Neoclouds: How GPU-Only Clouds Are Redefining AI Infrastructure
Microsoft’s $17B Nebius deal signals a new normal: hyperscalers are partnering with specialized clouds to keep up with AI demand.
The Hidden Bottleneck in AI
AI headlines usually focus on new models, jaw-dropping demos, or billion-parameter benchmarks. But beneath all that excitement lies the true story: infrastructure is the bottleneck.
GPUs are the new oil — scarce, expensive, and unevenly distributed. From startups to Fortune 500s, everyone is competing for the same limited pool of NVIDIA H100s and next-gen B200s. Even the hyperscalers — AWS, Azure, Google Cloud — face GPU shortages and long customer wait times, despite their massive scale.
That’s why Microsoft signed a $17.4B deal with Nebius, with an option up to $19.4B. This partnership marks the rise of a new force in cloud computing: the neocloud.
What Exactly Are Neoclouds?
Neoclouds are specialized, GPU-only cloud providers purpose-built for AI and machine learning workloads. Think Nebius, CoreWeave, Lambda Labs, Crusoe.
Unlike general-purpose clouds, they:
Optimize everything — racks, cooling, networking — around GPU density.
Deliver transparent pricing — flat per-GPU hourly rates, without layers of hidden services.
Scale fast — spinning up GPU-centric datacenters in months, not years.
In short, they’re the picks and shovels of the AI gold rush.
Why They’re Exploding
GPU Shortages Are Real
Even enterprises with big budgets are on waitlists.
Hyperscalers oversubscribe clusters, prioritizing the biggest spenders.
Neoclouds contract directly with NVIDIA and other suppliers to secure dedicated GPU capacity, often bypassing traditional allocation queues.
Cost Efficiency
Analyst reports (e.g., Uptime Institute) suggest neocloud GPU capacity can be significantly cheaper — in some cases as much as 66% compared to hyperscalers.
Lean design (immersion cooling, hot/cold aisle containment, InfiniBand fabrics) cuts operational costs and improves efficiency.
AI-First Design
Where AWS and Azure juggle thousands of services, neoclouds focus on one thing: maximizing throughput for training and inference.
NVLink and InfiniBand enable GPU-to-GPU chatter at terabits per second — critical for training massive models.
Enterprise Urgency
AI initiatives aren’t slowing down. CIOs can’t afford to wait six months for GPUs.
Industry surveys show the vast majority of enterprises are already piloting or planning to adopt neocloud services, largely due to GPU scarcity and cost.
Microsoft + Nebius: A Turning Point
The Microsoft–Nebius deal is a watershed moment:
It locks in predictable GPU supply for Azure customers.
Industry observers expect Nebius capacity to integrate into Azure’s control plane, so customers continue to consume services as if they’re running natively in Azure.
It highlights that even hyperscalers are augmenting their infrastructure with external partners to meet demand.
The signal is clear: neoclouds aren’t niche players. They’re becoming essential partners in the AI supply chain.
What This Means for Cloud Architects
Here’s how you should think about neoclouds in your designs:
Multi-Cloud Under the Hood
With partnerships like Microsoft–Nebius, it’s increasingly possible that workloads could be dispatched to neocloud capacity behind the scenes.
Hybrid AI Pipelines
Run base workloads in your primary cloud, then burst to neoclouds during training spikes.
Portability Is Non-Negotiable
Use containers and MLOps frameworks (Kubernetes, Kubeflow, SageMaker pipelines) to keep workloads mobile.
Benchmark Aggressively
Compare dollar-per-training-throughput across clouds. Neoclouds may offer better performance at lower cost.
Evaluate Vendor Risk
New players can be fragile. Balance innovation with governance, compliance, and long-term support.
Risks & Realities
Neoclouds bring new opportunities, but also new questions:
Compliance: Where exactly are those datacenters? How do you audit them?
Vendor viability: Are they built for the long haul, or could they get acquired tomorrow?
Integration: If you go direct, you add a new layer of billing, IAM, and monitoring.
That’s why many enterprises will prefer to consume neocloud capacity through Azure, AWS, or Google — just as Microsoft is doing with Nebius.
The Takeaway
The rise of neoclouds marks a clear shift in cloud computing:
Hyperscalers no longer monopolize the infrastructure conversation.
GPU-only clouds are carving out massive deals — and winning.
The future of AI compute will be hybrid by design: hyperscalers + neoclouds, woven together under the hood.
If you’re designing AI infrastructure today, the message is clear: assume multi-cloud, plan for portability, and keep an eye on neoclouds.
Because when Microsoft hedges its bets with a $17B Nebius deal, it signals a new normal — hyperscalers partnering with specialized clouds to keep up with AI demand.
Call to Action
💡 What do you think? Would you trust a neocloud provider like Nebius directly, or only consume them through Azure/AWS? Drop your thoughts — I’d love to hear how you’re approaching GPU supply in your AI strategy.