What happens when the old cloud meets the AI era — and why neoclouds like Compute with Hivenet are winning the race.
The limits of hyperscalers
AWS, Google Cloud, and Azure, the largest cloud providers, built the modern web. Their infrastructures power billions of apps, databases, and websites. Yet their scale comes with trade-offs — complexity, latency, and cost. Other cloud providers are also entering the market with new GPU offerings.
These hyperscalers were never designed for the demands of today’s AI workloads. GPU access is limited, provisioning is slow, and pricing models are opaque. Hyperscalers often struggle to guarantee high availability for GPU resources, leading to potential interruptions in access. Developers pay for abstraction layers they don’t need. Performance suffers, and experimentation slows, with the added risk of downtime due to resource contention or maintenance windows. Large-scale AI/ML model training requires massive parallel processing power and specific GPU types, which these hyperscalers struggle to provide. Various cloud GPU services support multiple GPU types including NVIDIA H100, A100, A10 Tensor Core, V100, RTX A6000, RTX 4090, and GH200 Superchip, which are essential for modern AI workloads. AI startups and research labs need immediate access to high-end GPUs without long wait times.
The AI era doesn’t need bigger clouds. It needs smarter ones. Neoclouds are particularly beneficial for organizations with specific regional data sovereignty or latency requirements.
For a full introduction to the concept and origins of the neocloud, read What is a Neocloud — the rise of cloud built for AI.
Transitioning from these limits brings us to the new alternative — the AI-first cloud built for performance and simplicity.
What makes a neocloud different
The neocloud flips the hyperscaler model on its head. It’s built for GPU-first operations, AI-first workloads, and real-world efficiency.
Compute with Hivenet isn’t competing on size — it’s competing on focus. It delivers: Customers from startups to enterprises rely on neoclouds for secure, scalable AI compute that meets their demanding workloads. a more sustainable future for cloud computing.
- GPU-first architecture: Direct, bare-metal access to RTX 4090 and 5090 GPUs without virtualization overhead. Users can quickly create new GPU resources or accounts with minimal setup.
- Transparent pricing: Clear per-second billing with no egress or storage surprises.
- Distributed compute: A network of real devices across regions, improving latency and energy use.
- Sustainability: A distributed model that reuses idle hardware, reducing environmental impact.
- Cost savings: You can save up to 80% compared to traditional clouds.
Neoclouds don’t replace hyperscalers for general workloads. They specialize in AI compute where performance and control matter most. A hybrid multicloud strategy uses traditional hyperscalers for general IT needs and neoclouds for specialized AI workloads, offering the best of both worlds.
For a deeper understanding of where a neocloud fits, see our guide on when to use a neocloud.
Cost and performance: where hyperscalers lose
Hyperscalers charge a premium for GPU access. Renting an A100 on a big provider can cost over $3/hour — often double or triple the rate of a neocloud. Access to specialized computing resources in neoclouds can be significantly cheaper for on-demand GPU access by 2-7x compared to other options. Most offerings provide billing without extra costs for data transfers like ingress/egress fees, making them more cost-effective for AI workloads.
Compute with Hivenet offers RTX 4090s for €0.20/hour and 5090s for €0.40/hour with per-second billing. No minimums. No commitments. That’s real transparent GPU pricing. Hivenet delivers the best price per GPU hour, maximizing value for every dollar spent.
The difference goes beyond numbers. With Hivenet’s distributed design, workloads run closer to users, cutting latency and boosting throughput. Each instance benefits from high network bandwidth, with up to 10 Gb per instance available for fast data transfer during compute tasks. For inference, that means faster responses and smoother scaling. AI workloads also benefit from the rapid deployment of GPU instances that can be spun up or launched in seconds, ensuring minimal delays in execution. Users can select from a variety of instance types tailored to different workloads and budgets. GPU instances can be scaled up from a single GPU to multiple GPUs such as 2x, 4x, or 8x based on workload requirements. Users can start with a single GPU and scale up their resources at any time. Cloud GPU services emphasize instantaneous setup, allowing users to deploy or spin up instances in seconds.
Control and simplicity
Hyperscalers built their ecosystems for scale — but at the cost of control. You get dozens of configurations, services, and permission layers that most developers don’t need. Managing them adds friction.
Compute with Hivenet returns control to users. You choose your hardware, connect directly, and start computing. No provisioning queues, no hidden tiers. Once you select your hardware, your workloads are deployed automatically, so you can get started without delay.
It’s GPU as a service designed for people who want to build, not configure. This philosophy defines the AI compute infrastructure Hivenet provides — simple, efficient, and scalable. AI-enabled consumer GPUs provide better cost-performance ratios than data center GPUs. Additionally, some GPU cloud services support various machine learning frameworks like TensorFlow, PyTorch, and NVIDIA CUDA out of the box, streamlining the development process for AI applications. After your initial selections, the rest of the deployment process is fully automated by the platform.
The sustainability gap
The carbon footprint of hyperscale data centers is massive. Cooling, redundancy, and power draw all add up. As AI workloads multiply, that impact grows.
Compute with Hivenet takes a different path. By turning existing idle devices into active compute nodes, it forms a sustainable GPU cloud. Energy that would otherwise be wasted powers real AI workloads.
This eco-friendly AI compute model reduces the need for new infrastructure and extends the lifecycle of existing hardware. It’s not just efficient — it’s responsible.
The sovereignty advantage
Hyperscalers often centralize data in ways that conflict with emerging privacy and data-sovereignty laws. For European teams, this can be a legal and ethical problem.
Hivenet’s distributed approach aligns naturally with digital sovereignty and operates as a distributed GPU cloud that enhances regional compliance while improving performance. Its GPU clusters run locally in the UAE, France, and the USA, ensuring lower latency and tighter compliance.
This combination of sovereignty and sustainability makes Compute with Hivenet stand apart in a landscape still dominated by centralized thinking. Hivenet provides built-in GDPR compliance without extra hoops to jump through.
Enterprise-grade features for modern workloads
Compute with Hivenet gives you access to powerful GPUs for AI work. It's a cloud platform that handles the heavy lifting so you can focus on your projects. You get on-demand access to high-performance GPU instances, including the latest NVIDIA models. Train AI models, fine-tune them, or run large-scale inference. No more waiting around.
The platform puts you in control. Deploy virtual machines in seconds. Manage your setup across multiple regions. NVIDIA CUDA works right out of the box, so your workloads run at full speed. Developers and data scientists can build, train, and deploy AI models without fighting hardware limits or complex configurations.
Your data stays secure on Hivenet's GPU cloud. The infrastructure scales when you need it and keeps your sensitive workloads protected. You pay per second with transparent billing—no hidden costs or surprise egress fees. You'll know exactly what you're spending.
Hivenet supports a wide range of GPU types. Pick the hardware that fits your needs and budget. Running intensive AI training? Deploying inference at scale? Experimenting with new models? The platform's high-performance GPUs handle it all.
Developers get robust APIs and automation tools. Integrate the GPU cloud into your existing workflows without hassle. Automate your AI model deployments. The support team is there when you need help—they'll guide you through setup, help optimize your workloads, and keep costs under control.
Hivenet delivers a secure, high-performance GPU cloud built for modern AI work. You can innovate without managing complex infrastructure. It's transparent, flexible, and reliable—everything you need to move your AI and machine learning projects forward in the cloud.
The future of cloud is smaller — and smarter
The future of cloud computing won’t be defined by scale alone. It will be defined by purpose. The neocloud isn’t about replacing AWS or Google Cloud; it’s about offering an alternative for AI workloads that value transparency, speed, and locality.
Compute with Hivenet shows what this future looks like — decentralized, efficient, and fair. It stands as both an AI-first cloud and a cornerstone of AI compute infrastructure.
For more practical guidance, you can revisit When to use a neocloud — and when you don’t need one to better understand how to align workloads with the right cloud model.
The era of hyperscale dominance is ending. The neocloud is what comes next.
To stay informed about the latest product updates, benchmarks, and tutorials, follow Hivenet's blog.
Frequently Asked Questions (FAQ)
How is a neocloud different from a hyperscaler?
A neocloud focuses on GPU-first infrastructure and AI workloads, while hyperscalers target general-purpose compute.
Is Compute with Hivenet cheaper than AWS or Google Cloud?
Yes. It offers lower GPU cloud pricing with per-second billing and no hidden fees.
Can I use Compute with Hivenet for training and inference?
Absolutely. It supports both use cases with on-demand RTX 4090 and 5090 GPUs.
Why are hyperscalers inefficient for AI workloads?
They rely on virtualized layers and centralized data centers that increase latency and cost.
What makes Compute with Hivenet more sustainable?
It reuses idle hardware to form a sustainable GPU cloud, cutting carbon output and extending hardware lifespans.
