
With AI came the technology to transform entire industries, but the infrastructure to run it was suddenly constrained. Hyperscalers secured much of the available advanced GPU capacity for their own needs, leaving many startups, research labs, and smaller enterprises scrambling for compute.

Demand for GPUs surged so high that pricing quickly became prohibitively expensive. In early 2024, renting four NVIDIA H100-powered instances from a hyperscaler for just one month could [cost](https://journal.uptimeinstitute.com/neoclouds-a-cost-effective-ai-infrastructure-alternative/) nearly $300,000.

This opened up a gap in the market, and into it stepped an entirely new type of cloud provider: the [neocloud](https://vast.ai/hosting/neoclouds).

## What Is a Neocloud? Definition and Core Models

Born out of GPU scarcity and cost barriers, as well as hyperscaler allocation bottlenecks, neoclouds are AI-first cloud infrastructure providers that specialize in GPU-as-a-Service (GPUaaS) and high-performance computing (HPC) workloads. Neoclouds serve a market that's [projected](https://www.abiresearch.com/blog/leading-neocloud-companies) to grow from $42B last year to $250B+ by 2030.

A neocloud is an AI-first cloud infrastructure provider that specializes in GPU compute for machine learning training and inference workloads. Unlike traditional hyperscalers, neoclouds are purpose-built for AI, offering faster provisioning, transparent pricing, and configurations optimized for high-performance computing at significantly lower cost.

Neoclouds are an emerging type of AI-first cloud infrastructure provider that emphasizes GPUs, faster networking, and optimized storage for the intensive demands of generative AI workloads. Inference, training, and other machine learning algorithms require more advanced infrastructure to keep their data pipelines filled. Otherwise, resources become idle and turnaround time suffers.

Unlike hyperscalers, which offer general-purpose infrastructure for a wide variety of workloads, neoclouds typically focus on AI. Many provide GPU compute through simpler pricing models. Another advantage: faster provisioning and configurations that are optimized for AI training and inference.

But not all neoclouds are built the same way. Here's a look at the three business models that neoclouds operate under.

### 1. Own Infrastructure

Larger neoclouds like CoreWeave and Nscale build AI infrastructure in their own data centers. This requires massive capital investment in the millions to billions of dollars and involves depreciation risk for aging hardware, but it also offers full-stack control. Neoclouds that invest in their own infrastructure can provide earlier access to next-gen GPUs and support larger-scale enterprise deployments.

### 2. Colocation Strategy

Under the colocation model, neoclouds take a hybrid approach. They partner with data centers to lease established infrastructure, reducing upfront capital expenditure. High-density, GPU-optimized colocation environments allow neoclouds to scale quickly without building facilities from scratch.

### 3. Asset-Light Marketplace

With this approach, neoclouds like Vast.ai aggregate GPU capacity without owning infrastructure. The marketplace model connects distributed compute providers with customers who need flexible, cost-effective access without long-term commitments, while the platform itself acts as the coordination layer.

## Neocloud Benefits for Enterprise AI: Cost, Speed, and Scale

No matter their business model, neoclouds offer numerous benefits to organizations working with AI.

First and foremost, better pricing. Because they run leaner and have less overhead, neoclouds can [deliver](https://www.mckinsey.com/capabilities/tech-and-ai/our-insights/the-evolution-of-neoclouds-and-their-next-moves) up to 85% cost savings compared to hyperscaler GPU instances. They also monetize differently. Rather than complex, layered billing, neoclouds tend to offer transparent, per-second pricing where you only pay for what you use.

Second, specialized infrastructure. Neoclouds are purpose-built for AI, so they can deliver faster provisioning with infrastructure optimized specifically for model training and inference.

Third, greater elasticity and scalability. AI workloads can be unpredictable. Neoclouds give organizations the ability to scale GPU resources up or down dynamically, so they get exactly what they need when they need it and avoid overprovisioning. Some neoclouds, like Vast.ai, even support [serverless autoscaling](https://vast.ai/article/vast-ai-serverless-automated-gpu-scaling) for workloads with variable demand.

Finally, better support for AI-specific compute requirements. AI workloads demand high-bandwidth interconnects, large memory pools, and massive parallel processing capacity. Because neoclouds are built around these requirements, they're often better suited for GPU-intensive workloads than general-purpose cloud environments.

Neoclouds like Vast.ai are also built to meet enterprise-grade security and compliance needs, so organizations don't have to choose between affordability and peace of mind.

However, not all neoclouds operate under the same economic or operational constraints.

## The Limitations of Traditional GPU Cloud Providers

Traditional neoclouds that own or lease infrastructure face a few major challenges:

- **Rapid price erosion:** Each new GPU generation decreases the value of older chips, with the price of a GPU hour [declining](https://www.mckinsey.com/capabilities/tech-and-ai/our-insights/the-evolution-of-neoclouds-and-their-next-moves) by up to 50% within five years.
- **Thin margins:** After depreciation, power, and labor costs, gross profit margins are often quite slim.
- **Difficulty differentiating:** To stay competitive, neoclouds may offer more than just raw compute, but developing higher-margin AI services can put them in direct competition with hyperscalers.

These pressures mean that traditional neoclouds have to maintain high utilization rates and constantly reinvest in new hardware while also navigating how to differentiate their services successfully.

The marketplace model, on the other hand, avoids these challenges.

## How GPU Compute Marketplaces Solve What Traditional Neoclouds Can't

By aggregating distributed GPU capacity rather than owning hardware, marketplace-based neoclouds eliminate the millions in upfront investment that would otherwise be needed, and never have to manage an aging fleet of depreciating hardware. There's also often little to no direct competition for the massive enterprise contracts that infrastructure owners depend on.

This means that the marketplace model is able to provide the same advantages as other neoclouds, including flexibility, elasticity, and on-demand access, while offering the most competitive rates possible.

One important byproduct of this approach is democratized compute. Organizations that can't afford higher rates can still leverage the latest hardware and pursue AI development.

### Neocloud vs. Hyperscaler: Key Differences

<div className="article-table-scroll neocloud-comparison-table">
  <table aria-label="Neocloud vs. hyperscaler key differences">
    <thead>
      <tr>
        <th>Neocloud</th>
        <th>Hyperscaler</th>
      </tr>
    </thead>
    <tbody>
      <tr>
        <td>GPU first</td>
        <td>CPUs first, with GPUs as add-ons</td>
      </tr>
      <tr>
        <td>Built for fast networking speed natively</td>
        <td>Mixed networking speeds</td>
      </tr>
      <tr>
        <td>Geared toward bare metal or thin VMs</td>
        <td>Virtualization overhead</td>
      </tr>
      <tr>
        <td>Simplified pricing</td>
        <td>Complex a la carte pricing</td>
      </tr>
      <tr>
        <td>Quick provisioning</td>
        <td>DIY provisioning or managed services</td>
      </tr>
    </tbody>
  </table>
</div>

## The Bottom Line

Both traditional neoclouds and hyperscalers carry overhead that drives up costs. Marketplace-based neoclouds like Vast.ai offer a better path forward, delivering the GPU compute your AI and HPC workloads need at prices that make experimentation and innovation possible for teams of any size.

For teams evaluating [GPU cloud](https://vast.ai/products/gpu-cloud) providers, from scrappy AI startups to enterprise ML teams, understanding the neocloud landscape is the first step toward smarter infrastructure decisions. Stop overpaying for GPU compute. Start building on Vast.ai today.


Neoclouds are AI-first GPU cloud providers built for machine learning workloads. Learn how they work, the three business models, and how they compare to hyperscalers.

Posts about: Gpu Cloud

What Is a Neocloud? The Business Model Explained