Executive Summary
As AI moves from experimentation to enterprise-wide adoption, infrastructure decisions will determine how effectively you can scale AI while balancing performance, cost, security, and control.
- Gartner forecasts AI-optimized IaaS spending will reach $108.6 billion by 2029, growing at a 70.9% CAGR from 2024; inference is projected to make up more than 65% of that spending.
- Deloitte reports that 72% of enterprises expect to operate AI at the edge at scale by 2028, compared with 36% today, while 88% expect at least limited AI-factory deployment.
Together, these trends show that cloud migration is only the starting point of an enterprise AI journey. To support AI at scale, you need an infrastructure strategy that balances performance, cost, data residency, security, and operational control.
Explore this article to learn how to do it.
Cloud migration made it easier to run applications and respond to changing business needs. But AI brings its own set of challenges. Simply moving workloads to the cloud does not mean your infrastructure is ready to support AI at scale.
So, what should be done? AI-optimized Infrastructure is the answer.
To support this statement, here are some insights that experts have gathered. For instance, Gartner predicts that spending on AI-optimized infrastructure-as-a-service will reach $108.6 billion by 2029, growing at 70.9% annually. Inference, which means running trained models, will make up over 65% of this spending. However, the main challenge now is running AI reliably and cost-effectively across the business.
This shift is fundamentally transforming infrastructure strategies. As with other technology domains, organizations are now carving out specialized spaces and optimizations for AI. The goal of AI-optimized infrastructure is to integrate AI where it delivers real value—complementing rather than disrupting existing systems—while minimizing extra cost, risk, or operational complexity.
In this post, you will explore why AI needs a different infrastructure approach—and how to choose the right foundation to scale it securely, efficiently, and cost-effectively.
Why AI-Optimized Infrastructure Is a Business Imperative
Before understanding the need for AI-optimized infrastructure, let’s understand why cloud-first is not AI-ready. Well, cloud environments may be scalable, but they were never built for AI. They can fulfil general needs like websites, data storage, or enterprise applications.
Talking about AI workloads, they require specialized compute, low-latency responses, quick access to large data volumes, and powerful controls over sensitive data. All these are mandates as you can face increasing costs, growing operational risk, and inconsistent performance if failed to offer these capabilities.
If you AI-optimize your infrastructure, you will get the right foundation to get started. AI-optimized infrastructure brings together computing capacity, governed data, secure access, and ongoing monitoring – allowing you to deploy AI reliably and improve it over time. As usage grows, infrastructure determines whether an AI initiative remains an isolated experiment or becomes a scalable business capability.
Related: AI Workloads in 2026: What Platform Engineering Teams Need to Own
Cloud, On-Premises, or Hybrid: Finding the Right AI Infrastructure Mix
There is no single “best” environment for every AI workload. The right model depends on what you are trying to achieve, where your data resides, how quickly your AI application needs to respond, and how much control you need over cost and security.
As you plan for AI at scale, the decision is not simply whether to use cloud or on-premises infrastructure. You need to decide where each workload should run—and how those environments can work together as demand grows.
Here are 4 best approaches to deal with this confusion. Let’s begin with the first one below:
Public Cloud
This approach is best for experimentation and is often the fastest way to start. With public cloud, you get access to flexible computing capacity, managed AI services, and the ability to test ideas without making a major upfront infrastructure investment.
Public cloud is a strong option when you are building early pilots, managing fluctuating demand, training models, or launching AI services across multiple regions. For example, a customer-facing assistant that serves users across geographies may benefit from the scale and reach of the public cloud.
However, there are some challenges involved as well. For instance, cloud costs can increase as usage becomes steady and large-scale. This approach may also be less suitable when you need strict control over sensitive data, predictable long-term costs, or near-instant response times.
So, you need to consider asking yourself the key question: Are you using cloud for the workloads where flexibility delivers the most value?
On-Premises AI
On-premises AI infrastructure gives you greater control over where data stays, how infrastructure is used, and how performance is managed. It can be a practical choice for organizations handling highly sensitive information, operating in regulated industries, or running AI workloads with consistent, high-volume demand.
This is also where an AI factory can play a role. An AI factory is a purpose-built environment that combines computing capacity, storage, networking, AI software, data pipelines, and operating processes to support AI development and deployment at scale.
Deloitte’s March 2026 survey found that 64% of organizations have already begun deploying AI factories at a limited or scaled level, while 88% are anticipated to do so by 2028.
For you, the business case is not simply owning more infrastructure. It is gaining stronger control over performance, sensitive data, and the economics of sustained AI usage.
Hybrid Cloud
A hybrid cloud is when you combine public cloud services with private cloud or on-premises AI infrastructure. Doing so enables you to utilize cloud resources for experimentation, peak demand, and large training workloads. And it will keep sensitive data or predictable inference workloads in a private environment.
For instance, you may use public cloud to test a new AI-powered service, then move high-volume inference workloads to a private AI environment once usage becomes stable. You may also keep confidential enterprise data on premises while using cloud-based tools to build or scale AI applications around it.
When you choose a hybrid cloud, you can optimize performance, cost, data residency, and control without forcing every workload into one environment. As AI becomes more embedded in business operations, hybrid infrastructure can give you the flexibility to adjust workload placement as needs change.
Related: Cloud & Infrastructure Trends 2026: Insights From 5 Industries
Multicloud
Multicloud means using services from multiple public cloud providers. Although it seems the same as hybrid, it differs from hybrid cloud: hybrid connects a public cloud with private or on-premises infrastructure, while multicloud uses multiple cloud platforms and services.
You can adopt a multicloud approach to access specialized AI capabilities, meet regional data requirements, improve resilience, or avoid becoming dependent on a single provider. However, it comes with greater complexity: a need for consistent security policies, identity controls, data governance, visibility, and cost management across every environment.
The enterprise leap to the future will depend on making these choices deliberately. You need to scale applications without overspending, align infrastructure investments with business growth, and develop the skills to run AI as an operating capability. The goal is not to standardize every workload on a single platform—it is to place each workload where it can deliver the most value with the least risk.
From Migration to AI Advantage
As AI evolves, it is making way for better collaboration between the cloud and enterprises. Considering AI-optimized infrastructure, the goal is not to build the biggest AI environment and replace the old one. On the contrary, you need to create an infrastructure strategy that enables your key AI use cases to grow, be managed, and be enhanced over time.
At Scalence, we believe that cloud migration (when initially introduced) gave you flexibility. And now AI-optimized infrastructure is here to turn that flexibility into a long-term operational advantage.
If you are planning to modernize your infrastructure but are stuck on the best approach, Get in touch with our experts, who have unbeatable expertise in infrastructure optimization.
FAQs
Why is cloud-first infrastructure not always AI-ready?
Traditional cloud environments support general applications like websites etc., but AI needs high-performance compute, fast data access, low latency, strong governance, and predictable cost management. Therefore, if you wish to keep using cloud-first infrastructure, you need to optimize it for AI.
Should enterprises use cloud, on-premises, or hybrid infrastructure for AI?
It depends on the workload. Public cloud works well for experimentation, on premises suits sensitive or high-volume workloads, and hybrid environments provide flexibility across both. So, you can choose the one which suits your enterprise needs best.
How can an enterprise assess its AI infrastructure readiness?
Start by evaluating priority AI use cases rather than selecting technology first. For each use case, assess data location and quality, compute requirements, response-time expectations, security and compliance needs, expected usage volumes, integration dependencies, operational ownership, and cost thresholds.
Or you can get in touch with our experts at Scalence to run a thorough assessment of your current infrastructure.
How can enterprises control AI infrastructure costs?
AI cost optimization requires more than reducing cloud consumption. Practical actions include right-sizing GPU capacity, scheduling noncritical training jobs, using smaller models where appropriate, optimizing model inference, tracking idle resources, setting budgets by business unit or use case, and placing workloads in the most cost-effective environment.
