How to decide if your AI workloads belong on Compute with Hivenet or a traditional cloud platform
Choosing the right platform for your AI workloads depends on your performance, cost, and compliance needs. Neoclouds fill the gap left by traditional cloud providers by enabling faster and more cost-effective AI innovation.
Neoclouds are designed to foster AI innovation by providing specialized infrastructure for advanced AI workloads, such as high-performance GPU compute and distributed storage. This shift toward performance-focused cloud design is especially relevant for organizations seeking to accelerate experimentation and development in artificial intelligence. For more background, see our foundational articles on distributed cloud infrastructure and next-generation cloud platforms.
Understanding when a neocloud matters
Not every workload needs a GPU-first cloud. Some run perfectly well on traditional CPU-based infrastructure. But when performance, speed, or energy efficiency become bottlenecks, the neocloud starts to make sense.
A neocloud isn’t just a new term—it’s a practical answer to the growing divide between general-purpose compute and AI-driven workloads. Neoclouds are built for AI, with cloud architecture specifically designed to support demanding AI training and inference tasks, delivering bare metal performance and predictable throughput. Compute with Hivenet represents this shift toward performance-focused, sustainable cloud design, with neoclouds focusing on the unique requirements of AI-driven applications. Neoclouds are expected to capture significant market growth in the GPUaaS sector.
To better understand what a neocloud is, read our foundational article on neocloud infrastructure, which defines the term and its principles.
Workloads that thrive on a neocloud
The best way to decide if you need a neocloud is to look at your workload type. Neoclouds are designed to handle a wide range of AI workloads, including those that require specialized hardware, optimized performance, and scalable GPU infrastructure for demanding machine learning and analytics tasks. Here’s where Compute with Hivenet excels:
- AI training and fine-tuning: These tasks require massive parallelism, something GPUs handle natively. Compute with Hivenet’s RTX 4090 and 5090 instances make training large models faster and more cost-efficient. The infrastructure of neoclouds is purpose-built to accelerate the training and fine-tuning of large language models (LLMs), providing optimized GPU infrastructure and powerful GPUs for high performance computing and machine learning workloads.
- Inference at scale: If you’re deploying an AI model and need low latency, the distributed design of Hivenet’s Compute brings inference closer to users. Neoclouds leverage edge computing to deploy inference workloads on edge nodes, reducing latency by processing data geographically closer to end users.
- Rendering and simulation: Graphics, 3D animation, or physics simulations rely on GPU-heavy operations. Neoclouds offer better performance per watt than traditional setups.
- Scientific research and data analysis: Compute with Hivenet supports complex workloads where precision and processing power matter more than general compute flexibility. Neoclouds enable advanced analytics and support complex AI workload processing, making them ideal for scientific research and data-intensive tasks.
These scenarios benefit from GPU as a service models where performance and transparency are non-negotiable.
When traditional clouds still work
There are cases where hyperscalers still fit. Small websites, storage-heavy applications, and low-power automation don’t require GPU-level performance. If your workloads run fine on CPUs and scalability matters more than raw compute density, a traditional cloud might be cheaper. Traditional hyperscalers are better suited for general IT workloads and complex billing structures. However, traditional clouds may not efficiently meet the demands of resource-intensive AI workloads, especially when real-time scaling and rapid infrastructure adaptation are required.
The difference lies in specialization. Neoclouds like Compute with Hivenet aren’t designed to replace every cloud—they’re designed to do one thing exceptionally well: deliver predictable, high-performance AI compute. Neoclouds specialize in offering GPU-as-a-Service (GPUaaS) primarily for AI workloads. They also offer flexible consumption models, including pay-as-you-go options for GPU usage.
When deciding between cloud types, consider your team’s needs for control, cost predictability, and sustainability. If those matter as much as speed, a neocloud is likely the better fit.
Cost, control, and sustainability
Moving to a neocloud model often reduces both cost and waste. Transparent GPU pricing means you only pay for what you use. Compute with Hivenet’s per-second billing helps small teams experiment without fear of overages. Neoclouds also offer transparent hourly pricing for GPU resources, unlike traditional cloud providers which have complex, layered pricing structures. Neocloud providers give you one simple hourly rate per GPU that covers everything—networking, storage, and help with managing costs. You can cut your GPU expenses by up to 66% when you choose neoclouds over traditional cloud providers.Enterprises can save up to 66% on GPU costs when using neoclouds compared to traditional cloud providers. Neoclouds deliver competitive pricing, making them a cost-effective alternative to legacy cloud platforms.
There’s also control. Developers know exactly what hardware they’re running on—no abstracted tiers or mystery instances. And because Hivenet distributes workloads across existing idle devices, it also creates a sustainable GPU cloud that minimizes carbon footprint. Many neoclouds focus on building high-performance compute hubs that cater to specific regional data privacy and compliance requirements. Neoclouds leverage secure, compliant data centers to host AI infrastructure, ensuring enterprise-grade security and reliability. Neoclouds enable enterprises to rent GPU compute capacity instead of acquiring infrastructure themselves. Neoclouds prioritize data security and regulatory compliance while serving diverse clients from AI startups to Fortune 500 companies.
When your infrastructure aligns with your values—speed, transparency, and sustainability—you spend less time managing and more time building. However, organizations may face challenges when transitioning to a neocloud, such as managing infrastructure integration and adapting to new operational models.
Security and compliance in neoclouds
Your AI workloads are becoming the backbone of your business. Security and compliance aren't nice-to-haves anymore—they're must-haves. Neocloud providers get this. They build their AI infrastructure with solid security and regulatory compliance baked in from day one. If you're handling sensitive data, large language models, or proprietary algorithms, you need the peace of mind that comes with a secure, compliant environment.
Neoclouds don't stop at basic protections. They put advanced security measures at every layer. Your data gets encrypted whether it's sitting in storage or moving across the network. Access controls are detailed—you decide exactly who can touch your GPU instances, storage, and AI workloads. Regular security audits and continuous monitoring catch potential threats before they mess with your operations.
Compliance is where neoclouds really shine. The leading providers stick to industry standards like SOC2 and ISO 27001. They support regulatory frameworks like HIPAA and PCI-DSS too. This means you can deploy AI workloads knowing your infrastructure meets strict compliance requirements. Data sovereignty features let you pick where your data gets stored and processed, helping you meet regional regulations and privacy laws.
The AI infrastructure itself gets serious protection. Neoclouds protect GPU hardware—including powerful NVIDIA GPUs—using secure boot mechanisms, firmware updates, and intrusion detection systems. Your AI training, fine tuning, and inference tasks run on trusted, uncompromised hardware. Full support for NVIDIA CUDA and the latest GPU types comes standard.
You keep full control over your data and workloads. Tools for auditing, access management, and encryption let you manage your AI infrastructure according to your own compliance policies. This control matters especially if you're working on sensitive projects or in regulated industries.
Neoclouds work with your existing setup. They support multi-cloud and hybrid cloud architectures. You can deploy AI workloads across multiple cloud providers—including Google Cloud, Microsoft Azure, and traditional cloud providers—while keeping consistent security and compliance standards. You can use the strengths of each provider to improve your total cost of ownership, avoid unnecessary egress fees, and make sure your AI infrastructure is both secure and cost-efficient.
Neocloud providers are competing hard to deliver the most secure, compliant, and high-performance AI infrastructure available. Features like secure GPU instances, regulatory compliance frameworks, and transparent pricing help neoclouds meet the unprecedented demand for secure, scalable AI compute. If you're looking to innovate with confidence, neoclouds offer a solid foundation—combining security, compliance, and cost efficiency for every stage of your AI journey.
How to transition to a neocloud
Adopting a neocloud doesn’t require a full migration. Many teams use hybrid setups: training large models on Compute with Hivenet while keeping non-critical services on other providers. Many businesses use a hybrid cloud strategy, leveraging neoclouds for AI tasks and hyperscalers for traditional needs. Leading neocloud companies include Hivenet, CoreWeave, Crusoe, Lambda Labs, Nebius, and Vultr. Neoclouds can help democratize access to AI infrastructure by making advanced GPU resources more affordable for startups and research organizations.
This hybrid approach combines the best of both worlds—GPU-first performance where you need it and existing infrastructure where you don’t. With APIs and SSH access, Compute with Hivenet integrates easily into most pipelines. AI models and workloads can be easily deployed on neocloud platforms using automation and orchestration tools, allowing users to launch, manage, and scale resources efficiently.
If your workflows involve inference, distributed AI, or frequent retraining, moving part of your stack to a neocloud could pay for itself quickly.
The takeaway
Neoclouds represent a step forward, not a replacement. Compute with Hivenet isn’t just faster—it’s smarter, fairer, and built for the workloads that define the next era of computing. Neoclouds specialize in delivering bare metal performance computing tailored for demanding AI workloads.
If you’re running AI models, simulations, or rendering pipelines, the neocloud model offers clarity where old clouds offer complexity.
The question isn’t if you’ll move to a neocloud—it’s when.
Frequently Asked Questions (FAQ)
What kinds of workloads benefit most from a neocloud?
AI training, inference, and simulation tasks see the largest performance gains thanks to GPU-first design.
Can I mix Compute with Hivenet with my existing cloud setup?
Yes. Many users adopt hybrid strategies, running GPU-heavy workloads on Hivenet and other services elsewhere.
How does Compute with Hivenet pricing compare to AWS or Google Cloud?
It’s typically 50–60% lower for equivalent GPU power, with per-second billing and no hidden fees.
Is moving to a neocloud complex?
No. Compute with Hivenet supports common frameworks and APIs, so integration is straightforward.
Does a neocloud help reduce environmental impact?
Yes. Compute with Hivenet reuses idle devices, forming an eco-friendly AI compute network that’s efficient and sustainable.
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.
