprises the ability to adapt to new innovations and efficiencies without significant disruption.
Enterprises that expend all their budget on uneconomic infrastructure will find they cannot keep their agents running. Instead of unlocking productivity gains and new revenue streams, they will put themselves further in the hole. Prioritizing performance per dollar per agent creates a logical framework for their entire infrastructure rebuild, from support for agentic systems to more efficient options for traditional enterprise workloads. While it’s not the only metric that matters – security and stability are also top of mind for leaders – it is the one that opens the path to cost reduction without sacrificing the businesses advantages of agentic adoption.
Defining performance-per-dollar-per agent
Agents represent a unit of work to be done. Agents reason through a plan of execution to accomplish a goal. Businesses that are re-engineering processes or re-arming their workforce are paying for agents to accomplish a goal to do real work. If that work is done quickly and cost-efficiently, when the next unit of work comes down the line, the business has the funds available to pay for it; the work continues seamlessly, at maximum throughput. Conversely, if the work is slow and costly, a fixed budget limits the amount of work that gets done – productivity and profitability tank. Agents become a burden instead of a benefit.
To keep agents running sustainably, the underlying infrastructure must be performant and cost-efficient because the cost of the infrastructure determines the majority of agent operating costs. The costs of amortizing the build of the agent or offloading agent management to development or IT teams are minimal by comparison. The definitive metric, then, is how many dollars an enterprise is spending to complete work using an agent. Just like you measure employee productivity and the performance per dollar for units of your labor force, the same standard applies to maximizing performance per dollar per agent – hardly surprising, when many organizations have taken to treating agents like virtual employees.
Driving agent performance
Agent performance is about how quickly and reliably an agent can reason through a task, call the right tools, and converge on a correct outcome. Enterprises often over-index on model size or GPU count while ignoring the actual infrastructure bottlenecks that slow agents down: network latency between compute and data, memory bandwidth constraints during multi-step reasoning, and orchestration overhead when agents hand off subtasks.
The best results arise when performance is treated as a system-level property, not a single-chip specification. Performance gains increasingly come from matching workloads to the right hardware, whether that’s GPUs for heavy inference or CPUs for lighter-weight orchestration and retrieval tasks. As agentic workflows grow more complex, involving multiple agents coordinating in real time, the infrastructure needs to keep pace at every layer, or the fastest model in the world will still bottleneck at the slowest link in the chain.
Driving agent cost efficiency
Cost efficiency is achieved by eliminating waste, rather than cutting spend. Often, enterprises over-provision for peak demand that rarely materializes, when they could be right-sizing their infrastructure to actual agent workloads. They could also have idle capacity sitting on the balance sheet, or a hardware mismatch that’s wasting compute resources on processes that don’t need them. Not every agentic task needs a top-tier GPU; many orchestration, retrieval, and lighter-inference steps run more economically on CPUs, freeing GPU capacity for the workloads that truly need it. Flexible, consumption-based infrastructure models let enterprises scale compute up or down with actual agent demand instead of locking into fixed capacity that becomes a sunk cost. Every dollar spent should map directly to units of agentic work completed, not idle potential.
Strategies for building high-performance AI infrastructure
Enterprises today need to move beyond the mad rush to secure and provision GPUs. Instead, they should be thinking about boosting revenue, driving margin, and unlocking new competitive advantages. This means focusing on outcomes and building systems that do real work both quickly and cost effectively to achieve superior results. This is where performance per dollar per agent becomes the core strategic metric: How is the joint investment in CPU and GPU infrastructure enabling more agents to do more?
Prioritizing performance per dollar per agent creates a logical framework for the entire infrastructure rebuild, from support for agentic systems to more economical options for traditional enterprise workloads. It’s not the only metric that matters, security and stability are also top of mind for leaders, but it is the one that opens the path to cost reduction without sacrificing the business advantages of agentic adoption.
The enterprises that win in this next phase won’t be the ones that spend the most on infrastructure, but the ones that spend the most strategically, building agentic systems that are fast enough to compete, efficient enough to sustain, and secure enough to trust with real work. More than simply a metric to track, performance per dollar per agent is the discipline that makes agentic AI viable at scale.
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ABOUT THE AUTHOR
Kevin Cochrane is the Chief Marketing Officer of Vultr where he is working to build Vultr’s global brand presence as a leader in the independent Cloud platform market. He is a 25+ year pioneer of the digital experience space. Kevin co-founded his first start-up, Interwoven, in 1996, pioneered open-source content management at Alfresco in 2006, and built a global leader in digital experience management as CMO of Day Software and later Adobe. Kevin has also held senior executive positions at OpenText, Bloomreach, and SAP.
