The monthly cloud bill used to be the hard part. Then artificial intelligence arrived, busting budgets like a wrecking ball.
IT cost optimization is about minimizing spending. It has expanded to ensuring every AI workload runs in the environment that delivers the best combination of performance, governance and cost, according to IT experts. This is making FinOps an essential discipline for all hybrid multicloud operations.
“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 FinOps tools have to have insights into your training cost and inference cost," Mayank Gupta, former director of product marketing at Nutanix, said in a podcast interview with The Forecast.
With the rise of agentic AI, new technologies like gateways that help IT teams control AI traffic and token costs. However, the same foundation enterprises built to manage a hybrid multicloud is now positioning them to absorb AI. Workload placement, data mobility and a single operations layer were once the discipline of hybrid cloud. Now those capabilities are becoming the blueprint for what amounts to hybrid AI: the ability to run training in one environment, inference in another and data where sovereignty and cost demand it.
Many IT cost optimization strategies are built around new cloud capabilities and modernized systems built on hyperconverged infrastructure that consolidates components and virtualizes data centers, enabling system scalability and governance. A modernized data center can evolve into hybrid multicloud operations, allowing applications and data to be placed on-premises or across different public clouds to meet specific business needs. Turning to unified data services, automation and infrastructure-as-a-service offerings can help IT teams harness new innovations while managing complexity and operational costs.
AI has introduced a new and far less predictable cost curve: GPUs, inference at scale, data movement and the compliance overhead of keeping sensitive data in the right place, explained Eddie Ryan, Senior Competitive Economics Strategist, Nutanix in a blog post.
“The answer increasingly comes down to two complementary capabilities: FinOps with architectural freedom,” Ryan explained. “FinOps provides the financial discipline needed to understand and optimize cloud spending, while architectural freedom enables organizations to respond to those insights by choosing, moving, or modernizing workloads without being constrained by a single platform or vendor.
What Is Multicloud Cost Optimization in the Age of AI?
It is the practice of placing each AI and traditional workload where it delivers the best blend of performance, governance and cost, with FinOps as the operating discipline. Multicloud cost optimization refers to the practices and strategies that help keep those costs in check or ensure the organization gets a greater return on investment for the amount it spends.
Operating in a multicloud environment comes with costs, including cloud services and the need for multiple APIs. In an article for the Walmart Global Tech blog, software engineer Rupesh Patel poses the question, "Enterprises are spending huge money on cloud services, the bigger question here is: are enterprises spending wisely, optimally?"
Without cost optimization measures in place, organizations operating in the multicloud can face challenges such as compatibility issues, lack of flexibility and suboptimal data protection, all due to poor spending diversification. Even so, multicloud strategies can enhance scalability and reduce vendor lock-in over time, Ryan explained.
“Standardize operations,” he advised. “Unify the operating model. A single management and data plane across private and public clouds lets teams reuse existing skills, tools and processes instead of maintaining a different playbook for every environment.”
How Does Hyperconverged Infrastructure Lower Cloud Costs?
HCI collapses separate servers, storage and networking into a single software-defined layer, cutting the upfront cost of buying and managing discrete IT components.
Hyperconverged infrastructure is software that combines physical servers, storage and networking, eliminating pain points common in legacy infrastructure. This enables more seamless integration of on-premises data centers with public clouds, creating a hybrid multicloud environment.
HCI can be a central aspect of reducing IT costs by eliminating the upfront costs of purchasing and managing separate IT components.
“Reducing operational complexity is itself a powerful cost optimization strategy,” Ryan explained. “Repeatable automation can reduce infrastructure deployment from hours or days to minutes or seconds while eliminating many of the manual operational tasks that quietly consume IT budgets.”
What Role Does Cloud-to-Edge Management Play in Cost Optimization?
A universal cloud operating model that spans public clouds, on-premises, hosted and edge computing environments breaks down silos and keeps distributed deployments cost-efficient.
Innovations in hybrid multicloud are also empowering edge computing. Centralized data center operations that connect to cloud services and remote offices can face latency and cost challenges for businesses. The ability to replicate a data center and run that same IT environment in remote locations can bring efficiencies, especially when it comes to security, application updates and other functions that can be executed in a coordinated, uniform manner.
For the most time-sensitive applications like self-driving cars or industrial automation, edge computing is emerging as an important low-latency environment to run AI workloads too.
“The reason we push AI to the edge is because that's where the data is,” Steve McDowell, chief analyst and founder at NAND Research, told The Forecast.
McDowell’s report, Taming the AI-enabled Edge with HCI-based Cloud Architectures, explores the impact of extending IT resources to the edge and the driving force of AI, particularly in areas like image recognition for retail, manufacturing and other industries.
The AI edge differs from traditional edge deployments by requiring greater compute cycles and data management, as mentioned, as well as in software lifecycle maintenance and security requirements, according to McDowell.
“Traditional edge computing involves things like point-of-sale systems in retail,” McDowell explained. “Once we start putting AI in, then suddenly we have processing requirements that can require AI accelerators.”
In a distributed multicloud environment, the underlying architecture facilitates compliance, performance and edge deployments. The presence of a centralized cloud-to-edge management solution can help manage multicloud costs through system optimization a control across on-premises and cloud services.
According to Gartner, many organizations are evaluating a distributed cloud model as part of their cloud migration strategies for its ability to meet sovereignty, latency and network bandwidth requirements. There are cost challenges to consider when choosing a model that incorporates the edge, but the right cloud-to-edge management tool addresses them head-on and keeps costs favorable.
How Does Cost Governance Keep Cloud Bills in Check?
Cost governance tools meter total cost of ownership, generate consumption reports and automatically rightsize resources so spending tracks to observable demand.
Cost governance refers to an organization's initiatives toward retaining visibility over spending, reducing costs through measures such as automation, and controlling expenses by allocating resources based on observable consumption patterns. It is also a smart strategy that business leaders can follow in optimizing cloud costs at the private, public and multicloud levels.
The Cost Governance tool on Nutanix Cloud Manager is a platform-based solution that can simplify multicloud cost optimization. Adopting this type of software solution in a cost governance strategy yields the benefits of metered TCO calculations, automatically generated cloud consumption reports and automated resource rightsizing.
"It is essential to monitor the cloud resource utilization and configuration on a regular basis, clean up the unused resources, and remove any storage volumes that are no longer in use," Patel pointed out on the Walmart Global Tech blog. Monitoring, as well as the actions a business takes as a result of that monitoring, is a crucial process in cost governance and cost optimization overall.
Why Is FinOps the New Standard for AI-Era Cloud Spending?
FinOps unites engineering, finance and leadership around shared unit economics, turning cloud spend from a surprise into a forecast and making optimization continuous.
FinOps brings engineering, finance and business teams together to coordinate and strategize cloud usage. With business value in mind, each department is responsible for its own cloud spend and must communicate throughout the company to ensure optimal efficiency. As AI applications scale, FinOps promotes visibility, ensuring company-wide transparency and accountability with cloud usage, helping determine which workloads deliver the most value and where the best optimization opportunities exist.
"Employed well, FinOps changes behaviour at three levels," Ryan explained. "It gives engineers unit economics, the cost per customer, per transaction or per model inference, so technical decisions carry a visible price tag. It gives finance a credible forecast instead of a surprise and it gives leadership a shared language for trading spend against speed and risk."
98% of FinOps teams now manage AI spend, according to the State of FinOps 2026 Report.
AI itself can even aid modern FinOps efforts to autonomously identify underutilized resources, recommend rightsizing, detect anomalous spending, forecast trends and continuously optimize workload placement across hybrid multicloud environments. Ultimately, FinOps enables organizations to continuously evaluate placement decisions and make optimization an ongoing practice instead of a periodic occurrence.
How Is AI Reshaping Cloud Economics?
AI shifts workload placement toward data sovereignty and inference cost, sending regulated workloads to private cloud and experimental ones to public cloud.
Artificial intelligence has fundamentally transformed where workloads run. Not all machine learning workloads belong in the public cloud. If an AI application is processing proprietary data or handling information with strict regulations, the private cloud may better ensure security and compliance. With vast quantities of information feeding AI algorithms, data sovereignty must drive placement decisions.
"Regulation, contractual obligations and sector rules increasingly dictate not just how data is protected but where it may physically reside," Ryan said. "A global bank serving customers across dozens of jurisdictions, or a distributor operating in more than forty markets, cannot simply send everything to the nearest hyperscaler region. Sovereignty is now an economic input."
However, frequent experimentation or AI workloads without those constraints may be better served by public cloud resources. In fact, public cloud spending is projected to be about $1.03 trillion in 2026, according to Forrester's Public Cloud Market Outlook.
Inference costs, or the expense of running prompts through trained models to generate meaningful outputs, are increasingly variable as workload complexity, hardware choices and token costs and volume dictate all different kinds of spends.
What Makes a Platform Built for Multicloud Cost Optimization?
The right platform lets IT teams evaluate equivalent cloud services on cost, security, compliance and governance and place each workload where it fits best.
It is important to choose the right service across cloud providers to meet particular needs, Patel explained.
"The field has a lot of competitors," he wrote. "It is essential to understand the use case and specific technical requirements, various cloud providers offer more or less similar managed services for the job but evaluate each equivalent service in terms of Cost, Security, Compliance, Governance, etc."
The need for multicloud cost optimization stems from an IT landscape marked by unprecedented and ever-growing complexity. As businesses scale AI across industries and environments, cost optimization becomes a continuous discipline rather than a one-off exercise. FinOps provides the operational framework for companies to balance technological innovation with financial accountability, to help organizations place AI workloads most efficiently, maximize infrastructure utilization and ensure that every dollar generates business value.
Perhaps the future of multicloud optimization is not just spending less, it is spending smarter. In this emerging era, organizations that combine hybrid multicloud flexibility with FinOps will be best positioned to scale AI initiatives sustainably.
Ryan put it like this: The strategic question for the next renewal cycle is no longer, "How much will this cost to renew?" It is, "Do we have the financial visibility and architectural flexibility to run every workload, including AI, where it makes the most economic sense and can we change that decision as technology and pricing evolve?"
In the age of Enterprise AI, competitive advantage will belong to organizations that treat infrastructure as a strategic financial decision, using FinOps and hybrid multicloud to optimize not just cloud spend, but the economics of the entire digital estate.
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This is an updated version of the original article by Michael Brenner published in August 2023.
Chase Guttman updated this article. Find him at chaseguttman.com or @chaseguttman.
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