Your competitor is paying $2.01 per hour for the same chip from a provider you’ve never heard of.
The difference? $4.87 per hour. For a model training run lasting weeks, that’s hundreds of thousands of dollars** thrown away—simply out of **brand loyalty.
Welcome to the era of neoclouds. Cloud providers that don't try to be "everything to everyone." They do one thing: end-to-end AI infrastructure. No managed databases. No message queues. No CDNs. Just GPUs, networking, and storage—built specifically to run AI workloads at maximum speed and minimum cost.
While you were reading this sentence, CoreWeave generated $2.08 billion in revenue last quarter—a 112% year-over-year increase. Nebius grew by 684%, reaching $399 million.
The neocloud market, currently valued at $35.22 billion, is projected to explode to $236.53 billion by 2031. Gartner predicts these new providers will capture 20% of the $267 billion AI cloud market.
The question isn't whether you'll migrate to a neocloud. It's when—and whether you'll discover your competitors are already saving 60% while you keep paying full price.
🧠 What on Earth is a Neocloud? (And why you should care)
Neoclouds are cloud providers specializing exclusively in AI that offer GPU-as-a-Service.
They are the opposite of hyperscalers (AWS, Azure, GCP). While the giants offer thousands of services—databases, queues, serverless, CDN, analytics—neoclouds offer only what matters for AI: bare-metal GPU computing, high-speed networking, and low-latency storage.
The result? Transparent pricing, provisioning in minutes, and costs 30% to 60% lower.
As Gartner defines them: neoclouds are "cloud providers built specifically for AI and high-performance workloads."
And they are growing at a rate hyperscalers cannot match:
- The neocloud market grew 205% year-over-year in the second quarter of 2025.
- Sector revenue is expected to exceed US$ 23 billion in 2025 and US$ 20 billion in 2026, according to Forrester.
- The projected compound annual growth rate (CAGR) is 46.37% through 2031.
💰 The Numbers Don't Lie: 3x to 6x Cheaper
Here is the figure that should make any CTO or CFO jump out of their seat:
Hyperscalers cost 3 to 6 times more than neoclouds for the same computing capacity.
A concrete example: H100-class compute costs around US$ 2.01 per hour on Spheron, versus US$ 6.88 per hour on AWS—a difference of 3.4 times. At VESSL, the comparison is even more stark: hyperscaler H100s are 2.9 to 5.1 times more expensive than those from neoclouds.
And it doesn't stop there. While hyperscalers have complex, bundled pricing, neoclouds offer transparent pricing per GPU-hour.
What does this mean in practice?
- Foundation model training: weeks of continuous compute. A 3x price difference translates to hundreds of thousands of dollars per run.
- Production inference: persistent workloads running 24/7. Annual savings can reach millions of dollars.
- Experimentation: research teams can run 3 times as many experiments on the same budget.
The price difference isn't a minor detail. It is a strategic competitive advantage.
⚡ The Problem Hyperscalers Don't Solve: GPU Availability
Price isn't the only advantage of neoclouds. Availability is equally critical.
Hyperscalers frequently face GPU shortages, which delay projects or force companies into expensive long-term commitments.
Neoclouds, on the other hand, are specifically designed to meet GPU demand and often have capacity available when hyperscalers don't.
CoreWeave provisioned 512 H100s in less than 15 minutes. Try doing that on AWS.
The difference isn't just about price. It’s about speed of innovation.
🏢 The Players: Who Are the Neoclouds Changing the Game?
The neocloud market already features dozens of providers, each with its own specialization. Key players include:
CoreWeave
The neocloud giant. Revenue of US$ 2.08 billion in the last quarter, with a backlog of US$ 99.4 billion. Projected capital expenditure of up to US$ 35 billion by 2026. Recently secured a US$ 21 billion commitment from Meta.
Nebius
Explosive growth: revenue up 841% in the AI division, reaching US$ 390 million. Capital expenditure between US$ 20 billion and US$ 25 billion.
Lambda Labs
Focused on machine learning research teams, offering transparent hourly pricing and good availability of H100s.
RunPod
The "all-rounder" for AI workloads, offering good cost-efficiency and flexibility.
Vast.ai
The lowest-cost option for irregular training jobs, with rates starting at US$ 0.20 per hour.
Crusoe, Voltage Park, Vultr, OVHcloud
Other key names in the ecosystem.
New Entrants: SoftBank and Meta
The market is so hot that giants are entering the fray. SoftBank plans to launch a neocloud in the next fiscal year, leasing AI resources to US companies. Meta is also entering the neocloud market.
🏛️ Digital Sovereignty: The New Neocloud Frontier
Gartner has identified an additional trend: sovereign neoclouds.
Amidst growing regulatory pressure—GDPR, the EU AI Act, LGPD—companies are seeking providers that guarantee data and operations remain within specific jurisdictions. Sovereign neoclouds offer contractual guarantees that data, operations, and governance remain confined within national borders, protecting them from foreign legal claims.
As Gartner senior analyst Enrique Castera put it: "Neoclouds are differentiating themselves through superior performance on AI workloads, flexible deployment models, and a strong commitment to data sovereignty—often at a more competitive price point."
🔄 The Winning Strategy: Hybrid is the New Normal
The choice is no longer "neocloud vs. hyperscaler." It is neocloud and hyperscaler.
By 2026, mature engineering teams are adopting a hybrid approach:
- Neocloud for training and inference: Where cost and GPU availability are critical.
- Hyperscaler for the application stack: Where managed services (databases, queues, authentication) make sense.
This architecture allows companies to leverage the best of both worlds: the performance and cost-efficiency of neoclouds for heavy computing, and the mature ecosystems of hyperscalers for everything else.
🛡️ The Action Plan: How to Start Your Neocloud Journey
Migrating to neoclouds isn't a weekend project. But it’s not an insurmountable challenge, either.
1. Identify Pure AI Workloads
Model training, fine-tuning, and production inference—these are the natural candidates. If you are paying for GPUs on a hyperscaler for these tasks, you are overpaying.
2. Compare Prices
Calculate the actual cost of your workload on a hyperscaler versus a neocloud. Consider:
- Cost per GPU-hour
- GPU availability
- Data egress costs
- Storage costs
3. Test with a Pilot Project
Choose a non-critical workload and migrate it to a neocloud. Measure cost, performance, and operational experience. Adjust and learn.
4. Adopt a Hybrid Architecture
Keep what works on the hyperscaler. Migrate what makes sense to the neocloud. The key is orchestration, not total migration.
5. Consider Sovereign Neoclouds
If you operate in regulated sectors or have strict data residency requirements, evaluate providers that guarantee sovereignty.
6. Monitor and Optimize Continuously
The neocloud market is evolving rapidly. New providers emerge, prices change, and capabilities expand. Stay up to date.
💡 Conclusion: Neoclouds Are Not the Future — They Are the Present
Neoclouds are not just a promise. They are a reality that is already changing the economics of AI.
CoreWeave generated $2.08 billion in revenue in a single quarter. The neocloud market is growing at 46% annually and is projected to reach $236 billion. Gartner predicts they will capture 20% of the AI cloud market.
Meanwhile, hyperscalers are becoming 3 to 6 times more expensive for the same computing capacity. The price difference can no longer be ignored. It is strategic.
The question isn't whether you will adopt neoclouds. It’s when—and whether you’ll discover that your competitors are already saving 60% while you continue to pay full price.
The AI war won't be won by whoever has the best model. It will be won by those who can train and run models with the greatest economic efficiency.
Neoclouds are the secret weapon in this war. And they are available to you—right now.
📌 Has your company evaluated neoclouds for its AI workloads yet? Have you compared the true cost between hyperscalers and specialized providers? If the answer to any of these questions is "no," you are leaving money on the table. Share this post with your engineering and FinOps teams. The first step toward saving 60% on AI infrastructure is knowing that the alternative exists.
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