What Is a Neocloud? The Fastest Way to Get GPU Access
"I need GPUs for my AI project, but the waitlist is weeks long."
If you've ever tried to spin up high-end GPUs on a major cloud provider, you know the pain. Long queues, complicated pricing, and surprise bills. And all just to get the compute you need.
There's a new category of cloud infrastructure built to solve exactly this: neoclouds. In this post, we'll break down what neoclouds are, how they compare to hyperscalers, and how to get started today.
This article was written by the VESSL AI team and includes an introduction to VESSL Cloud.
What Is a Neocloud?
A neocloud is a next-generation cloud provider that delivers GPU compute as a service (GPUaaS), purpose-built for AI training and inference. Unlike general-purpose hyperscalers like AWS, Azure, and GCP, neoclouds focus exclusively on GPU infrastructure.
The term emerged in late 2024 to describe cloud providers that go all-in on AI compute.
Think of it this way: if hyperscalers are big-box retailers that stock everything from groceries to electronics, neoclouds are specialty stores that do one thing, GPU compute, and do it faster and cheaper.
Leading neoclouds globally include CoreWeave, Lambda, Crusoe, and Nebius. These providers primarily serve North American and European markets. VESSL AI is one of the few neoclouds with a presence in Asia-Pacific, serving research teams at Stanford, MIT, CMU, and Berkeley alongside AI startups, with one of the widest GPU lineups in the market, from A100 all the way to B300.
Why Are Neoclouds Growing So Fast?
Three forces are driving the rapid rise of neoclouds.
1. GPU Demand Has Outpaced Supply
According to SemiAnalysis's GPU Shortage Report (March 2026), on-demand GPU rental capacity is now sold out across virtually all GPU types. Their H100 Price Index, built on monthly survey data from over 100 market participants, shows one-year rental pricing surged nearly 40% in just six months, from $1.70/hr per GPU in October 2025 to $2.35/hr by March 2026. Even securing 64 GPUs (8 nodes) of H100s or H200s has become difficult, with half the providers surveyed reporting zero available capacity. With all reserved capacity through August 2026 already booked, teams are turning to neoclouds that can deliver GPUs in minutes, not weeks, because their infrastructure is built for GPU compute from the ground up.
When a critical AI project slips by an entire quarter because you can't get GPUs, that's not an inconvenience. It's a competitive disadvantage.
2. Significant Cost Savings
According to a February 2025 analysis by the Uptime Institute (Neoclouds: a cost-effective AI infrastructure alternative), on-demand pricing for an NVIDIA DGX H100 node (8 GPUs) averages $98/hr on hyperscalers vs. $34/hr on neoclouds, roughly a 66% cost reduction. For reference, the equivalent configuration on VESSL Cloud costs under $20/hr.
How do neoclouds achieve this? By optimizing for GPU density. While hyperscalers spread resources across general-purpose, CPU-centric infrastructure, neoclouds build facilities specifically around GPU compute, maximizing efficiency per dollar.
3. Purpose-Built for AI Workloads
Hyperscalers spread resources across hundreds of services, from databases to networking to storage. Neoclouds do one thing: GPU compute. That specialization means GPU-optimized data centers with high-bandwidth interconnects, pre-configured ML environments, and infrastructure designed from the ground up for training and inference. The result is better performance per dollar and less operational overhead for AI teams. Instead of fighting for GPU quota inside a general-purpose cloud, teams get direct access to the hardware they need, configured for the workloads they actually run.
Neoclouds vs. Hyperscalers: What's the Difference?
This is the question everyone asks. Here's how they compare:
Source: Uptime Institute, February 2025 analysis
One important nuance: neoclouds aren't replacing hyperscalers. They're complementing them. Microsoft and Google both offload some AI compute to neoclouds. Microsoft has committed roughly $10 billion in GPU capacity from CoreWeave across multiple contracts through 2030, and OpenAI signed a separate $11.9 billion deal with CoreWeave in 2025. The emerging pattern is a hybrid approach: general infrastructure on hyperscalers, GPU-intensive workloads on neoclouds.
How Neoclouds Compare: CoreWeave, Lambda, Crusoe, and VESSL AI
The neocloud landscape is still young, but a few major players have already emerged, each with a distinct strategy.
CoreWeave is the largest neocloud by revenue and capacity. The company went public in 2025 and operates 40+ data centers with over 250,000 GPUs across the U.S. and Europe. Its customer base includes Microsoft, OpenAI, and Meta, with OpenAI alone signing an $11.9 billion five-year deal in early 2025. CoreWeave offers H100 through GB200 NVL72 clusters and is known for large-scale, long-term infrastructure contracts.
Lambda started as a deep learning hardware company and expanded into cloud, offering a developer-friendly experience with strong community ties. Their focus is largely on U.S.-based AI researchers and startups, with an emphasis on fast onboarding and simple pricing.
Crusoe has evolved from a stranded-energy computing startup into a major AI infrastructure player. It is the lead developer of OpenAI's flagship Stargate data center campus in Abilene, Texas, a 1.2 GW facility backed by $11.6 billion in financing. Crusoe's energy-first model leverages stranded natural gas and renewables to achieve energy costs 30 to 50% below traditional data centers. Valued at over $10 billion after its October 2025 Series E, Crusoe also launched Managed Inference and modular Edge Zones in late 2025.
Nebius (spun out from Yandex) has grown into a full-stack AI cloud platform serving European and global enterprise customers. It offers its own proprietary server chassis, Token Factory (inference-as-a-service), and enterprise security certifications including SOC 2 Type II. Nebius has signed large-scale contracts including a reported deal with Meta for up to $27 billion in AI capacity.
VESSL AI offers one of the widest GPU lineups among neoclouds, A100, H100, L40S, B200, GB200, and B300, with self-service access starting in minutes. Unlike single-provider neoclouds, VESSL Cloud aggregates capacity across multiple hyperscalers and neoclouds, giving teams multi-cloud flexibility without managing multiple vendor relationships. It also includes GPU infrastructure in South Korea, making it a strong option for teams across the Asia-Pacific region. Its proprietary Fluid Computing technology enables seamless multi-node training and mid-workload scaling, from 1 to 64 GPUs with zero contracts. VESSL AI holds SOC 2 Type II certification.
Why VESSL Cloud?
Jaeman Ahn, CEO of VESSL AI: "The core promise of a neocloud is getting GPUs fast, at a fair price. VESSL Cloud delivers on that, from A100 to B300, with self-service access in under 3 minutes."
GPU Lineup & Pricing
Pricing as of April 2026. Up to 15% discount for reserved instances. Academic and research discounts available. Spot instances coming soon for all models. Prices shown are per individual GPU. For comparison, the Uptime Institute benchmark of $34/hr (neocloud avg.) and $98/hr (hyperscaler avg.) refers to a full DGX H100 node (8 GPUs).
What Makes VESSL Cloud Different?
- Instant start: Sign up and launch a JupyterLab on A100 or H100 within 3 minutes
- Widest GPU selection: A100 → H100 → L40S → B200 → GB200 → B300. Match the GPU to your workload
- Flexible billing: Choose between spot, on-demand, and reserved instances
- Persistent environments: Switch GPUs without losing your code, data, or configurations
- Team management: Real-time dashboards for GPU utilization, VRAM, and cost tracking
When Should You Use a Neocloud?
Neoclouds aren't for every workload. Here's a simple framework:
Neoclouds are a great fit when:
- You're training large AI models (LLM fine-tuning, multi-node training)
- You're an AI startup that needs GPUs fast without procurement headaches
- You're a research lab or university looking for cost-effective GPU access
- You need to scale flexibly from prototype to production
- You need specific next-gen GPUs (GB200, B300) for cutting-edge experiments
Hyperscalers make more sense when:
- You need a wide range of cloud services beyond GPU (databases, networking, IAM)
- Your existing infrastructure is deeply integrated into a specific hyperscaler
- Your GPU usage is minimal and sporadic
Of course, a hybrid strategy works well too. Many teams run general infrastructure on a hyperscaler and offload GPU-heavy workloads to a neocloud.
Getting Started: 3 Steps
Getting GPU access on VESSL Cloud is straightforward.
Step 1: Sign Up
Create an account at cloud.vessl.ai. After email verification, you'll have immediate access to the dashboard.
Step 2: Choose Your GPU
A100 and H100 are available instantly via self-service. For next-gen GPUs like GB200 and B300, reach out to the Sales team.
Step 3: Run Your Workload
Launch JupyterLab, VS Code, or define your workload via YAML.
From sign-up to your first GPU: 3 minutes.
Frequently Asked Questions
Q. What is a neocloud?
A neocloud is a next-generation cloud provider that delivers high-performance GPUs as a service (GPUaaS) for AI training and inference. Unlike general-purpose hyperscalers (AWS, Azure, GCP), neoclouds focus 100% on GPU compute, offering faster provisioning and lower costs.
Q. What's the biggest difference between neoclouds and traditional clouds?
GPU availability and cost. On hyperscalers, getting GPU capacity takes an average of 2 to 4 weeks. On neoclouds, provisioning is instant or within days. Costs are roughly 66% lower for the same GPU (Uptime Institute, February 2025).
Q. How much do Neocloud GPUs cost?
On VESSL Cloud, A100 SXM 80GB starts at $1.55/hr and H100 SXM 80GB starts at $2.39/hr on-demand. Reserved instances get up to 15% off, and academic/research discounts are available.
Q. What regions does VESSL Cloud serve?
VESSL Cloud operates GPU infrastructure in South Korea, serving teams across the Asia-Pacific region. For the latest on region availability and expansion plans, visit vessl.ai/pricing.
Q. Is it secure?
VESSL AI has completed SOC 2 Type II certification, meeting enterprise-grade security standards for GPU infrastructure.
Q. How big is the neocloud market?
ABI Research initially projected neocloud GPUaaS revenue would surpass $65 billion by 2030 (July 2025), with subsequent estimates trending higher. The broader GPUaaS market, including hyperscalers, is expected to reach approximately $49.8 billion by 2032 at a 35.8% CAGR, according to Fortune Business Insights.
Get Started with VESSL Cloud
If your team is waiting weeks for GPU access or overpaying on hyperscalers, VESSL Cloud is the fastest way to fix that. Sign up, pick your GPU, and launch a workload in under 3 minutes.
Launch your first GPU now:
- Start building on VESSL Cloud
- Academic or research team? Ask about research discounts
- Not sure where to start? Book a 15-min walkthrough
This article was last updated on April 10, 2026. GPU pricing and lineup are subject to change. Check vessl.ai/pricing for the latest.
VESSL AI