A neocloud is a category of cloud infrastructure provider built specifically for AI and machine learning workloads, offering GPU-dense computing at lower cost than traditional hyperscalers (AWS, Microsoft Azure, Google Cloud) by focusing narrowly on raw compute access rather than a broad suite of managed services. Neoclouds — also called GPU clouds or AI clouds — emerged prominently in 2023 as demand for NVIDIA H100 and A100 GPUs far outpaced hyperscaler supply, creating an opening for specialized operators to acquire GPU inventory, build purpose-built AI networking infrastructure, and serve training and inference workloads at 40-70% lower per-GPU-hour pricing.
Think of the difference between a full-service hotel and a well-equipped apartment rental. The hotel offers a spa, concierge, restaurant, and meeting rooms — comprehensive and convenient, but priced accordingly. The apartment gives you exactly what you need to work and rest, nothing more. Hyperscalers are the hotel: bundled, integrated, and priced to include every amenity. Neoclouds are the apartment: stripped down to GPU compute, purpose-built for AI workloads, and substantially cheaper per unit — the right choice if you know exactly what you need and don't want to pay for infrastructure you won't use.
For enterprises running large-scale AI training or high-volume inference, the cost difference between hyperscalers and neoclouds is material. Training a foundation model can cost $1-5 million in compute alone; at 40-70% savings per GPU-hour, the neocloud option can represent $500,000 to $2 million in direct cost reduction on a single training run. As AI compute transitions from a project expense to a recurring operating cost, enterprise technology leaders are evaluating neoclouds alongside AWS and Azure in their AI infrastructure strategy — particularly for sustained, GPU-intensive workloads that don't benefit from hyperscaler managed service integration.
A neocloud operates more like a specialized data center rental than a cloud platform. Where AWS offers hundreds of managed services — databases, serverless functions, networking tools, and monitoring — a neocloud like CoreWeave or Lambda Labs offers primarily one thing: direct access to GPU clusters at scale, often on bare-metal hardware with minimal abstraction between the customer and the silicon. You bring your software stack; they provide the hardware, high-speed networking, and power. The value proposition is compute density and cost, not service breadth.
Neoclouds differentiate on three dimensions: GPU inventory depth, interconnect architecture, and pricing structure. Distributed AI training requires extremely low-latency connections between GPUs — achieved through NVIDIA's NVLink for intra-node communication and InfiniBand for inter-node networking at scale. Neoclouds build these networks specifically for AI training cluster requirements rather than adapting general-purpose cloud networking. They typically maintain large reserves of specific GPU models — H100s, A100s — and offer reserved or spot pricing that undercuts hyperscaler on-demand rates. CoreWeave, for example, grew by securing large NVIDIA GPU allocations during the 2023-2024 supply crunch and offering access weeks or months faster than AWS or Azure could deliver equivalent capacity.
In foundation model development and large-scale AI research, neoclouds became the primary infrastructure choice for teams that needed large GPU clusters quickly and couldn't wait for hyperscaler capacity. Mistral AI trained several of its open-weight models on neocloud infrastructure. Stability AI and early generative AI startups built on CoreWeave and Lambda Labs when hyperscaler AI-optimized offerings lagged demand. For organizations building or fine-tuning large models on competitive timelines, neoclouds offered the combination of access speed and cost efficiency that hyperscalers could not match through 2023-2024 — and that cost advantage persists for compute-intensive workloads even as GPU availability has improved.
In enterprise AI programs, some organizations have adopted a hybrid architecture that uses neoclouds for training and batch inference while retaining hyperscaler relationships for applications requiring integrated managed services. A pharmaceutical company might train drug discovery models on CoreWeave's H100 clusters — where the workload is GPU-intensive and benefits from optimized interconnect architecture — then deploy the resulting model for real-time inference on Azure, where the managed Kubernetes and monitoring ecosystem reduces operational overhead. This hybrid approach requires more architectural work but can reduce total AI infrastructure spend by 30-50% for compute-heavy programs while preserving managed service access where it matters.
For enterprise technology leaders building multi-year AI infrastructure strategies, the neocloud landscape represents a legitimate cost optimization opportunity that requires a different evaluation framework than hyperscaler procurement. The GPU-hour price comparison is the starting point, not the conclusion — total cost must incorporate engineering overhead for platform management, vendor stability for commitment risk, and geographic and compliance coverage against actual requirements. Organizations with strong ML platform engineering capability and large, sustained training workloads get the most value. Those without that capability may find that hyperscaler pricing premiums are justified by the operational complexity they absorb.
The neocloud category has roots in the post-2012 deep learning boom, when NVIDIA GPUs became the standard compute substrate for AI research and the gap between general-purpose cloud infrastructure and AI-optimized data center design became apparent. Lambda Labs, founded in 2012 as an AI workstation company, pivoted to cloud GPU rental in 2018 and was among the first operators to offer bare-metal GPU access at scale. CoreWeave, founded in 2017 originally for cryptocurrency mining, pivoted to AI GPU compute in 2019 and grew rapidly by acquiring large NVIDIA GPU inventories when they became available at scale. The category remained a niche option for AI-native startups until 2022-2023, when ChatGPT's commercial success drove enterprise AI demand beyond what hyperscaler GPU capacity could satisfy.
The term "neocloud" entered mainstream enterprise vocabulary in 2023-2024 as analysts and procurement teams needed a category designation to distinguish these providers from hyperscalers in infrastructure planning and vendor evaluation. CoreWeave's 2024 IPO — at a valuation exceeding $19 billion — validated the category as a durable market structure rather than a temporary supply gap opportunity. NVIDIA reinforced the sector by maintaining strategic relationships with neocloud operators, including an equity stake in CoreWeave, recognizing them as a critical distribution channel for GPU capacity. As hyperscalers have expanded their own AI-optimized infrastructure and GPU availability has improved, the neocloud value proposition has shifted from primarily speed of access to cost efficiency at scale — with the architectural advantages of purpose-built AI networking increasingly the differentiator for large training and inference workloads.
A neocloud is a cloud infrastructure provider built specifically for AI and machine learning workloads, offering GPU-dense computing at 40-70% lower per-hour cost than hyperscalers by focusing on raw compute access rather than managed service breadth. The category emerged prominently in 2023 from GPU supply constraints and the cost requirements of large-scale AI training, with CoreWeave, Lambda Labs, Together AI, and Crusoe among the leading operators. The value proposition is real and material for compute-intensive AI programs — but limited managed services, vendor stability differences, and narrower compliance coverage require a more complex evaluation than per-GPU-hour price comparisons.
For enterprise leaders building AI infrastructure strategy, neoclouds are a legitimate component of the procurement landscape rather than a niche alternative. The organizations extracting the most value are those with strong ML platform engineering capability, large training workloads, and the operational discipline to manage bare-metal GPU infrastructure independently of hyperscaler tooling. For organizations without that foundation, the hyperscaler premium is often justified by the operational complexity it absorbs. The most competitive enterprises are evaluating both options against specific workload profiles rather than defaulting to either extreme.
