Why the future of AI infrastructure depends on fair costs, predictable billing, and smarter design.
That’s why the neocloud model emerged — to fix the economics of high-performance computing. Users can quickly create a cloud account to access GPU and storage resources, making setup fast and straightforward. With just a few clicks, you can spin up GPU instances on demand for AI workloads or new projects, enabling immediate access without long-term commitments.
For a detailed comparison of cost and performance, read Neocloud vs Hyperscalers — it shows why traditional providers can’t match neocloud efficiency.
Why cloud economics had to change
The first generation of cloud computing promised elasticity. You paid for what you used — in theory. In practice, hidden fees, unpredictable egress costs, and complex pricing tiers made budgets hard to control.
AI workloads made this worse. Training large models, fine-tuning, and inference aren’t like hosting a website. Each workload is compute-intensive, unpredictable, and expensive. Hyperscalers optimized for scale, not fairness, and are actively deploying new hardware and strategies to maintain their dominance. Neocloud infrastructure, by contrast, is purpose-built around high-value assets such as the latest NVIDIA GPUs—including PCIe-based models for optimal connectivity and performance—and advanced cooling systems, addressing the unique demands of AI workloads. Cloud GPU providers now offer on-demand, enterprise-grade cloud GPUs for AI training, fine-tuning, and inference, with major providers like Amazon Web Services, Google Cloud Platform, Microsoft, and Microsoft Azure leading the market. Enterprise clients can access dedicated clusters for exclusive computing resources, personalized support, and SLAs. These resources are strategically deployed to maximize efficiency and performance, with bandwidth up to 1 Gb/S per instance. Users can connect to GPU instances directly from their web browser, eliminating the need for extra software. The neocloud model also emphasizes comprehensive support for users, efficient scaling of AI workloads, and the flexibility to support different types of workloads. There are no limits on building AI applications, enabling true scalability and freedom for innovation.
This shift marks the rise of the AI-first cloud, one built around real usage, not arbitrary billing cycles.
Market context and trends
The cloud GPU market is changing fast. AI workloads are growing, and people need more computing power. Organizations are working to train, fine-tune, and deploy complex AI models. They need scalable GPU instances more than ever. Cloud GPU providers—Google Cloud, AWS, Azure, Lambda Labs, and DGX Cloud—are expanding what they offer. This makes it easier for developers and companies to access the latest NVIDIA GPUs and get the most from NVIDIA CUDA for deep learning and machine learning work.
Multi-GPU instances and dedicated GPU clusters are becoming popular. These solutions help AI startups and companies scale their training and inference workloads. You can use one or more GPUs per instance to speed up model development and deployment. The choice between virtual machines and bare metal instances means you can pick what works best for performance and compliance. Whether you're building new AI infrastructure or working with existing systems, you've got options.
Cloud providers are listening to what customers want: clear, fair pricing. The industry is moving away from confusing bills and hidden costs—like egress fees. Instead, you'll see competitive pricing with per-second billing and rates you can predict. This change helps developers and companies manage budgets better, cut waste, and focus on building instead of accounting.
The market is growing up, and distributed infrastructure matters more now. Edge computing and IoT are pushing cloud providers to expand their data centers and try new technologies. They want to give you low-latency access to GPU capacity wherever you need it. In Europe, providers like Seeweb, Datacrunch.io, and OVHcloud are doing well. They offer high-performance GPU instances with strong security, compliance, and sustainability—things that matter if you work in regulated industries.
New companies and established players are both investing in AI hardware, GPU clusters, and cloud-based machine learning platforms. The cloud GPU world is more dynamic and competitive than it's been. For AI developers, researchers, and companies, this means better access to high-performance GPU infrastructure. You'll find more instance types to choose from and the ability to deploy and scale AI models when you need them—without hidden costs or long contracts. If you are considering choosing a distributed compute provider, make sure to assess your specific needs and options carefully.
Innovation is speeding up and competition is getting stronger. The cloud GPU market will deliver more powerful, flexible, and affordable solutions for the next wave of AI workloads. Whether you're an AI startup training deep learning models or a company looking to improve your AI infrastructure, the opportunities to build, scale, and deploy with confidence are better than they've ever been.
What transparent pricing really means
In a GPU cloud, every second of compute time counts. Neoclouds like Compute with Hivenet bring clarity to that equation. You know what you pay, when billing starts, and when it stops. They also provide bare-metal or thinly virtualized access to raw GPU power, enabling faster model training and high-throughput inference for AI applications. Prebuilt templates for AI workloads include frameworks like TensorFlow and PyTorch, simplifying deployment for developers. NVIDIA offers various GPUs like H100 and A100 for AI workloads, ensuring compatibility with cutting-edge AI demands.
Compute with Hivenet offers RTX 4090 GPUs for around €0.20/hour and 5090s for €0.40/hour, billed per second. No setup costs. No egress fees. No long-term contracts. This is what transparent GPU cloud pricing looks like, saving users up to 70% compared to traditional clouds.
That transparency isn’t marketing — it’s math. For small AI teams, it means fewer budget surprises. For researchers and enterprises, it means accurate forecasting. Real-time performance insights help manage AI workloads without overhead. The neocloud turns compute from a gamble into a predictable asset.
For a broader context, see Neocloud vs Hyperscalers, which compares these cost models in depth.
The inefficiency of hyperscale billing
Traditional cloud pricing is built on abstraction. You rent virtual instances, not actual hardware. This works fine for light applications but wastes money on GPU-heavy tasks.
In hyperscale systems, you pay for the convenience of scale — and for idle time. Billing clocks often continue even when instances sit unused. By contrast, Compute with Hivenet uses direct GPU access and per-second billing, so you pay only for active compute. Cloud GPU services often feature pay-as-you-go billing models, allowing users to pay only for the resources they consume, making them more cost-effective for dynamic workloads.
Over time, this model redefines the economics of AI compute infrastructure. It’s leaner, fairer, and measurable. The primary metric for neoclouds is maximizing performance for AI tasks relative to cost, emphasizing reliability, stability, and speed. Forecasts suggest that the overall AI infrastructure spend will exceed $100 billion by 2025. AI workloads drive over half of data center power use and 70% of new revenue opportunities as of 2025. Hyperscalers control well over 80% of the global AI compute capacity as of early 2025, but the consensus estimate for the serviceable addressable market for neo-cloud providers is roughly $25 billion through 2027.
Predictability as a feature
Developers don’t just want cheaper compute — they want stability. Predictability builds trust. When you know exactly what each run costs, experimentation becomes easier and safer.
Compute with Hivenet’s transparent GPU cloud pricing removes the anxiety of unexpected bills. Teams can train, test, and deploy with confidence. That stability accelerates innovation — because fewer resources go to accounting.
Distributed efficiency
The neocloud isn’t just about pricing; it’s about efficiency. Hivenet’s distributed design lets workloads run closer to where data lives, reducing latency and wasted energy. Neoclouds strategically locate data centers in regions with low-cost, abundant power to manage operational expenses and achieve low Power Usage Effectiveness (PUE). Unlike traditional models that assume perfect information and equilibrium, the neocloud market is characterized by intense competition, rapid technological cycles, and resource scarcity. To launch a successful neo-cloud, it is crucial to secure silicon supply and power contracts at favorable terms.
This structure lowers operational costs. Each node contributes existing capacity, avoiding the overhead of new data centers. Neoclouds implement an economic model for the lifecycle of expensive AI hardware, allowing GPUs to be repurposed for less demanding AI inference workloads after use in training. Neoclouds concentrate their capital expenditure solely on high-value, cutting-edge hardware, primarily the latest GPUs and high-speed networking. That makes Compute with Hivenet not only affordable but also sustainable — a sustainable GPU cloud built for real-world economics.
The result is efficiency that benefits everyone: developers, researchers, and the planet.
Why fairness matters in compute
Cloud pricing isn’t just a technical issue; it’s an ethical one. Access to computation shapes who can participate in AI research and development. Neoclouds facilitate democratization of AI by providing accessible, cost-effective GPU power on-demand, allowing startups to access enterprise-grade resources. Specialized neoclouds can meet strict data residency and compliance requirements for industries like healthcare and government. Neoclouds can offer AI-specific infrastructure at significantly lower costs, sometimes 60-70% less for equivalent compute than hyperscalers. Neo-cloud providers are proving there is a serviceable gap in the market for on-demand GPU hours. When compute costs drop and transparency improves, innovation becomes more democratic.
That’s why neocloud economics matter. Compute with Hivenet doesn’t sell convenience — it sells fairness. When you pay only for what you use, you get freedom to build without financial friction.
The bottom line
The economics of the neocloud reflect a broader principle: trust through transparency. Compute with Hivenet proves that performance, fairness, and sustainability can coexist.
AI workloads are growing. Costs shouldn’t grow with them.
To see how the neocloud model fits within the bigger cloud ecosystem, read The future of cloud sovereignty — why the neocloud matters for Europe.
To continue the series, explore Sustainability in the neocloud era — how distributed compute cuts waste while improving efficiency, and discover eco-friendly cloud storage solutions bridging technology and sustainability.
Frequently Asked Questions (FAQ)
What makes neocloud pricing different from hyperscalers?
It’s simple, predictable, and billed per second — no hidden fees or forced commitments.
Does Compute with Hivenet charge egress fees?
No. Data transfers and storage are included in the hourly rate, keeping costs transparent.
Who benefits most from transparent GPU pricing?
AI researchers, developers, and startups who need budget control and scalability.
How does distributed design reduce costs?
By reusing existing devices instead of building new data centers, reducing both energy and infrastructure expenses.
Is Compute with Hivenet sustainable?
Yes. It runs on distributed nodes, forming an eco-friendly AI compute network that lowers cost and carbon impact.
