The Hidden Costs of AI: Why PVC Might be a Cost-Saving Solution

AI is one of the most promising forces shaping the future of business — but for many organizations, it is also becoming one of the most expensive. I often speak with IT leaders who are surprised at how quickly their AI costs spiral as models move from proof of concept into production. What looked like an affordable project during initial development can become a significant budget item once deployed at scale.

One of the key decisions that affects AI cost — and one of the most frequently overlooked — is where those workloads run. While public cloud is ideal for the bursty, flexible demands of AI model training, it isn’t always the most cost-effective home for long-running inference, sensitive data operations or AI services with steady, predictable usage patterns.

This is where private cloud can make a measurable difference. In this post, I’ll share what I’ve learned about the true cost drivers of enterprise AI, and why more of our customers are shifting key workloads to private cloud to improve cost control.

Why are AI costs so difficult to predict?

AI is not a typical enterprise workload. It draws heavily on high-performance GPUs, generates large volumes of intermediate and long-term data, and often involves significant movement of data between storage systems, compute environments and production applications. At first, these demands may seem manageable, but as usage scales, hidden costs emerge:

• Compute: AI inference workloads often require sustained GPU or specialized hardware access. Paying for these resources hourly in the public cloud can become costly over time.
• Storage: Large datasets used for AI training and inference need persistent storage. The longer this data lives in object stores or block storage, the higher the associated costs.
• Data transfer: AI systems frequently move data between services, clouds and locations. In public cloud, this movement triggers egress fees that can add up quickly.
• Licensing: Proprietary AI frameworks and advanced model capabilities sometimes come with licensing costs that are stacked on top of infrastructure charges.

In other words, public cloud is fantastic for agility and scale, but the pay-as-you-go model can make costs balloon as AI matures into production.

How private cloud helps you take control

For many of our customers, the pivot to private cloud for AI workloads is driven by a desire for greater financial predictability and more deliberate resource optimization. Private cloud enables this in several important ways.

First, pricing is more predictable. Instead of variable hourly billing, private cloud typically offers fixed or reserved-cost models. This makes budgeting for AI infrastructure much more straightforward, especially for steady-state inference workloads.

Second, data locality matters. By keeping data and inference close together in private cloud, you can avoid costly data egress charges — a major hidden cost in the AI pipeline that requires constant data exchange.

Third, private cloud gives you greater flexibility in how you manage storage. Rather than being locked into a single cloud provider’s tiered pricing, you can tailor your storage strategy to match the lifecycle of your AI data.

In addition to cost and data control, private cloud offers advantages in customization and security. With full control over infrastructure configurations such as GPU partitioning and workload isolation, you can fine-tune environments to meet workload-specific needs. Private cloud also supports enterprise-grade security policies and can make it easier to meet compliance requirements. This is especially important for safeguarding sensitive models and datasets in environments where shared infrastructure may introduce variability or limit configuration options.

Finally, many companies are finding they can repurpose existing hardware investments, such as on-premises GPU servers, as part of a private cloud strategy. This helps to reduce capital expense while extending the useful life of infrastructure.

How to balance public and private cloud for cost-efficient AI

Private cloud is not an all-or-nothing proposition — nor should it be. In fact, some of the most cost-efficient and scalable AI architectures I see today take advantage of both public and private cloud. A thoughtful hybrid strategy allows you to align each part of your AI pipeline with the environment that delivers the best balance of cost, performance and control. For example:

• Use public cloud for training large foundation models, where elasticity and scale are critical
• Run inference workloads in private cloud, especially those that must operate continuously, serve customers in real time and handle regulated data
• Architect data preparation and storage pipelines in private cloud to take advantage of predictable costs and strong data control

The goal is not to choose between public or private cloud, rather it’s to design an AI architecture that uses each where it delivers the most value.

Where private cloud delivers the most value for AI

In my experience, certain AI workloads deliver far better value when run in private cloud, especially when cost control and data management are top priorities.

• High-volume inference: Applications like chatbots, recommendation engines and fraud detection systems, which serve millions of requests per day
• Data-sensitive AI: Workloads that must comply with strict data residency or regulatory requirements
• Predictable, steady-state AI workloads: Business processes that run at consistent volumes, where predictable costs are valued over flexibility

If your organization is beginning to feel the weight of AI costs, it may be time to consider whether these types of workloads would be better served in a private cloud environment.

Driving sustainable AI value with the right cloud mix

AI’s value is unquestioned, but so is its cost. The most effective AI leaders I work with today are thinking beyond performance and accuracy; they are also optimizing for financial sustainability.

Private cloud gives organizations a powerful lever to control the hidden costs of AI, especially as models move from pilot to production. By choosing the right mix of public and private infrastructure, you can balance innovation with cost efficiency — and keep your AI initiatives on a path to long-term business value.