Private cloud remains a vital and stable foundation for enterprise IT
Most companies aren’t ditching their data centers just yet. That’s not surprising, and it’s not the wrong move either. Enterprises have spent years building reliable internal systems and infrastructure. They work. They’re stable. They support critical operations every hour of every day. So for many, keeping a large portion of workloads in private or on-site environments is just common sense.
Right now, we’re seeing roughly 50% of enterprise workloads still run outside the public cloud. That includes on-premises data centers and private cloud setups built on virtualization and containers. These environments aren’t disappearing, they’re adapting. Companies are leaning into them for predictable, steady-state workloads. If what you’re running doesn’t need to scale overnight or shift based on new users or new markets, then a private cloud can handle it cost-effectively, with fewer moving parts.
There’s also a deep operational familiarity with private infrastructure. Your teams know it well. Costs are stable. Governance, compliance, and system integration are easier to manage in a controlled environment. That’s especially true for highly regulated industries, finance, healthcare, defense, where data locality and control remain high priorities.
According to Forrester, 79% of large enterprises have already implemented their own private cloud platforms. These systems aren’t experiments, they’re well established. IDC expects private cloud spending globally to reach about $66 billion by 2027. So even though public cloud continues to grow rapidly, we’re not in a zero-sum game. This isn’t about picking a side, it’s about matching tools to the job.
Michael Coté at VMware calls it a “50:50 equilibrium” between public and private cloud. Makes sense. What we’re seeing is a mature, nuanced infrastructure strategy that balances speed with control. Decision-makers get that. And they’re not rushing to change something that’s working well.
Public cloud serves as the engine for innovation and dynamic scalability
You want to move fast. You want to build the next product, enter a new market, train new models, launch a feature, and iterate in days, not months. That’s the public cloud. It gives you the space to act on ideas without delay, to scale up, and pull back when you need to. Public cloud isn’t about replacing private systems, it’s about unlocking what those systems can’t offer: speed, flexibility, and access to advanced tools across multiple regions and teams.
C-suite leaders are leaning into this because the market is changing constantly. Your customer baseline from six months ago isn’t your baseline today. Trying to pre-plan all infrastructure requirements in that environment? It just slows you down. Public cloud is built to remove that friction. You need GPUs? You get them in hours. You want APIs to experiment with machine learning? They’re already there. That’s where real-time innovation happens.
According to Flexera’s “State of the Cloud” report, we’ve now passed the tipping point, more than 50% of enterprise workloads are already in public clouds. And despite some headlines about cloud repatriation, moving workloads back on-prem, the actual numbers are small. Only about 21% of cloud workloads have been pulled back. And even then, that movement is overwhelmed by the continued migration and net-new growth into public cloud.
Executives already understand that public cloud expenses need management, but they’re still directing investment into areas that make a difference. It’s not about cutting back; it’s about getting smarter with cloud budgets. And the areas seeing acceleration, like AI and advanced analytics, all point to one thing: innovation lives here. This is what you use when you’re doing something new, and need the infrastructure to keep up.
This setup aligns with how high-performing organizations move today. Stable systems sit on legacy infrastructure that’s already optimized. For everything else, market-facing apps, AI workloads, anything untested or fast-moving, you don’t wait around. You go to where iteration is faster, risk is lower, and scale is instantaneous. That’s the public cloud.
AI development critically depends on the elastic capabilities of public cloud infrastructure
AI doesn’t sit still. It moves fast, demands resources that spike, and evolves quickly. That means you need infrastructure that can scale on demand, sometimes overnight. You’re not training basic models anymore. You’re running large-scale training jobs, deploying inference pipelines, and adjusting based on real-time user feedback. That type of work can’t be locked into static infrastructure. The public cloud is built for this.
There’s no long procurement cycle and no need to forecast how many GPUs you’ll need months in advance. If you’re fine-tuning a large language model or spinning up inference jobs for new customer features, you pay for the exact compute you use, when you use it. That makes AI more accessible to teams across the organization. Engineers and data scientists don’t have to wait for resources, they iterate aggressively, push boundaries, and move fast.
This isn’t a theory. It’s backed by data. By the end of 2024, 79% of organizations reported using or experimenting with AI and machine learning services from public cloud providers. Additionally, 72% are using generative AI in some form, primarily via APIs or platforms hosted by public cloud vendors. That demand is driving business results. When boards prioritize AI, CIOs and CTOs turn to hyperscalers, not local data centers.
Matt Wood, former Data Science Chief at AWS, said it directly: if your infrastructure can’t shift with the business, you miss the moment. He’s right. AI doesn’t operate on a set schedule or run at predictable loads. What matters is throughput, response time, and scaling whenever the model or product requires it. Public cloud handles it by design.
This is why Cloud providers are investing heavily in compute. Amazon, Microsoft, and Google are all seeing AI-specific cloud revenues rising. The ecosystem is advancing aggressively, with pre-trained models, inference platforms, and high-availability GPU clusters becoming standard across regions. You don’t need to build the foundation anymore, you deploy on it.
A hybrid cloud strategy is emerging as the dominant model for enterprise IT
This isn’t about choosing public versus private cloud. It’s about using both where they make sense, and right now, that’s exactly what most enterprises are doing. Hybrid cloud is no longer just a transition tool. It’s a deliberate, long-term strategy. Critical systems that depend on stability, lower latency, or internal compliance frameworks stay on-premises or in private infrastructure. New or high-growth projects shift to the public cloud to meet speed and scale demands.
This dual setup is winning because it aligns with how businesses operate today. It avoids trying to force a single framework across every use case. Instead, it allows infrastructure decisions to be shaped by performance needs, data governance policies, total cost of ownership, and product lifecycles. That’s where leaders are increasingly focusing, on optimization and alignment, not full migration for the sake of it.
IDC and Forrester both report that hybrid cloud is becoming the standard configuration for large enterprises. This isn’t trend-chasing, it’s about operational efficiency. As of now, global spending projections suggest continued investment in both private and public stacks, not a winner-takes-all scenario. Private workloads aren’t dropping off immediately. But the innovation budgets, real R&D, AI, and customer experience initiatives, are clearly tilting toward public cloud.
For executive teams, the message is simple: align infrastructure with business velocity. Stable, compliance-heavy processes? Keep them where they run best. Building something new? Use the system that adapts, integrates globally, and lets you deploy features without delay. The goal isn’t cloud purity, it’s outcome-driven infrastructure. That’s what hybrid cloud enables, and the companies that embrace it are seeing faster iteration, better resource use, and reduced technical debt. That’s forward motion.
The evolution of public cloud infrastructure
AI isn’t just demanding in terms of compute, it’s also sensitive to latency. Applications like real-time translation, recommendation engines, autonomous operations, and voice recognition can’t afford delay. That’s why cloud infrastructure is expanding, not just in size, but in location. Edge computing is now a key pillar of public cloud strategy because centralizing every AI process in a few regions doesn’t meet real-time user expectations globally.
Public cloud providers understand the demand. They’re not only increasing capacity but pushing that compute closer to end users. What this enables is high-performance AI inference that happens near the point of interaction, not hundreds or thousands of kilometers away. That shift matters for any use case requiring low-latency performance in large or distributed user networks. Global footprint isn’t just a benefit, it’s becoming a requirement.
Cloudflare is building toward that vision aggressively. Known originally for its network and security stack, it’s now deploying NVIDIA GPUs in more than 100 cities worldwide. That rollout allows developers to run AI inference tasks on the edge, serving users where they are, in real time. Seeing Cloudflare evolve from infrastructure optimization into AI execution at the edge is an indicator of where the industry’s headed: fast deployment, decentralized processing, and smarter resource distribution.
CIOs and CTOs evaluating where to run next-generation workloads need to factor this kind of edge capability into infrastructure strategy. It impacts user experience, system responsiveness, and operational efficiency, especially for product lines that can’t be constrained by centralized data center architecture.
The most forward-looking companies aren’t waiting. They’re already integrating edge infrastructure into their cloud architectures, either directly with the hyperscalers (Amazon, Microsoft, Google) or through edge-first networks like Cloudflare. That’s not a minor shift. It’s a model recalibration. As applications become increasingly intelligent and interactive, deployment needs to happen not just fast but also close to the user. Edge is how that happens at scale, and the cloud platforms ready for it are leading the next phase of enterprise computing.
Key executive takeaways
- Private cloud remains critical but limited: Private cloud continues to support nearly half of enterprise workloads, offering stability, predictable costs, and control, especially for regulated environments. Leaders should maintain these systems for steady workloads but not rely on them for innovation.
- Public cloud drives scale and speed: Public cloud infrastructure is the go-to environment for rapid iteration, global scalability, and on-demand resource provisioning. Executives should prioritize it for initiatives requiring fast deployment and high growth potential.
- AI depends on cloud elasticity: AI workloads require burstable, high-performance compute that on-premises systems rarely support efficiently. Leaders investing in AI should focus resources on public cloud platforms that offer scalable GPUs, model APIs, and agile development environments.
- Hybrid cloud is the strategic norm: Enterprises are standardizing on hybrid models to balance control with flexibility. Decision-makers should align workloads with infrastructure that matches their performance, scalability, and compliance needs.
- Edge is reshaping AI deployment: Cloud providers are extending compute power to the edge to reduce latency for AI inference and improve real-time performance. Leaders building latency-sensitive or globally distributed applications should incorporate edge strategies into their cloud architecture.
