Cloud AI strategy must align with provider selection to manage costs and meet specific use cases
Cloud and AI are now inseparable. If your business is investing in one, it’s already investing in the other, whether you see it or not. The right cloud AI strategy needs more than tools or technology. It needs to align tightly with your chosen providers. That’s where the leverage is. Lose that alignment, and you’re increasing complexity, limiting speed, and burning through budget without impact.
Ed Anderson, Distinguished VP Analyst at Gartner, puts it clearly: “Cloud provides the necessary infrastructure for AI.” That’s why we’re seeing a huge acceleration in data center infrastructure builds by top players like Microsoft, Google, and AWS. They’re laying down billions into AI-optimized compute, storage, and network layers, not out of chance, but necessity. According to Dell’Oro Group, the top 10 hyperscalers invested over $455 billion in infrastructure in 2024 alone, a 51% jump from the year before. That’s not a small pivot. It’s the new foundation for competition.
If you’re still looking at cloud and AI as separate initiatives, you’re behind. Enterprises need to merge their AI ambitions with their choice in cloud architecture. That includes understanding what workloads drive your business forward, and then connecting them to the best infrastructure match possible. Spend and scale are variables you can control if you make thoughtful decisions upfront. Strategy isn’t about saying yes to every shiny object, it’s about aligning your resources with real, high-leverage outcomes.
Four essential steps for effectively evaluating cloud AI providers
Buying decisions for cloud AI can’t be based on branding or default vendor relationships anymore. If you’re in the C-suite, you need clarity on the steps that determine whether your AI plans translate into results, or stall.
Start first with your needs. What types of AI models make sense for the problems you’re trying to solve? What AI agents or applications do your teams need to deploy, now and in the next two years? Be specific. That’s the only way to choose an architecture that doesn’t just fit, but scales.
Next, you need to get into the capabilities. Different providers bring different levels of AI maturity, some offer strong model training platforms, others are better at deployment or orchestration. Look closely at how they implement and scale AI-specific functions across compute, security, and data. Don’t assume parity, test it.
Third, work your relationships. If you’re already partnered with a hyperscaler, use that as leverage for better access to premium AI tools and early-stage capabilities. These relationships often carry room for negotiation. Make sure you’re not defaulting to standard packages when a custom deployment could bring more value.
Finally, think seriously about governance, security, and hiring. Strong AI workloads shift data and expand edge cases. That comes with risk. Make sure your team knows how to operate within the trust, compliance, and security boundaries your industry and geography demand.
If you get the selection and integration wrong, you lose time, and time at this scale is worth more than budget. Make the call once, and make it right.
Categories of cloud providers are evolving, choose based on capability, not just size
There’s no one-size-fits-all cloud anymore. You’re choosing from three major types, hyperscale, specialty, and AI-optimized, and the right call depends on what you’re actually building. Hyperscalers like AWS, Microsoft, and Google have deep stacks and global scale. But that doesn’t mean they’re always the right fit for every workload.
Specialty cloud players such as Akamai, Expedient, Vultr, DigitalOcean, and Render are gaining relevance. They focus on gaps big providers often overlook, like supporting sovereign frameworks in specific geographies or optimizing for unique industry requirements. If you need to deploy AI where data regulations demand physical proximity or stricter control, these are vendors that bring local nuance and operational focus.
Then there’s private cloud, quietly making a comeback, especially for AI models that process sensitive or regulated data. Staying in control of where and how data is stored isn’t just a preference anymore. It’s a compliance imperative in sectors like healthcare, defense, and critical infrastructure. Private cloud gives you that isolation, and isolation in AI workloads isn’t just about risk, it’s about reliability.
CIOs should stop making decisions based on the biggest brand name or the most aggressive pricing. What matters now is whether a provider can deliver the compute footprint, security controls, and specialized accelerators your models demand. If you pick based on what you needed two years ago, you’ll get left behind in the next six months.
Ed Anderson from Gartner notes this division clearly and sees each segment playing its own role, hyperscalers dominating scale, specialty providers capturing overlooked edge cases, and AI-optimized services redefining raw performance.
AI-optimized clouds (Neoclouds) offer a strategic edge for demanding workloads
If your AI strategy centers around training large models or deploying inference in compute-heavy environments, pay attention to AI-optimized clouds, also called neoclouds. These providers, including CoreWeave, Nvidia, DataCrunch, Runpod, and Fluidstack, are built for one thing: delivering peak performance for AI-first use cases.
They’re not a scaled-down version of a hyperscaler. They deploy similar capabilities, but tuned for performance. That means GPU-rich infrastructure, rapid model deployment, and focused SLAs that prioritize throughput and uptime on AI-specific tasks.
Ed Anderson makes it clear: neoclouds offer the same types of services as hyperscale and specialty platforms, but with optimized performance that matters when you’re pushing limits. If speed from training to inference is a bottleneck in your production cycle, this category may help you move faster without rebuilding everything internally.
This isn’t a side market anymore. According to Synergy Research Group, neoclouds are projected to drive over $23 billion in revenue by 2025. The entire AI-optimized cloud market is expected to push above $180 billion by 2030 and is growing at an annual pace of nearly 69%. Those numbers don’t happen without demand, and that demand is coming from enterprises pushing full-scale AI into core operations.
For leadership teams, the message is simple: You don’t need to replace existing cloud infrastructure to benefit. You can integrate AI-optimized clouds into your existing architecture to complement specific demands. What matters is recognizing where these providers fit and deploying their strengths where they make the clearest impact. Don’t wait until existing infrastructure maxes out. Plan for performance now, or pay later in lost velocity.
AI agent management is becoming a core evaluation factor in cloud AI strategy
The next shift in AI isn’t just about models, it’s about agents. If you’re leading AI strategy, you need to pay attention. AI agents are systems that can reason, act, and adapt autonomously within software environments. They go beyond task-specific models. They interact, initiate, and make decisions at scale. That increases both opportunity and risk.
According to Ed Anderson, Distinguished VP Analyst at Gartner, the ability to build, deploy, and manage AI agents will be a critical differentiator when evaluating cloud providers going forward. Many providers can train and serve models. Far fewer are ready to orchestrate intelligent agents within enterprise environments, especially at scale, under governance, and with full traceability.
The presence of AI agents brings weight to areas like regulatory compliance, security, control frameworks, behavioral monitoring, and rollback capabilities. These aren’t technical add-ons; they’re requirements for any environment where AI acts independently. If you’re choosing a provider without investigating how it supports AI agent infrastructure, you’re not future-proofing.
This move toward agents also changes the skillsets your team needs. Developers, architects, and operations teams must learn to manage agents not just as background services, but as active participants embedded in business logic. This adds complexity, but it also creates leverage. The organizations who get ahead of this curve will be operating at a higher level of productivity, with faster automation cycles and tighter feedback loops.
Leadership needs to start asking hard questions: Where are we today on agent readiness? Where are our gaps in cloud support? Which providers are actually investing in secure, scalable, governed agent deployment? If those questions aren’t part of your strategy sessions now, they will be soon.
Hybrid cloud AI architectures are essential for Long-Term flexibility and interoperability
No single cloud provider will give you everything. You’ll need a hybrid approach, full stop. It’s not just about redundancy or reach. It’s about capability.
As AI adoption deepens, use cases are spreading across departments, geographies, and compliance requirements. That means you’ll need different environments, with different constraints. A single provider might be ideal for enterprise-wide scale. Another might offer stronger support for local data sovereignty, or industry-specific certifications. If you limit yourself to a single ecosystem, you’re limiting the business.
Ed Anderson reinforces the point: “Hybrid is going to be critical no matter what.” That means working across hyperscale, specialty, AI-optimized, and private environments, and ensuring interoperability between them. You’re not just buying tools anymore. You’re designing infrastructure that has to coordinate, adapt, and grow.
The architecture must make services portable and deployments composable. This involves aligning compute, storage, security, access control, and model lifecycle management across domains. The organizations that do this well will not only perform better, they will move faster, with lower operational risk.
For executives, this is no longer an infrastructure question. It’s a business agility question. Interconnectivity between clouds lets you position AI where it has the most impact, negotiating cost, performance, and governance without trade-offs that stall innovation. Your architecture should allow you to make decisions based on outcome, not vendor lock-in. That’s how you scale AI sustainably.
Key highlights
- Align strategy with cloud investment: Cloud and AI are now interconnected. Leaders should ensure their AI strategy is directly tied to their choice of cloud provider to control costs and scale effectively as infrastructure demands accelerate.
- Evaluate using a structured decision process: Executives should follow a clear four-step process, define AI needs, assess provider capabilities, negotiate through existing relationships, and evaluate governance and skill requirements, to ensure AI deployments succeed operationally and financially.
- Select vendors based on use case fit: Leaders should differentiate between hyperscalers, specialty clouds, and AI-optimized clouds, leveraging private cloud as needed to meet specific regulatory, performance, or geographic requirements.
- Leverage AI-optimized clouds for performance: AI-first providers like CoreWeave and Runpod are growing fast because they meet performance demands at scale. Consider integrating them into hybrid strategies to handle intensive model training and inferencing.
- Prepare for AI agent deployment: As AI agents become more central to operations, leaders must evaluate providers based on their ability to manage agent lifecycle, security, and governance, this will be a critical competitive edge.
- Build for hybrid interoperability: Cloud environments will remain distributed. Executives should prioritize hybrid-ready architectures that allow seamless integration across providers to maximize flexibility, compliance, and performance.
