Dedicated servers outpace public clouds for AI

AI systems are computational beasts with costs that can easily spiral out of control in a public cloud environment. This isn’t hypothetical; the data backs it up. Nearly half of IT leaders in a recent survey reported unexpected cloud-related costs ranging from $5,000 to $25,000, with AI workloads being a common culprit. These workloads demand massive amounts of cloud-based compute power, storage, and real-time data processing, all of which are billed dynamically.

The core promise of the public cloud—“pay only for what you use”—can become a double-edged sword for AI. High-performance AI systems rely on specialized hardware, such as Nvidia GPUs or TPUs, which are costly to rent and often underutilized without continuous workload optimization. Furthermore, many AI tasks require scaling across fleets of compute instances, a process that generates additional expenses for network traffic, storage retrieval, and latency reduction.

Some enterprises are also finding that the flexibility of the cloud comes at the cost of predictability. As 32% of IT professionals have pointed out, many existing cloud resources are underutilized or wastefully allocated due to the fear of under-resourcing mission-critical AI tasks. This is frustrating, given that budgets are squeezed tighter than ever.

Dedicated servers offer a more predictable and stable pricing model. Leasing or buying physical servers for AI workloads provides enterprises with full control over the hardware, eliminating hidden costs and surprise bills at the end of the month. IT leaders are increasingly viewing this model as more cost-effective and better suited to provide a clear return on investment.

Control, security, and AI infrastructure

The need for better control and tighter security is also accelerating the move to private, dedicated servers. AI systems thrive on data, which is often sensitive and proprietary. Enterprises are growing wary of entrusting such a crucial asset to public cloud providers. The fear of accidental data exposure, breaches, or even non-compliance with data protection regulations can outweigh the benefits of outsourcing infrastructure to loosely managed public clouds.

In industries such as finance, healthcare, or government, dedicated hardware is almost a no-brainer. These organizations rely on strict compliance with regulations like HIPAA, GDPR, or PCI DSS. Their sensitive data must not cross jurisdictions or become entangled with other tenants in shared public cloud environments. The report from Liquid Web revealed that government (93%), IT (91%), and finance (90%) are at the forefront of dedicated server adoption.

Another issue is that AI systems often require IT staff to fine-tune workflows and infrastructure to maximize efficiency, which is only possible with granular control. IT professionals highlight this as a key advantage of private environments. Dedicated servers allow organizations to customize performance settings for AI workloads, whether that means optimizing servers for large-scale model training, fine-tuning neural network inference, or creating low-latency environments for real-time application predictions.

With the rise of managed service providers and colocation facilities, this control no longer requires organizations to purchase and install physical servers themselves. The old days of building and maintaining in-house data centers may be over, but physical infrastructures are far from extinct. Instead, most enterprises are opting to lease managed, dedicated hardware and have the responsibility for installation, security, and maintenance fall to professionals who specialize in running robust server environments. These setups mimic the operational ease of the cloud while providing IT teams with deeper visibility into and greater authority over their computing resources.

The performance edge of private servers

Performance is a deal-breaker in AI, and latency isn’t just an inconvenience—it directly impacts business outcomes. Many AI systems, particularly those focused on real-time decision-making, recommendation engines, financial analytics, or autonomous systems, require microsecond-level response times. Public clouds, although designed for scalability, introduce unavoidable latency due to the publicly shared infrastructure’s multitenancy and potential geographic distance from users or data sources.

In contrast, dedicated physical servers are often located closer to the data sources or users that drive AI operations. Organizations can use colocation providers or on-premises edge facilities to place hardware near key geographic areas, reducing the number of network hops and decreasing latency. Network performance is further enhanced by eliminating the overhead caused by shared cloud networking, which can be unpredictable during busy periods when other tenants compete for resources.

By consistently maintaining high performance, private infrastructures significantly improve the feasibility of scaling AI from small projects to mission-critical systems. Furthermore, as AI models become increasingly complex—some now exceed a trillion parameters—the performance of private servers explicitly designed for high-speed computation has become essential, not optional.

A hybrid public-private strategy

Although the transition toward private infrastructure is clearly underway, the public cloud remains relevant. Enterprises continue to use public clouds for specific AI tasks, such as testing new models, integrating externally available AI APIs, or running non-critical systems. Public clouds excel at rapid scalability and often serve as platforms for innovation, particularly during the iterative training phases of AI development.

However, as these projects mature and transition into long-term production, enterprises find that controlling costs, maintaining compliance, and delivering optimal performance require a different approach. For many, it’s not about choosing between public clouds and private servers; it’s about finding a balance. The public cloud works best as part of a hybrid strategy where its elasticity complements the stability and control of private infrastructure.

It’s also worth noting that even private infrastructure doesn’t necessarily require companies to have physical servers. With colocation and managed services, enterprises enjoy the benefits of dedicated hardware without the need to build or manage their own data centers.

The idea of “everything in the cloud” is shifting toward a more practical, individualized approach. Almost half (45%) of IT professionals expect that dedicated servers will become even more important by 2030, evolving from a traditional backbone to a key element of AI-driven innovation.

The future of enterprise infrastructure is hybrid. Public clouds and private servers complement each other. The public cloud will continue to lead innovation in experimentation and scalability. However, when costs and performance are most critical—especially for resource-heavy AI systems—the dedicated server is reemerging as a quiet powerhouse driving enterprises forward.