Today we’re joined by Dominic Jainy, an IT professional whose extensive expertise in artificial intelligence and machine learning provides a unique lens through which to view one of the most dynamic shifts in the tech landscape: the rise of the neocloud. As enterprises move from AI experimentation to full-scale deployment, these specialized cloud providers are challenging the dominance of traditional hyperscalers, creating a market projected to grow an astonishing 69% annually. We’ll explore the strategies neoclouds must adopt to win over enterprise CIOs, the critical services they need to build, and the existential questions they face about specialization versus broader appeal.
With neocloud revenues projected to grow 69% annually, what specific strategies must providers implement to capture this growth? How can they balance aggressive, competitive pricing against the need for sustainable, long-term profitability? Please share some key metrics they should be tracking.
That 69% figure is staggering, and it creates an incredible land-grab opportunity. The primary strategy, and the one that gets them in the door, is undeniably aggressive pricing. When you can undercut the established giants, you give enterprise buyers a compelling reason to pay attention. But price alone is a race to the bottom. The real key to sustainable growth is to pair that cost advantage with genuinely differentiated, simplified services. They need to find a niche where they are not just cheaper, but better for a specific job, particularly high-intensity AI workloads. To balance this, they must obsessively track their customer acquisition cost against the long-term value of that contract, ensuring the initial discounts don’t cripple future profitability. They also need to monitor their GPU utilization rates like a hawk; idle, expensive hardware is the fastest way to burn through capital.
Many enterprises are now moving beyond their first few AI pilot projects to full-scale deployment. What practical steps can a neocloud provider take to convince a CIO at this inflection point to shift strategic AI workloads away from an established hyperscaler relationship?
This inflection point is the single most important moment for a neocloud provider. A CIO who has maybe run five to twenty pilot projects and only seen one to three make it to deployment is now facing a strategic decision. The conversation has to shift from “Can we do AI?” to “How do we do AI at scale, efficiently and cost-effectively?” The first practical step is to present a crystal-clear value proposition. Don’t just show them a lower price; show them a performance benchmark on their specific model that blows the hyperscaler out of the water. Secondly, acknowledge the reality of hybrid cloud. Over 80% of cloud buyers are modernizing their strategies, so you aren’t asking them to rip and replace everything. Instead, frame it as picking the right tool for the right job—a specialized, high-performance environment for their most demanding AI workloads, while their general-purpose computing can stay put. Finally, make the transition feel less risky by offering deep technical support and simple, transparent contracts. You have to remove as much friction as possible.
The long-term health of the neocloud market is often seen as dependent on enterprise adoption. How can a leading provider, whose primary customers are currently other hyperscalers or AI labs, successfully pivot its go-to-market and distribution strategy to attract a more diverse base of enterprise customers?
This is the existential question for providers like CoreWeave. Being the go-to infrastructure for giants like Microsoft and OpenAI is a fantastic way to build scale and credibility, evidenced by Microsoft spending billions and OpenAI signing a contract worth over $22 billion. However, it’s also a high-risk concentration. The pivot to the enterprise requires a fundamental shift in mindset and operations. Their go-to-market strategy has to evolve from a few large, bespoke deals to a broader, more scalable sales motion targeting enterprise IT departments. This means building a traditional enterprise sales team, cultivating a channel partner ecosystem, and investing in marketing that speaks the language of business outcomes, not just GPU specs. The distribution model also needs to change, offering self-service portals, easier onboarding, and solutions tailored to industries like financial services, which are already showing growing interest. It’s a move from being a specialized wholesaler of compute to a trusted enterprise partner.
Beyond competitive pricing, neoclouds must overcome barriers like providing more out-of-the-box functionalities and enterprise-grade SLAs. What are the most critical service offerings or operational changes they must prioritize in 2026 to be seen as a viable, business-critical option by enterprise buyers?
Price gets you a meeting, but robust services and reliability get you the contract for business-critical applications. For 2026, the absolute top priority must be building out a suite of managed services and out-of-the-box functionalities. An enterprise CIO doesn’t want to just lease a bare-metal GPU; they want a platform with pre-configured software stacks, MLOps tools, and data management features that accelerate their development cycle. They expect a “one-click” experience, not a DIY project. The second, equally critical change is the formalization of enterprise-grade Service Level Agreements (SLAs). For a pilot project, a bit of downtime might be acceptable. For a production AI application that’s driving revenue or customer interactions, it’s a non-starter. Neoclouds must offer guaranteed uptime, performance, and support response times that are contractually obligated and financially backed. This is the ultimate proof that they are ready for primetime.
Some neoclouds risk being overspecialized, while others try to balance AI focus with general-purpose cloud services. What are the key trade-offs in these approaches, and how does a provider determine the right mix to maintain a differentiated service while still appealing to broader enterprise needs?
It’s a classic business dilemmdepth versus breadth. The highly specialized providers, the ones laser-focused on AI, can offer unparalleled performance and cost-efficiency for those specific workloads. Their entire architecture, from the network fabric to the cooling systems, is optimized for training and inference. The trade-off is that they risk being a niche player, potentially alienating enterprise customers who need a broader range of services for the ancillary parts of their applications. On the other hand, providers like Vultr are trying to strike a balance, offering both AI-specific infrastructure and general-purpose cloud services. This broadens their customer base and makes them a more “sticky” provider, but they risk diluting their message and being seen as a master of none. The right mix depends on their ambition. If the goal is to be the absolute best-in-class for AI researchers and developers, hyper-specialization is the way. If the goal is to capture a significant share of the overall enterprise cloud budget, a more balanced portfolio is necessary.
What is your forecast for the neocloud market’s competition with hyperscalers over the next three to five years?
My forecast is that the next few years will be defined by intense, but evolving, competition. We won’t see neoclouds replace hyperscalers; the market is not a zero-sum game. Instead, we will see them successfully carve out a permanent and highly profitable segment of the market focused on specialized compute. Hyperscalers will respond by lowering their own AI infrastructure prices and launching more competitive specialized services, but the neoclouds’ focus and agility will remain a key advantage. The most successful neoclouds will be those that mature their enterprise offerings, proving they can provide not just raw power, but also the reliability, security, and support that businesses demand. The market will become more fragmented, with enterprises confidently adopting multi-cloud strategies where they use a hyperscaler for 80% of their needs but move that critical, high-cost 20%—their strategic AI workloads—to a specialized neocloud that does it better and more efficiently.