The past two years have changed what enterprise AI actually costs. Not in theory – in practice, on real procurement calls, when organisations try to turn a proof of concept into a production deployment.
Earlier this month, one of our development teams while working on some building an enterprise RAG platform and tried scaling a RAG pipeline on current standard cloud infrastructure but they quickly ran into GPU allocation limits. This brings us to explore the growing usage of Nerocloud in enterprise AI infrastructure.
The demand for AI is easy to see. From ChatGPT-like applications and enterprise AI agents to RAG systems, model fine-tuning, and large-scale computer vision, organizations are investing heavily in AI capabilities. The bigger challenge often emerges behind the scenes. As these workloads move from experimentation to production, many teams find themselves constrained by GPU shortages, long provisioning delays, and cloud costs that continue to climb. Running AI at scale is no longer just about building models—it’s about securing the infrastructure needed to support them efficiently.
AWS, Azure, and Google Cloud were not built for this. They were built for everything – which means they were optimised for nothing in particular. A new category of provider has emerged to fill that gap. They are called NeoClouds, and they are increasingly appearing on serious enterprise infrastructure shortlists.
What Are NeoClouds?
A NeoCloud is a cloud provider whose entire business is GPU compute for AI and high-performance workloads. Not a product line within a broader platform. The whole company.
They focus primarily on:
- AI model training
- AI inference at scale
- GPU infrastructure management
- Large language model deployment
- Generative AI applications
Unlike hyperscalers, which serve every workload imaginable and treat GPU rental as one SKU among thousands, NeoClouds are purpose-built for AI. That distinction shows up throughout the stack.
Key characteristics:
- GPU-first infrastructure – the newest hardware gets prioritised here, not allocated as a side line
- AI-optimised networking – InfiniBand and high-bandwidth interconnects are standard, not optional upgrades
- High-performance storage – designed to keep GPU clusters fed, not to serve general object storage patterns
- Faster access to latest GPUs – providers compete on hardware recency; it is their core differentiator
- AI-focused support teams – engineers who understand distributed training, vLLM, and multi-GPU failures, not general cloud ticketing
Why NeoClouds Are Emerging
The immediate cause is straightforward: demand for AI compute has outrun what hyperscalers can provision.
The workloads driving that demand include:
- ChatGPT-style assistants and enterprise copilots
- RAG systems querying large internal document stores
- Model fine-tuning on proprietary data
- Enterprise AI agents running multi-step workflows
- Computer vision systems in retail, logistics, and manufacturing
Building new hyperscale data centres takes three to five years. AI adoption timelines are measured in quarters. That gap is not closing quickly.
As a result, organisations running serious AI workloads routinely face:
- Long provisioning times – quota requests, waitlists, and capacity constraints on the GPUs they actually need
- Unpredictable costs – on-demand GPU pricing designed for short-burst compute does not hold up under sustained training or inference loads
- Limited availability – specific GPU families, particularly the latest generations, are often unavailable at scale through general cloud channels
- Operational complexity – general-purpose platforms require significant engineering work to configure for AI-specific networking and storage patterns
NeoClouds exist because these problems are structural, not temporary.
NeoClouds vs Traditional Cloud Providers
| Feature | Traditional Cloud (AWS, Azure, GCP) | NeoClouds |
|---|---|---|
| Primary Focus | General cloud workloads | AI and GPU workloads |
| GPU Availability | Shared across services | GPU-first |
| Deployment Speed | Moderate | Faster for AI workloads |
| AI Expertise | General cloud support | AI-specialised teams |
| Cost Model | Broad pricing across services | AI-focused, per-GPU pricing |
| Hardware Recency | Balanced across product lines | Latest GPUs prioritised |
| Sovereign / Regional Options | Limited in some markets | Growing, especially UK and Europe |
The key point is not that NeoClouds are better than hyperscalers. It is that they are optimised for a different job. For general application hosting, data pipelines, or SaaS infrastructure, hyperscalers remain the sensible default. For sustained GPU-intensive AI workloads, the calculus often runs the other way.
Benefits of NeoClouds
Faster GPU Access
Organisations can provision high-performance GPUs without queuing behind quota systems or waiting for capacity that may not be available in the region they need. For teams with a model to train or a product to ship, that time difference is material.
AI-Optimised Infrastructure
The infrastructure is built specifically for:
- Large-scale model training across multi-GPU clusters
- Low-latency inference serving
- Fine-tuning open-source models on proprietary datasets
- Distributed training requiring fast inter-GPU communication
Running AI workloads on infrastructure tuned for general compute means engineering around constraints that should not exist.
Better Economics for Sustained Workloads
Many organisations find NeoClouds significantly more cost-effective than hyperscaler on-demand pricing for workloads that run continuously. The savings depend on matching commitment structures to actual usage patterns – GPU utilisation is the number that matters, not the headline hourly rate.
AI-Native Support
Support teams who understand the practical failure modes of distributed training: why a job stalls at 98% completion, how to tune vLLM for throughput versus latency, what InfiniBand topology affects model parallelism. That expertise is rarely available through general cloud support channels.
Types of NeoCloud Providers
Energy-Optimised NeoClouds
Examples: Crusoe, IREN
Focused on sustainable AI infrastructure co-located with renewable or stranded energy sources. Relevant for organisations with carbon commitments or ESG reporting requirements.
Developer-Focused NeoClouds
Examples: DigitalOcean, Hot Aisle
Designed for rapid deployment and developer productivity. Shorter provisioning cycles, cleaner APIs, and straightforward pricing. Better suited for teams shipping AI features quickly than organisations running petabyte-scale training runs.
Scale-Focused AI Providers
Examples: CoreWeave, TensorWave
Built for large-scale AI training and inference. CoreWeave is the largest in this category – surpassing $5bn in annual revenue, with Microsoft, Meta, and OpenAI among its customers. These providers operate at hyperscaler-like scale while remaining GPU-specialist in focus.
Enterprise and Sovereign AI Clouds
Examples: Core42 (UAE/Europe), IBM
Built for compliance, governance, and regulated industries. Core42 is the most active name in this space right now: a G42-backed company that recently raised $550M from HSBC, partnered with Red Hat and Microsoft Azure for sovereign-by-design infrastructure, and serves public sector, defence, and regulated industries across the UAE – with a European headquarters now established in Dublin. Its sovereign controls platform, Insight, gives regulated organisations technical and policy controls over data classification, residency, and AI workload governance. For organisations operating across the Middle East or in European regulated sectors, Core42 is an increasingly credible alternative to hyperscaler sovereign offerings. The question it answers is not just “where is the data” — but “who governs the full AI pipeline, including model weights, inference inputs, and audit trails.”
Who Should Consider NeoClouds?
AI Startups
- Building AI-native products requiring consistent GPU availability
- Fine-tuning open-source models (Llama, Gemma, Mistral) on proprietary data
- Deploying AI agents or inference services at scale
- Where hyperscaler pricing does not fit early-stage unit economics
SaaS Companies
- Adding AI features that require reliable, scalable inference infrastructure
- Running recommendation engines or personalisation systems with high throughput requirements
- Building AI capabilities that need to scale with product usage, not cloud quota cycles
Enterprises
- Internal AI assistants querying large knowledge bases
- Knowledge management systems with RAG pipelines across years of documentation
- AI-powered customer service platforms with real-time latency requirements
- Any organisation where data residency, UK GDPR compliance, or sector-specific regulation makes sovereign GPU infrastructure a hard requirement
Our CTO Take:Â
As many enterprise leaders have discovered, the current challenge is no longer in deciding whether to invest in AI, but the actual challenge is securing the cloud infrastructure needed to support AI initiatives efficiently and scale business growth.
How NeoClouds Fit Into Enterprise AI Architecture
A production AI stack built on NeoCloud infrastructure typically looks like this:
Applications layer
Internal AI assistants · Customer support agents · Sales and HR copilots · Knowledge management tools
Models layer
GPT-4o (via API) · Llama 3, Gemma, DeepSeek, Qwen (self-hosted) · Fine-tuned proprietary models
Frameworks layer
PyTorch / TensorFlow · ONNX · vLLM / Triton
Infrastructure layer
NeoCloud GPU platform · Kubernetes · High-speed InfiniBand or RoCE networking · AI monitoring and observability
The NeoCloud sits at the base of that stack, but it is not the only component. Orchestration, monitoring, integration with existing systems, and the application layer above it all require engineering work that is separate from – and at least as important as – the infrastructure choice itself.
What This Means for Businesses
Gartner estimates that by 2030, NeoCloud providers will hold around 20% of a $267bn AI cloud market. The hyperscalers themselves – Microsoft, Meta, Google – are already buying NeoCloud capacity because they cannot build data centres fast enough to meet their own demand.
For businesses evaluating AI initiatives, the practical question is not “should we use a NeoCloud” but “which workloads belong on specialist infrastructure, and what does our architecture need to support that.”
Organisations should assess whether NeoCloud platforms offer advantages across:
- Cost – total cost at realistic utilisation levels, not the headline rate
- Performance – throughput and latency under real application load
- Scalability – capacity that can grow with the workload without quota constraints
- GPU availability – particularly for specific GPU families needed at scale
- AI deployment speed – time from model to production
Where Carmatec fits is at the architecture and integration layer – workload characterisation before the procurement decision, orchestration and DevOps to run workloads portably, and the application engineering (RAG pipelines, inference services, agent frameworks) that sits above the compute. The infrastructure is the foundation. Making it work for a specific business problem is the harder piece.
Conclusion
NeoClouds represent a significant shift in how AI infrastructure is delivered. As AI adoption accelerates, organisations will increasingly look beyond traditional cloud providers to platforms designed specifically for AI-native workloads.
For businesses building AI applications, AI agents, RAG systems, or large-scale inference platforms, NeoClouds may provide a faster and more cost-effective path to production than continuing to work around the constraints of general-purpose cloud infrastructure.
If you are evaluating AI infrastructure options for your organisation, speak with our team to assess your workload requirements before committing to a provider.
