For years, Anand Naidu has been a pragmatic voice in the world of enterprise IT, advocating for a balanced approach to platform architecture even when the “cloud-first” mantra was at its peak. As an expert in both frontend and backend development, he has a deep, hands-on understanding of what it takes to build and deploy complex systems. Now, as the intense demands of AI are forcing a widespread industry re-evaluation, his long-held perspective on hybrid cloud is moving from the fringes to the mainstream. We sat down with Anand to discuss why the economics and performance needs of AI are reshaping enterprise strategy, making a thoughtful mix of on-premises, cloud, and edge deployments the new standard for success.
Given that AI workloads can generate monthly cloud bills in the tens of millions, what specific operational factors drive these extreme costs? Please walk us through the key metrics a CIO should use to determine the financial tipping point for moving a workload on-premises.
It’s a staggering figure, but those multi-million dollar bills are a reality for some enterprises, and it’s driven by the very nature of AI. Unlike traditional applications, AI workloads have an insatiable appetite for compute power and data, whether you’re training a massive model or running inference at scale. This intense resource consumption is what sends public cloud costs skyrocketing. For a CIO, the tipping point becomes a straightforward, if sobering, calculation. You have to look at your total cloud spend for that AI workload and compare it to the total cost of acquiring and maintaining an equivalent on-premises system. The moment your cloud bill starts surpassing 60% to 70% of that on-prem cost, you’ve hit the financial red line. At that point, the cloud-first argument collapses under its own economic weight, and bringing that workload in-house becomes a matter of fiscal responsibility.
For AI applications requiring response times under 10 milliseconds, the public cloud is often not viable. Can you share some real-world examples of these ultra-low-latency workloads and explain how architects should design an infrastructure that reliably meets such strict performance demands?
Absolutely. We see this demand in any scenario where an AI-driven decision has to happen in the blink of an eye. Think of a factory floor where an AI-powered camera system needs to detect a flaw on an assembly line and trigger a robotic arm to remove it instantly, or in financial services, where an algorithmic trading platform must react to market data in microseconds. These applications simply cannot tolerate the inherent delays of sending data to a distant cloud data center and waiting for a response. The network latency alone makes it impossible. For architects, the design principle is clear: you must move the computation as close to the source of the data as possible. This means designing for on-premises or even edge deployments where the AI models run locally. That is the only way to guarantee you can reliably meet those sub-10-millisecond response times required for mission-critical, real-time operations.
A “three-tier approach” using cloud for elasticity, on-premises for production, and edge for immediacy is gaining traction. How do these tiers interact in a real-world AI deployment? Please provide a practical example of a business process that would leverage all three platforms simultaneously.
This three-tier model is really about using the right tool for the right job, and they work together in a very complementary way. Imagine a large retail chain. At the edge—in their physical stores—they might deploy AI on local devices to analyze video feeds for real-time shelf stocking alerts or to power a frictionless checkout system. This requires immediate, on-site processing. The data from these edge devices, perhaps aggregated and anonymized, is then sent to their on-premises data center. This is where their core production workloads run—things like the AI models for demand forecasting and supply chain optimization, which need consistent, predictable performance and cost. Finally, the cloud serves as their innovation sandbox. Their data science teams can spin up massive GPU clusters in the public cloud for experimentation, using its elasticity to train next-generation AI models without the upfront cost of building out permanent infrastructure. Once a model is proven, it can then be deployed to the on-prem or edge tiers for production use. It’s a continuous, integrated cycle.
For years, a “cloud-first” strategy was dominant, but AI economics are causing a shift. What is the most compelling argument for communicating the value of a hybrid platform to a board that may still be committed to a cloud-only mandate?
The most compelling argument is that the ground has shifted, and AI is the earthquake. For a board committed to a cloud-only mandate, you have to frame this not as a retreat from the cloud, but as a progression toward a more mature and sustainable AI strategy. You can start by stating plainly that old assumptions no longer hold; as industry analysis now shows, “cloud-first strategies can’t handle AI economics.” You then present the hybrid model as the solution that allows the company to harness AI’s power without being financially crippled by it. It’s about optimizing for cost, performance, and security. By keeping predictable, mission-critical AI workloads on-premises, we gain control and predictability over our budget. By leveraging the cloud for what it does best—experimentation and scaling—we retain our agility. It’s no longer an ‘either/or’ decision; it’s a strategic ‘both/and’ approach that delivers the best of both worlds and ensures our AI initiatives are built on a sound financial and operational foundation.
What is your forecast for the evolution of enterprise AI platforms over the next five years? Will the balance between cloud and on-premises continue to shift, and what new technologies or challenges might influence this trend?
My forecast is that the shift toward hybrid platforms will not only continue but accelerate, becoming the default architecture for any serious enterprise AI deployment. AI has acted as a great normalizer, stripping away the dogma and forcing a return to pragmatic, workload-driven decisions. The balance will keep tilting towards a thoughtful mix, where the public cloud is a vital component, but not the only one. The biggest challenge ahead won’t be about choosing between cloud and on-premises, but about mastering the integration between them. We’ll see a greater focus on technologies that enable seamless data and workload mobility across these different environments. The new frontier will be managing security, data sovereignty, and regulatory compliance across this more distributed landscape. Ultimately, the future belongs to organizations that ignore the rhetoric and build an integrated, best-of-both-worlds platform, because that is the only sustainable path to scaling AI effectively.
