Why the Infrastructure That Built Your AI Won’t Run It

The AI infrastructure playbook is being rewritten. What got companies through the training era — raw GPU availability, hyperscaler defaults, brute-force compute — isn’t what will carry them through the inference era.

In a recent conversation, Kyle Sosnowski, Vice President of Software Engineering at Crusoe, and WEKA’s Val Bercovici broke down exactly why that gap matters, and what organizations need to do about it. Their core message: inference isn’t just a new workload. It’s a fundamentally different problem that requires rethinking cloud strategy, cost optimization, and infrastructure reliability from the ground up.

Why GPU Workloads Require Different Cloud Infrastructure Than CPU Applications

When asked whether a cloud is simply a cloud in the AI world, Kyle’s response was definitive: “It’s a very different landscape when you’re focusing on GPU workloads vs. more broader application infrastructure.”

This distinction matters more than most organizations realize. Traditional CPU-focused cloud platforms were built for stateless applications and batch processing. AI inference demands something different altogether: memory-bound optimization, intelligent caching, and microsecond latency at scale.

Crusoe recently put resources behind this thesis by acquiring Atero, a company focused specifically on memory-bound workload optimization. “That’s the area we see the market heading towards in 2026 and beyond,” Kyle explained. “Training had its heyday and people are really focusing on inference.”

How “Subscalers” Deliver Better Performance for AI Inference Than Hyperscalers

The conversation revealed a critical shift in how enterprises are thinking about cloud strategy. Companies are no longer defaulting to hyperscalers for GPU workloads. Instead, they’re diversifying their cloud mix to include “subscalers,” which are more specialized AI cloud providers that can deliver what hyperscalers struggle with: modern features, better hardware access, and truly hands-off infrastructure management.

“More and more companies are looking to diversify their cloud away from a hyperscaler into other subscalers,” Val said. The reason? “More modern features … a little bit more approachable and easier to manage.”

This isn’t about abandoning hyperscalers entirely. It’s about recognizing that different workloads have different infrastructure requirements, where some specialization is needed.

Why KV Cache Management Is Critical for Reducing AI Inference Costs

Among the most technical—and most critical—elements of the conversation centered on KV cache (Key-Value cache) management.

“The next scale of inference is not CPU-bound, it’s memory-bound,” Kyle emphasized. “It is unavoidable that the focus from the clouds and from model providers and from the frontier companies is all on optimizing for that solution.”

For organizations deploying AI at scale, this translates to real dollar savings. Efficient KV cache management can dramatically reduce the volume of input tokens processed, lowering costs while improving latency. It’s the difference between an AI application that’s economically viable and one that isn’t.

How Self-Healing Infrastructure Enables Multi-Cloud AI Deployments at Scale

When the conversation turned to user experience, Kyle highlighted an often-overlooked reality: “GPUs fail. They should be expected to fail. Hardware fails in general, so having to deal with XID errors coming off and remediating that yourself, shut off VMs, migrate workloads, deal with capacity management, it’s all very sophisticated.”

The question isn’t whether hardware will fail, but whether your infrastructure can handle failures without human intervention. Crusoe’s investment in self-healing infrastructure represents a bet that “the inference battle will be won at the user experience layer and the performance layer.”

Here’s what this looks like in practice:

• Active health checking that identifies problematic nodes before they impact workloads;
• Dynamic capacity management that pulls from reserved pools to replace failed hardware;
• Workload-aware migration that moves jobs seamlessly when issues arise.

“You can not worry about your cluster, and we’ll do both passive and active health checking,” Kyle explained. This “it just works” mentality enables AI-native companies and enterprises to go multi-cloud “very easily and just access hardware.”

Why Performance Metrics Matter More Than GPU Types for AI Infrastructure

“People become less concerned with which GPU is servicing their request; they’re just all performance-based,” Kyle said.

This represents the next step in the evolution of the AI infrastructure market. Early adopters obsessed over GPU models and configurations. Today’s sophisticated users care about outcomes: latency, throughput, cost per token, and reliability.

As Val noted in closing, “We take resiliency for granted, but we really notice the latency.” For infrastructure providers, this means the competitive battleground has shifted from GPU availability to holistic performance optimization.

What This Means for Your AI Strategy

The insights from this conversation point to three actionable takeaways for organizations scaling AI workloads:

1. Audit your inference economics. If you haven’t mapped your cost per token and identified optimization opportunities in KV cache management, you’re likely overpaying significantly.
2. Evaluate subscalers for GPU workloads. The hyperscaler-first strategy made sense when training dominated. For inference, specialized providers may offer better economics and performance.
3. Demand self-healing capabilities. Manual GPU cluster management doesn’t scale. Infrastructure that handles failures automatically is foundational for production AI systems.

The shift from training to inference isn’t simply a technical evolution; it’s an economic imperative. Organizations that recognize this and adapt their infrastructure strategy accordingly will have a decisive advantage in the AI-native era.

The Bottom Line

The shift from training to inference is already underway — and the organizations that treat it as a simple operational change will find themselves overpaying and underperforming. The winners in the AI-native era will be those who adapt their infrastructure strategy now: auditing inference costs, exploring specialized cloud providers, and demanding self-healing systems that can operate at scale without constant human intervention.

The infrastructure battle for AI has entered a new phase. The question is whether your stack is ready for it.

If you want to learn how WEKA can help you break through inference barriers, click here to learn about our solutions and request an infrastructure cost analysis.