DIaaS Is Exactly What Your AI Infrastructure Needs, Experts Say

Many businesses are learning that scaling AI projects beyond the pilot phase is far more complex and expensive than initially expected. The promise of automation, predictive analytics, and generative AI remains a realistic goal. But, the infrastructure needed to power these workloads remains a significant challenge.

Despite massive cloud investments, underutilized GPUs, inefficient resource allocation, and unpredictable cloud costs are draining budgets. Infrastructure teams scramble to patch inefficiencies while executives question AI’s actual return on investment (ROI).

Data Infrastructure as a Service (DIaaS) promises to change all that. Unlike traditional cloud-based infrastructure, which forces AI workloads into rigid, pre-defined structures, DIaaS dynamically assembles infrastructure based on an AI model’s needs. The result? More efficient computing, lower costs, and fewer headaches for data scientists.

Is DIaaS just another buzzword, or is it a real solution to AI’s growing infrastructure crisis? Let’s break it down.

Table of Contents Table of Contents

1. Key Takeaways
2. Why AI Workloads Are Burning Through Cloud Budgets
3. How DIaaS Turns AI Infrastructure into a Smarter System
4. The Three Pillars of DIaaS Optimization
5. AI's ROI Problem & How DIaaS Fixes It
6. Hybrid & Multi-Cloud Flexibility
7. The Bottom Line
8. FAQs
9. References

Show Full Guide

Table of Contents

1. Key Takeaways
2. Why AI Workloads Are Burning Through Cloud Budgets
3. How DIaaS Turns AI Infrastructure into a Smarter System
Show Full Guide
4. The Three Pillars of DIaaS Optimization
5. AI's ROI Problem & How DIaaS Fixes It
6. Hybrid & Multi-Cloud Flexibility
7. The Bottom Line
8. FAQs
9. References

Why AI Workloads Are Burning Through Cloud Budgets

AI’s problem isn’t a lack of computing power – it’s how poorly that power is managed. Most artificial intelligence (AI)/machine learning (ML) infrastructure is built on guesswork and overprovisioning, leading to wasted resources, ballooning costs, and underutilized GPUs.

John Blumenthal, Chief Product & Business Officer at Volumez, explained the core issue to Techopedia:

“Satya Nadella has been quoting the Jevons paradox and how when you create efficiency on some resource, you actually aren’t decreasing the demand for that resource. Your demand actually goes up.”

Many enterprises assume more GPUs equals faster AI models, but if those GPUs sit idle, companies are essentially paying for unused computing power.

Half-utilized GPUs mean companies are effectively paying double for AI processing. This inefficiency is mainly due to slow data pipelines that don’t feed GPUs fast enough – not a lack of GPUs.

AI researcher Dr. Eli David puts it more bluntly:

“I don’t care about storage. I care about GPU. I’m not getting 100% GPU utilization for many state-of-the-art models I’m training. 50% utilization means I’m paying double what I should for my GPUs.”

Enterprises are pouring billions into cloud storage and networking solutions, yet choosing the right combination still feels like solving a constantly shifting puzzle. Too much provisioning leads to wasted spend, while too little results in bottlenecks and inefficiencies.

How DIaaS Turns AI Infrastructure into a Smarter System

Unlike traditional cloud infrastructure, DIaaS doesn’t just rent out compute power – it optimizes it in real-time. Dianne Gonzalez,  Senior Director of Business Development and Product, Volumez noted that instead of looking at each one of those components as an isolated thing, they look at it from end to end. She said:

“We’re driving efficiency by really understanding all of the underpinnings of each one of those silos so that when we create our infrastructure pipeline on demand, we create a very efficient, balanced infrastructure that maximizes GPU utilization.”

By taking a workload-first approach, DIaaS avoids overprovisioning. Instead of companies guessing how many GPUs, storage, or networking resources they need upfront, DIaaS dynamically assembles precisely what’s required at that moment – then releases resources once a job is done.

The Three Pillars of DIaaS Optimization

Blumenthal also shared how DIaaS automates what was once a painstaking manual process, allowing AI teams to focus on innovation instead of infrastructure troubleshooting.

AI’s ROI Problem & How DIaaS Fixes It

Despite the excitement surrounding AI, the ROI remains elusive. Companies are spending heavily on GPUs, storage, and networking, but when infrastructure isn’t optimized, costs spiral while output remains stagnant. Blumenthal reiterated this problem in our conversation.

“Efficiency is at the core of the ability to get to the ROI, causing everything to degrade on these two precious resources that aren’t being operated efficiently. One is GPUs, and the other is the data scientists themselves.”

Instead of spending time fine-tuning AI models, data scientists are often stuck troubleshooting data infrastructure bottlenecks. DIaaS removes infrastructure management from the hands of AI teams, allowing them to focus on what matters: delivering results.

Hybrid & Multi-Cloud Flexibility

Another major AI challenge is vendor lock-in. Enterprises want the flexibility to use multiple cloud providers but struggle to manage workloads across different environments.

DIaaS allows enterprises to seamlessly integrate AI workloads across multiple cloud environments, avoiding single-vendor dependence while taking advantage of the latest cloud innovations. Gonzalez sees hybrid strategies as inevitable:

“Hybrid strategies, especially in AI, will always be there. Companies or enterprises must adopt a hybrid strategy to execute their outcomes.”

For AI teams, this means the freedom to move workloads where they perform best without getting locked into a single provider’s architecture.

The Bottom Line

AI is moving from pilot projects to full-scale production, and infrastructure inefficiencies will determine who succeeds and who will continue to struggle.

DIaaS isn’t just about making AI faster – it’s about making AI viable. For companies frustrated by wasted cloud spend, underperforming AI workloads, and overburdened data science teams, rethinking infrastructure efficiency is no longer optional – it’s essential.

The future of AI won’t be built on more GPUs and storage. It will be built on more innovative, dynamically optimized infrastructure, and DIaaS is leading the way.

FAQs