Why Hivenet’s distributed design and GPU-first architecture make it a true neocloud platform
The rise of AI has changed what we expect from the cloud. Traditional infrastructures were built for general-purpose workloads—file hosting, app deployment, and basic compute. AI workloads broke that model. They demand dense GPUs, minimal latency, and pricing that doesn’t penalize long-running jobs. However, neocloud providers face significant challenges, including high GPU costs, increased energy consumption, and complex infrastructure requirements that make adoption and scaling difficult.
That’s where the idea of the neocloud comes in. It’s not a new buzzword; it’s a real shift in how infrastructure is built and delivered. Compute with Hivenet wasn’t designed to mimic hyperscalers—it was built to replace their inefficiencies with something faster, simpler, and more sustainable.
You can learn more about what defines a neocloud in our previous article on the rise of neocloud infrastructure, which explains how this model emerged.
Defining the neocloud model
The neocloud model revolves around three principles: GPU-first infrastructure, transparent pricing, and distributed design.
- GPU-first infrastructure: Neocloud providers center their architecture on GPUs rather than CPUs. This shift enables higher performance for AI training, inference, and rendering. The platform also supports advanced analytics and HPC workloads, leveraging parallel processors and high throughput to handle demanding analytics and data-intensive tasks. Compute with Hivenet deploys top-tier RTX 4090 and 5090 GPUs, giving users raw power for deep learning, simulation, and generative AI. Additionally, Hivenet enables the rendering of high-performance video, including 4K/8K content, and supports scientific simulations like climate modeling. Hivenet also provides templates to start with preinstalled runtimes and frameworks, simplifying the setup process for users. Users can quickly launch demand GPU instances, benefiting from a responsive infrastructure that adapts to workload needs.
- Transparent pricing: Where legacy cloud models hide costs behind egress fees or inflated hourly billing, neoclouds simplify. Compute with Hivenet charges per second, with clear hourly equivalents (€0.20/hour for RTX 4090 and €0.40/hour for 5090). This aligns cost with actual usage—ideal for iterative AI workloads. Users can save significantly on costs compared to traditional cloud providers.
- Distributed design: Instead of relying solely on central data centers, Hivenet’s Compute distributes workloads across global peers. This creates local compute availability, improves resilience, and aligns with Europe’s growing emphasis on data sovereignty and energy efficiency. Storage is a key component of the infrastructure, and the distributed model removes limits on scaling and allows users to build custom solutions without restrictions.
Neocloud vendors are driving growth in the AI infrastructure market, enabling enterprises to explore new AI strategies and build tailored solutions for analytics, machine learning, and data processing.
Together, these principles define what makes Compute with Hivenet a real-world implementation of the neocloud model. Neoclouds can provide a larger number of parallel processors—including diverse processor types such as AI-specific hardware—higher bandwidth, and larger memory pools than traditional data centers, making them ideal for modern AI workloads that require high throughput and advanced analytics.
Introduction to Neocloud
Neoclouds are built for AI work. They're different from regular cloud providers because they focus on what AI projects actually need: fast access to powerful GPUs and infrastructure that won't break under heavy workloads. Companies need more computing power these days. Machine learning, deep learning, and generative AI all demand serious resources, and neoclouds stepped in to fill that gap.
These providers focus on GPU-as-a-Service and other AI tools, making it easier for teams to get the computing power they need when they need it. This speeds up AI development and lets businesses scale up or down based on what their projects require. AI's changing how we build technology, and neoclouds help companies keep up without the headache of managing complex infrastructure themselves.
Benefits of Neocloud Providers
Neocloud providers offer clear benefits that make them worth considering when you're running AI workloads. The biggest win is cost. You'll often save real money compared to traditional clouds—some customers cut their AI infrastructure costs by 80%. This happens because pricing is transparent, you're billed per second, and there aren't hidden costs. You pay for what you use.
You get fast access to GPU hardware too. Neoclouds let you spin up GPU instances when you need them, so your AI workloads launch and scale without delays. This speed matters when project requirements change or demand spikes. Neocloud providers also focus on networking and performance, giving you secure, reliable infrastructure that handles demanding AI workloads.
When you use neoclouds, you can scale your AI infrastructure without big upfront hardware investments. This opens up advanced AI capabilities to organizations of all sizes, helping them innovate, experiment, and grow with confidence.
GPU-first performance at human scale
Neoclouds exist because developers want direct access to hardware—not layers of virtualization and bureaucracy. Compute’s design philosophy reflects that. Users can spin up GPU instances instantly, connect via SSH or API, and run workloads at native speed. The platform also allows users to connect to their Hivenet instance via SSH or a web console, providing flexibility in how they manage their workloads. The platform’s user-friendly interface includes features for quick deployment and monitoring usage via a simple dashboard.
By focusing on GPU as a service, Compute with Hivenet makes AI compute infrastructure accessible to anyone—from independent developers to research labs. You get predictable pricing, scalable performance, and a clear understanding of what your compute power delivers. Additionally, Hivenet provides access to virtual workstations equipped for demanding professional software use, making it suitable for fields like architecture and video editing without the need for expensive local hardware.
For AI inference, fine-tuning, and small-scale training, Hivenet enables users to efficiently run inference tasks on dedicated GPU instances, ensuring optimal performance without the overhead of renting entire clusters. It’s power scaled to human projects, not enterprise sprawl.
To transition smoothly: this performance foundation feeds directly into Hivenet’s distributed approach, ensuring every computation happens closer to where data lives.
Distributed by design
The neocloud era also rethinks geography. Where the old cloud centralized data, Compute with Hivenet spreads it intelligently. Its distributed model connects peers across regions, reducing latency and energy waste. Workloads on Hivenet are run on a distributed infrastructure, ensuring security and resilience. Hivenet's distributed network utilizes idle computing power from a global community of devices instead of centralized data centers, further enhancing its efficiency and sustainability.
That distribution isn’t only about speed—it’s about sovereignty. In a world where data jurisdiction matters, running workloads closer to where data is generated gives teams both compliance and performance. For European developers, this matters. It keeps compute local and privacy intact while reducing dependence on non-sovereign infrastructures.
This distributed GPU cloud approach aligns with both the ethics and the efficiency expected from the next generation of cloud computing.
AI Workload Management
AI workloads need good management to run well and cost less. Neoclouds give you a set of tools that make this easier, from training your first model to running real-time predictions and handling ongoing machine learning work. You get access to NVIDIA GPUs and other AI hardware, so your workloads run faster and scale when you need them to.
Neoclouds let you focus on building and deploying AI models instead of wrestling with infrastructure. Your teams can spend more time creating solutions and less time figuring out servers or fixing hardware problems. When you need more power, you scale up. When you don't, you scale down. It's that simple, and your workloads keep running without breaking your budget.
Security and compliance matter too. Neoclouds protect your sensitive data and help you meet regulatory requirements, so you can work on AI projects without worrying about data breaches or compliance headaches. You get the confidence to move forward with your work.
Sustainability built in
Every neocloud promises better performance. Few address the environmental cost. Compute with Hivenet does both. By using idle devices and existing power footprints instead of energy-hungry data centers, it builds a sustainable GPU cloud from the ground up. Hivenet offers significant savings compared to major cloud providers, being up to 58% cheaper due to its decentralized model and use of consumer-grade GPUs.
That’s not a side benefit—it’s a design choice. It reduces e-waste, lowers cooling requirements, and decentralizes ownership of compute power. The result: an eco-friendly AI compute network that supports global workloads without adding new carbon debt. Data on the Hivenet platform is encrypted before leaving the user's device, ensuring that unauthorized personnel cannot access user data without encryption keys.
Sustainability isn’t just good ethics—it’s good engineering. A leaner, distributed network means lower costs, lower latency, and longer hardware life cycles.
Security and Compliance
You need your AI workloads secure when you're working in the cloud. Neocloud providers handle this with data centers that protect your information, encryption that keeps data safe, and access controls that let you decide who sees what. These steps help shield sensitive data and keep your AI systems running when threats appear.
Clear pricing helps you plan better. You won't find surprise costs, so you can invest in AI infrastructure knowing exactly what you'll spend. Neoclouds let you grow your AI work as your business expands while keeping the same security standards.
Hivenet uses ISO 27001 certified data centers and follows strict compliance rules. This means you can build your AI projects faster while keeping your data safe and meeting industry standards. With neoclouds, you can grow, build new solutions, and compete in AI knowing your work stays protected and compliant.
Why transparency matters
Pricing opacity is one of the biggest pain points in the traditional cloud. Developers spend hours predicting costs instead of building. Neoclouds simplify that equation by aligning usage and billing.
Compute with Hivenet’s transparent GPU pricing removes friction. You know exactly what each GPU costs and when billing stops. That’s crucial for AI teams experimenting with model tuning, where runs can vary from minutes to days.
This isn’t just about saving money—it’s about psychological clarity. When cost anxiety disappears, iteration accelerates. This transparency strengthens Hivenet’s position as an AI-first cloud provider built for innovation.
Hivenet and the future of the neocloud
Compute with Hivenet demonstrates what a mature neocloud can be: distributed, transparent, and purpose-built for AI. It doesn’t rely on marketing slogans or unsustainable expansion—it relies on rethinking what cloud should be in the age of intelligence.
As AI workloads grow more complex, the balance between accessibility, sovereignty, and sustainability will define success. Hivenet’s distributed model already embodies that balance.
The neocloud isn’t theoretical anymore—it’s here, and Compute with Hivenet is part of it.
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Frequently Asked Questions (FAQ)
How does Compute with Hivenet fit the neocloud model?
It combines GPU-first infrastructure, transparent pricing, and distributed architecture—three defining traits of the neocloud movement.
What benefits does distributed GPU cloud offer?
It reduces latency, improves regional sovereignty, and makes compute more resilient and energy-efficient.
Why is transparent GPU pricing important for AI teams?
It helps developers control costs in real time, especially during model training and inference, where usage can change quickly.
How does Compute with Hivenet make GPU as a service more accessible?
By offering per-second billing and direct GPU access, it democratizes AI compute infrastructure for all project sizes.
Is Compute with Hivenet sustainable?
Yes. It uses existing idle devices instead of new data centers, creating a truly sustainable GPU cloud that supports eco-friendly AI compute.
How is Compute with Hivenet different from AWS or Google Cloud for AI workloads?
It offers GPU-first access, transparent billing, and distributed design focused on sustainability—key differences from traditional hyperscalers.
