How GPU-first infrastructure is reshaping the cloud and where Hivenet fits
Before diving into definitions, let’s set the stage. The next evolution of the GPU cloud is already here. It’s called the neocloud—a new kind of AI-first cloud infrastructure designed for performance, transparency, and sustainability. This article explains what the neocloud is, why it matters, and how Hivenet’s Compute fits at the forefront of this movement.
Before the AI revolution, the cloud served a different purpose. Traditional providers optimized for web and storage tasks fell behind in the age of GPU cloud performance and AI-first cloud infrastructure. Traditional hyperscalers like AWS, Google Cloud, and Microsoft Azure built their public cloud offerings for broad, general-purpose workloads, but these platforms face challenges meeting the specialized needs of AI. The shift to neocloud infrastructure reveals why older models couldn’t keep up with today’s compute-intensive needs. Neoclouds are a modern class of cloud architecture that fill a gap between centralized hyperscale clouds and distributed telecom networks.
As organizations increasingly adopt AI, there is a surge in demand for high-performance GPU infrastructure, fueling the rapid growth of the neocloud market.
The cloud wasn’t built for AI
The cloud, as we know it, came from a different era. It was designed for web apps, databases, and file hosting. It thrived on virtualization and pay-as-you-go models. Then AI happened—and the old infrastructure began to show its cracks.
AI workloads don’t play well with generic compute. They need GPUs, fast interconnects, and access patterns that aren’t suited to multi-tenant virtual machines. Managing high-performance GPU infrastructure in multi tenant environments introduces challenges like ensuring workload isolation, high throughput, and low latency, which require specialized networking solutions. As a result, a new kind of provider began to emerge—leaner, faster, GPU-first. That’s what the industry has started calling the neocloud.
This shift marks more than a technical upgrade. It represents a new philosophy in cloud design—one that trades abstraction for control, and scale for efficiency.
What is a neocloud?
A neocloud is essentially a modern evolution of the GPU cloud—an AI-first cloud designed for the next generation of data-driven workloads. Neoclouds are purpose-built and rely on specialized infrastructure to meet the demanding requirements of advanced AI tasks.
A neocloud is a new generation of GPU cloud provider built specifically for AI and GPU computing. It’s not yet a mainstream marketing term, but an emerging label for companies rethinking cloud infrastructure around AI-first workloads. These providers design for machine learning, inference, and large-scale data processing. AI inference is a core focus, with robust gpu infrastructure and scalable gpu instances essential for handling these workloads efficiently.
Instead of sprawling data centers optimized for storage and CPU-based compute, neoclouds prioritize high-performance GPUs, direct access, and simplified pricing. They serve developers, researchers, and startups who want raw power without the hyperscaler overhead. Neoclouds offer more competitive pricing compared to traditional cloud providers due to their leaner operations and fewer services offered. They focus on delivering specialized cloud services and service GPUaaS, catering specifically to GPU-powered AI workloads. In essence, they are a new type of AI compute infrastructure focused on flexibility and speed.
How we got here
When AWS launched in 2006, the problem it solved was infrastructure sprawl. Businesses didn’t want to manage servers, racks, or cooling. The cloud turned capital expenditure into operational cost—a simple, elastic idea that transformed computing. Investments in neoclouds are expected to grow significantly, with the GPUaaS market projected to reach $12.26 billion by 2030, up from $3.80 billion in 2024.
Fast-forward to the 2020s. The new bottleneck isn’t servers or disks—it’s GPUs. AI training demands enormous compute density, and inference requires low-latency access to specialized hardware. The hyperscalers weren’t built for this kind of load. Their models rely on virtualized environments and generic networking stacks, which introduce latency and inflate costs. For example, the average hourly cost of an NVIDIA DGX H100 instance from traditional providers is $98, while from a neocloud it drops to $34, illustrating a 66% savings. Neoclouds provide faster access to the latest GPUs compared to traditional cloud providers, addressing these limitations effectively. NVIDIA H100, A100, and A10 Tensor Core GPUs are among the types of GPUs available for AI workloads. Powerful GPUs and specialized AI hardware are essential for supporting modern AI workloads, enabling scalable and efficient AI training, inference, and deployment.
Neoclouds offer on demand GPU resources with flexible, pay-as-you-go pricing. For organizations with larger or more predictable needs, volume discounts are available starting at thresholds like 500 GPU-hours per month, and long term contract options can be arranged for securing substantial GPU resources over extended periods. This flexibility allows users to choose between immediate, scalable access and customized agreements based on their usage patterns.
This gap opened the door for a new breed of providers: CoreWeave, Lambda Labs, Crusoe, Nebius, Scaleway, and now, Hivenet. These companies share one principle: build infrastructure optimized for AI workloads, not office spreadsheets.
What makes a neocloud different
Neoclouds stand apart because they combine GPU as a service flexibility with the strength of modern AI compute infrastructure. Their focus on direct GPU access, simplified billing, and distributed design creates a new standard for performance and cost-efficiency in the AI-first era. Comprehensive support and secure infrastructure are essential for protecting sensitive AI workloads and ensuring reliable operations. Neoclouds are designed to meet the evolving needs of customers by delivering scalable, high-performance AI services tailored to enterprise and individual requirements. Strategic partnerships between neocloud providers, data center operators, and AI leaders drive innovation and expand market opportunities across the cloud infrastructure ecosystem.
1. GPU-first, not CPU-first
Hyperscalers built their platforms on CPUs because web hosting and SaaS workloads didn’t need parallel computation. Neoclouds invert that. They start with GPUs as the foundation, often offering bare-metal access or thin virtualization layers for maximum performance.
2. Transparent pricing
Traditional cloud pricing is a maze—compute, storage, egress, IOPS, hidden fees. Many neoclouds eliminate egress fees, making their pricing more affordable and predictable for users who need to transfer large amounts of data. Neoclouds simplify that. They charge per GPU per hour (or even per second, as Hivenet's Compute does) and make costs predictable. This makes GPU as a service easier to budget for, especially for smaller teams. Neoclouds commonly utilize NVIDIA GPUs to ensure high reliability and performance for AI tasks.
3. Performance without bureaucracy
Provisioning a GPU on a major cloud often means waiting, configuring, and negotiating. Neoclouds are built for speed. They are ideal for high performance computing tasks, supporting demanding AI and data workloads that require consistent, optimized performance. You spin up instances in seconds, often with direct SSH or API access. Scalability allows users to rapidly adjust GPU cluster sizes without long waitlists or over-provisioning. Neocloud infrastructure can scale from a few to thousands of GPUs in less than 15 minutes.
4. Distributed or sovereign design
While some neoclouds still rely on centralized data centers, others adopt distributed or regionally sovereign architectures—where workloads stay closer to users and comply with local regulations. Hivenet goes further by distributing compute across a global mesh of real devices, turning idle capacity into usable cloud power. Many neocloud providers design compute hubs with data residency and sovereignty considerations for compliance in regulated industries like healthcare and finance.
5. AI-native mindset
Neoclouds integrate tools and templates for model training, fine-tuning, and inference out of the box. Many neoclouds provide pre-installed NVIDIA CUDA to support popular machine learning frameworks such as TensorFlow and PyTorch, enabling GPU acceleration for deep learning tasks. They assume users are building or deploying AI systems, not just hosting websites. This AI-first architecture defines them as a distinct class of AI compute infrastructure. Neoclouds focus exclusively on AI infrastructure, offering tailored solutions like pre-configured AI environments and integrated MLOps tools.
Why the old cloud model struggles
The hyperscale cloud model is efficient for horizontal scaling—millions of lightweight apps across virtual machines. But AI workloads scale vertically: they need powerful nodes, dense memory, and fast interconnects. An AI workload requires specialized infrastructure, such as GPUs, LPUs, or TPUs, and hardware configurations optimized for high-performance processing and scalability.
In that environment, virtualization becomes a bottleneck. Network throughput drops. Latency rises. And billing models that made sense for web apps become punishing for GPU workloads.
Developers end up paying for idle GPUs because spin-up times are long and billing clocks don’t stop instantly. That’s why neoclouds like Hivenet's Compute use per-second billing and transparent GPU pricing to make high-performance compute viable for smaller teams.
Examples of neoclouds today
These companies collectively represent the foundation of modern neocloud infrastructure, showing how the industry is evolving toward GPU-first and AI-centric design. Many of these providers deliver specialized GPU cloud services optimized for AI workloads, leveraging high-performance GPU compute resources, advanced interconnects like NVLink and InfiniBand, and architectures designed for large-scale AI training and inference.
The term “neocloud” isn’t owned by anyone—yet. But it’s already being used to describe a cluster of modern GPU cloud infrastructure providers:
- Hivenet — distributed, sustainable, and privacy-centric—built on real devices instead of data centers.
- CoreWeave — once a crypto mining startup, now one of the largest AI-focused GPU clouds in the U.S.
- Lambda Labs — a favorite of ML engineers for its plug-and-train simplicity.
- Crusoe Cloud — builds GPU farms powered by wasted flare gas energy.
- Nebius — spun out of Yandex, offering European GPU cloud infrastructure.
They differ in scale and philosophy, but all share the same shift: from CPU-based virtualization to GPU-based specialization within neocloud infrastructure.
The economics of neoclouds
Beyond performance, the economics of neoclouds are defined by GPU cloud pricing models and AI compute infrastructure cost efficiency. These factors highlight how neoclouds make high-end GPU computing more affordable and accessible for modern AI workloads. This affordability also extends to data-intensive analytics workloads, enabling organizations to efficiently run complex data analysis and AI-driven insights without prohibitive costs.
Neoclouds thrive because they break the hyperscaler monopoly on cost. Renting a single A100 instance on a big provider can exceed $3/hour, with hidden charges for storage and egress. Neoclouds operate at a fraction of that—sometimes by 60% less—through better utilization and leaner architecture.
This GPU cloud pricing advantage makes neoclouds particularly appealing to AI startups and research teams managing limited budgets.
Hivenet’s Compute, for example, offers RTX 4090 instances for around €0.20/hour and 5090s for €0.40/hour. No setup fees. No locked commitments. Just per-second billing.
That transparency doesn’t just lower costs. It restores trust. Developers know what they’re paying for—and when the meter stops.
The sustainability angle
This section highlights how the evolution of a sustainable GPU cloud and eco-friendly AI compute can reduce the environmental impact of intensive AI workloads.
AI computing has an energy problem. GPUs are power-hungry, and data centers consume vast amounts of electricity for cooling alone. Neoclouds can’t fix that entirely, but some, like Hivenet, take a distributed approach.
Instead of building new facilities, Hivenet uses the existing energy footprint of idle devices—computers, consoles, and servers already powered on around the world. That turns unused capacity into compute without extra environmental cost.
It’s not just efficient; it’s regenerative. It shifts the cloud from extraction to participation. This makes Hivenet one of the most sustainable GPU cloud platforms, offering a genuinely eco-friendly AI compute option.
Why “neocloud” matters
The term may be new, but the idea isn’t. Every wave of computing redefines how we access power. Mainframes gave way to personal computers, then to virtual machines, then to the web. AI is the next inflection point—and the neocloud is its infrastructure layer. The rapid rise of generative AI is driving unprecedented demand for scalable, high-performance infrastructure, while demanding AI workloads require specialized cloud solutions optimized for intensive training and inference.
The rise of the neocloud signals a philosophical shift: from infinite abstraction to intentional design. From selling compute by the gigabyte to designing it around real workloads.
That matters for everyone building AI today—from solo developers to research labs to enterprises looking for control over their data and costs.
Hivenet’s place in the neocloud era
Hivenet is part of the neocloud movement, embodying its principles of distributed design, transparent pricing, and GPU-first accessibility.
Hivenet didn’t start with GPUs; it started with a belief that the cloud could be distributed, sovereign, and fair. Over time, that philosophy naturally met the needs of AI teams frustrated by centralized clouds.
Compute with Hivenet brings together three things that define the neocloud movement:
- GPU-first infrastructure — RTX 4090 and 5090 GPUs ready for training, inference, and simulation.
- Transparent pricing — per-second billing with no surprise egress or storage fees.
- Distributed architecture — real devices forming a decentralized global cloud that prioritizes sustainability and sovereignty.
That combination makes Hivenet a cornerstone of neocloud infrastructure—one that reimagines who owns the cloud in the first place.
To learn more or discuss your AI infrastructure needs, contact the Hivenet team today.
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The road ahead
The neocloud isn’t a buzzword—it’s a direction. Over the next few years, more providers will follow the same logic: simplify, specialize, and decentralize. The future cloud will be smaller, faster, and closer to where data is generated.
For Hivenet, this is a continuation of its vision. The distributed foundation that powers its storage, compute, and file transfer services already anticipates this shift. As AI models grow and diversify, so will the need for flexible, ethical, and affordable compute.
The neocloud will become the norm. Hivenet just got there first.
To learn more about neocloud infrastructure and GPU cloud for AI workloads, explore more insights on the Hivenet Blog or start building today with Compute with Hivenet.
Frequently Asked Questions (FAQ)
What is a neocloud?
A neocloud is an AI-first cloud built around GPUs instead of CPUs. It’s designed for machine learning, inference, and other data-intensive workloads that require high-speed computation.
How is a neocloud different from a traditional cloud?
Traditional clouds rely on CPU-heavy virtual machines and complex billing. Neoclouds simplify this by offering GPU-first access, transparent GPU cloud pricing, and better performance for AI workloads.
Why is GPU as a service important?
GPU as a service gives developers flexible access to high-performance GPUs without owning hardware. It’s a key feature of AI compute infrastructure used for model training, fine-tuning, and inference.
What makes Hivenet part of the neocloud movement?
Hivenet combines distributed infrastructure, per-second billing, and sustainable GPU cloud design. It offers an eco-friendly AI compute model that makes advanced GPU resources more accessible.
Is a neocloud right for my business?
If your workloads involve AI, deep learning, or rendering, a neocloud can dramatically reduce costs while improving performance. For general web hosting or storage, a traditional provider may suffice.
