Hyperscalers vs Neoclouds: Two Models for GPU Infrastructure

Hyperscalers;Amazon Web Services, Microsoft Azure, and Google Cloud Platform;are vertically integrated cloud platforms. They own the physical infrastructure (data centres, servers, networking), the virtualisation and orchestration layer, and a vast ecosystem of managed services built on top of compute.

AWS offers over 200 distinct services. GPU compute for these providers is one service among many.

Each provider wraps GPU compute in their own tooling;SageMaker on AWS, Azure Machine Learning, Vertex AI on Google;which provides convenience but also introduces lock-in. GPU pricing on hyperscalers runs at a premium to neoclouds: AWS P5 instances (8x H100, 96GB each) were priced at $98.32/hour on-demand in early 2026. The same compute on Lambda Labs was available at $24/hour;a 75% discount. This gap exists because hyperscalers price for availability, ecosystem, and SLA credibility; not for raw compute cost.

Neoclouds are purpose-built GPU cloud providers. They own or lease data centre space, deploy GPU servers at scale, and sell compute access;primarily to AI developers, researchers, and enterprises running training or inference workloads. They do not offer databases, content delivery networks, or email services.

They offer GPUs. CoreWeave;the largest neocloud by deployed capacity;raised $2.3B in 2024 and reached a reported $19B valuation ahead of its IPO. Lambda Labs built a dominant position in the developer market through competitive pricing and ML-framework integration.

Crusoe Energy differentiates by co-locating clusters at stranded energy sources, offering lower power costs and a carbon reduction narrative. The neocloud model prioritises GPU density, raw pricing, and ML-specific features over breadth. Competing with AWS on breadth is impossible; competing on GPU cost and capability is viable.

Hyperscalers win on three dimensions: availability, integration, and compliance. Availability: AWS can provision 10,000 GPUs in days if you have the budget and a relationship; most neoclouds operate at smaller scale with longer lead times.

Integration: if your data is in S3, your model training pipeline uses SageMaker, and your production application runs on EC2, the path of least resistance keeps your GPU workloads on AWS. Compliance: regulated industries;financial services, healthcare, government;often require specific certifications (SOC 2, FedRAMP, ISO 27001, HIPAA BAA).

Hyperscalers have invested heavily in these certifications. Most neoclouds hold some certifications but not the complete matrix that regulated enterprises require. For a FTSE 100 financial services firm moving AI workloads to production, hyperscaler compliance credentials are often the deciding factor.

Neoclouds win on price, GPU generation access, and ML-specific capability. The 50-75% discount versus hyperscalers on raw compute is real and durable.

Hyperscalers price for the ecosystem; neoclouds price for the GPU. For compute-intensive workloads;training runs that consume millions of GPU-hours;this cost difference determines whether a project is economically viable.

Neoclouds have often offered access to new NVIDIA hardware before hyperscalers. CoreWeave and Lambda both offered H100 capacity in 2023 before AWS and Azure could scale their respective fleets. Neoclouds also integrate deeply with ML tooling: Lambda's on-demand instances come pre-configured with PyTorch, CUDA, and common ML libraries. CoreWeave's Kubernetes-native platform integrates directly with Argo Workflows, Ray, and Kubeflow.

The choice between hyperscaler and neocloud is not binary. Most serious AI teams use both.

Training workloads;cost-sensitive, long-running, tolerant of some operational complexity;tend to migrate to neoclouds as teams mature and price sensitivity increases. Production inference;where latency SLAs, compliance requirements, and integration with existing services matter;often stays on hyperscalers.

The pattern is: prototype on a hyperscaler, train at scale on a neocloud, deploy inference on whichever meets your SLA. Organisations spending over $1M/year on GPU compute should evaluate this split explicitly. For provider-level pricing intelligence and contract negotiation support across hyperscalers and neoclouds, get in touch at disintermediate.global/services.

AWS P5 instances (8x H100) run at $98.32/hour on-demand; equivalent neocloud capacity runs ~$24/hour;a 75% discount that compounds significantly at scale (pricing data current as of Q1 2026;verify current rates with providers directly)

Hyperscalers win on availability (10,000 GPUs in days), ecosystem integration, and compliance certifications

Neoclouds win on price, early access to new GPU generations, and deep ML framework integration

Most mature AI teams use both: neocloud for training (cost-sensitive), hyperscaler for production inference (SLA and compliance)

Organisations spending over £1M/year on GPU compute should explicitly model the training/inference split across providers