Does public cloud AI cost too much?

In today’s rapidly changing digital landscape, artificial intelligence remains the most transformative technology on the horizon. As organizations seek to integrate AI into mission-critical operations, the question of where to run these workloads is becoming increasingly consequential. The placement of an organization’s AI isn’t just about performance but also the bottom line.

Based on the latest research and decades of hands-on observation, the message is clear: On-premises infrastructure—whether in your data center or a colocation facility—now offers the best value for building and scaling AI initiatives. This shift is driven by the rapid decline in hardware prices during the past decade while public cloud cost structures have remained largely unchanged.

The cloud cost cliff

Deloitte’s recent findings identify the “public cloud cost cliff” as one of the most pressing issues for enterprises running AI at scale. They reveal a crucial inflection point: When cloud expenses for AI workloads hit 60% to 70% of the cost of dedicated infrastructure, the numbers begin to favor private infrastructure sharply.

Initially, the promise of the cloud is compelling, offering immediate scalability, rapid provisioning, and managed services. However, as organizations transition from pilots and proofs of concept to production-grade, steady-state AI, cloud costs can escalate rapidly, sometimes far exceeding initial forecasts.

Resource-intensive AI training or inference jobs in the cloud can trigger unexpected, fluctuating bills, often leaving finance teams scrambling for answers. Moreover, AI workloads tend to be “sticky,” consuming large volumes of compute that require specialized GPUs or accelerators, which come at premium prices in the cloud. Today, those same components are much cheaper to buy directly than they were 10 years ago, essentially reversing the previous equation.

The economics of hardware costs

A decade ago, acquiring advanced hardware for AI was costly, complex, and risky. Organizations faced long procurement cycles, supply chain volatility, and the daunting challenge of maintaining bleeding-edge gear. Public cloud was the solution, offering pay-as-you-go access to the latest GPUs and accelerators, with none of the upfront costs.

Today, the situation has flipped. Hardware costs have plummeted, thanks to advances in manufacturing and fierce competition. Modern GPUs, storage, networking, and even advanced AI chips are now widely available, and their cost per unit of performance has dropped dramatically. For the price of a few months’ worth of cloud bills, enterprises can now purchase, rack, and operate high-performance AI infrastructure suited exactly to their unique workloads.

Deloitte’s research confirms what many IT leaders have long suspected: The impact of lower hardware prices is not offset by corresponding drops in public cloud pricing. For all but the most minor or transient workloads, maintaining cloud AI infrastructure cannot compete with the value and long-term financial predictability of on-premises or colocation options.

In addition to cost savings, recent research shows several operational and strategic benefits of on-prem infrastructure:

Workload customization: Enterprises can precisely tailor hardware and update it as needed to match specific AI demands, creating tightly optimized, high-throughput environments that are not possible in generic public cloud setups.

Edge computing and latency: Many AI applications, particularly those requiring real-time insights or operating at the edge (factories, healthcare, autonomous vehicles), cannot tolerate the latency of shuttling data to remote cloud data centers. On-premises and colocation solutions positioned closer to the physical point of action deliver those crucial milliseconds.

Data sovereignty and security: Keeping sensitive information within your own controlled environment simplifies compliance with regulatory mandates, reduces exposure to external threats, and provides security teams with greater peace of mind.

Operational predictability: By avoiding the phenomenon of “surprise cloud bills,” enterprises gain a clear, multiyear view of total cost of ownership (TCO).

Define value and TCO early

Failing to define TCO rigorously can mean the difference between success and avoidable mistakes with costly repercussions. AI infrastructure decisions often involve millions or even tens of millions of dollars. Too often, rushed or incomplete analysis causes enterprises to over-commit to the cloud, only to face spiraling costs and subpar performance down the road.

A comprehensive TCO analysis must include not only hardware and data center costs, but also power, cooling, administrative overhead, refresh cycles, and support costs over the infrastructure’s lifespan. It’s crucial to account for the cost of data gravity and migrations. Moving petabytes between cloud and on-prem environments can be both technically complex and financially overwhelming.

The cost of getting the equation wrong is significant. Deloitte’s analysis—and the experience of many CIOs—show that poor platform choices can lead to overspending by millions of dollars, weakening competitive advantage, and trapping enterprises in rigid operational models. When you see a platform decision as a one-time cost rather than a long-term, multifaceted investment, you risk overlooking its full impact on the business.

AI infrastructure is more than just a background element; it is the driving force of innovation and efficiency. It will influence how fast you can train models, how securely you handle proprietary data, and how cost-effectively you expand new applications. The public cloud still plays a crucial role, particularly for early-stage development and unpredictable workloads; however, the era of one-size-fits-all cloud solutions for AI is coming to an end. Thanks to falling hardware costs and growing operational maturity in private and colocated deployments, enterprises now have more control and can achieve better returns by investing in infrastructure they own and customize.

Before you make your next big AI platform decision, take a step back to look at the big picture. Define what long-term value means for your organization. Run the numbers and test the scenarios. In today’s environment, tens of millions of dollars are on the line. With the right strategy, you can ensure your AI endeavor is powerful, sustainable, predictable, and a valid driver of enterprise value.