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What is a Neocloud?

A practical guide to next-generation cloud infrastructure, written for teams evaluating AI and GPU capacity across Asia.

The word neocloud shows up everywhere in 2025–2026 AI coverage, but definitions vary. This page is the working definition we use across our directory: who counts as a neocloud, how they differ from hyperscalers and colocation, where the category is strongest in Asia, and how to evaluate providers for your workload.

If you're here to find a neocloud rather than define one, jump straight to the data center directory or contact a provider via our team.

Definition: what counts as a neocloud

A neocloud is a cloud provider whose entire infrastructure — facilities, networking, storage, and scheduler — is purpose-built for AI workloads rather than retrofitted from general-purpose cloud. Practically, three traits distinguish neoclouds from other infrastructure providers:

  • GPU-first capacity. The provider's compute inventory is dominated by high-end AI accelerators (NVIDIA H100 / H200 / B200, AMD MI300X, Google TPU) rather than a mix of CPUs and GPUs.
  • Low-overhead primitives. Customers get bare-metal or single-tenant GPU access, InfiniBand or high-bandwidth Ethernet interconnect, and storage tiered for training throughput — not just virtual machines with attached GPU.
  • Pricing on GPU-hours. The unit of sale is the accelerator (per GPU-hour, per node-hour, or reserved GPU capacity) rather than rack space or generic VM time.

Operators commonly described as neoclouds include CoreWeave, Lambda, Crusoe, Vultr, Vast.ai, RunPod, and TensorWave in North America; in Asia the category includes regional operators plus arms of larger groups (YTL, VNET, GDS, NTT GDC) operating GPU-focused capacity. Neocloud Asia is not a neocloud — it's the marketplace you're reading; we catalog neoclouds alongside colocation, hyperscaler regions, and edge facilities.

Neocloud vs. hyperscaler

Hyperscalers — AWS, Azure, Google Cloud, Oracle Cloud, Alibaba Cloud, Tencent Cloud — run huge general-purpose clouds. AI workloads are a growing line of business but live on the same infrastructure as everything else: shared regions, multi-tenant VMs, capacity pooled across millions of customers.

The trade-off is straightforward:

  • Hyperscalers offer the broadest catalog, the deepest integrations, the strongest SLAs, and the easiest path to existing data and tools. They lose on newest-GPU availability (allocations are tight, especially for H100/B200) and on per-GPU price for sustained training.
  • Neoclouds win on newest-accelerator access, AI-specific networking, transparent pricing, and engineering access to the team running the facility. They lose on ecosystem breadth (no managed Kafka, no IAM, no equivalent of S3) and on multi-region failover.

In practice, many AI teams use a mix: a hyperscaler for data warehousing, feature stores, and orchestration; one or more neoclouds for the actual training and inference jobs where they need specific GPUs at known cost.

Neocloud vs. colocation

Colocation providers — Equinix, Digital Realty, NTT GDC, KDDI, GDS, Chindata, YTL — rent rack space, power, and cooling. You bring your own servers, your own network, and your own ops team. Many hyperscalers and neoclouds lease colocation capacity for their GPU clusters, which is part of why the categories get blurred in marketing.

The user-facing difference:

  • Colocation is for teams that want to own and operate their own hardware — typically for compliance, cost-at-scale, or custom silicon. You handle procurement, hardware lifecycle, and the MLOps stack.
  • Neoclouds handle the hardware so you don't have to. You pay a premium over colocation unit economics in exchange for elastic, on-demand GPU access and zero hardware ops.

Three infrastructure models, side by side

The diagram below compares the three infrastructure models across the five traits that most influence buyer decisions. Each row is one trait; each column is one model.

Hyperscaler vs Neocloud vs Colocation — infrastructure model comparison Five-row comparison chart. Rows: GPU inventory, interconnect, pricing, ops overhead, time to deploy. Columns: Hyperscaler, Neocloud, Colocation. Trait Hyperscaler Neocloud Colocation GPU inventory Which accelerators, how many Shared pool latest gen scarce GPU-dense H100 / B200 priority Bring your own whatever you ship Interconnect Network topology Ethernet region-pooled InfiniBand NDR tightly-coupled Your choice you build the fabric Pricing model Unit of sale vCPU-hour + GPU attach fee GPU-hour + reserved / commit kW + U-space month-to-month Ops overhead What you operate Workloads only no infra Workloads only no infra Hardware + net + MLOps stack Time to deploy From signup to workload Minutes subject to GPU stock Minutes–hours GPU stock visible 3–18 months procure + build
Neocloud column highlighted in orange — the column buyers come to this page to compare.

Why the category is strongest in Asia right now

Three structural forces have made Asia the second-largest neocloud market in the world, behind North America:

  1. Enterprise AI demand is compounding. Banks, manufacturers, telcos, and e-commerce platforms in Singapore, Seoul, Tokyo, Mumbai, and Shanghai have moved AI from pilot to production. Many of them prefer regional GPU capacity over US hyperscalers for latency and data-residency reasons.
  2. Data-residency rules favor regional capacity. China's PIPL, India's DPDP Act, Singapore's PDPA, and sectoral rules in Indonesia, Vietnam, and Thailand push certain workloads — especially finance, healthcare, and government — onto in-region GPU infrastructure. Hyperscaler regions don't always cover every regulated workload.
  3. Government incentives. Singapore's AI Verify framework, India's IndiaAI mission, Japan's METI GPU subsidies, and South Korea's K-Cloud program all explicitly back domestic GPU capacity. That tilts the market toward regional providers.

When a neocloud is the right choice

Neoclouds shine when the workload fits one of these profiles:

  • Large training jobs on the newest accelerators. Pre-training or fine-tuning on H100 / H200 / B200 clusters where capacity and InfiniBand bandwidth matter more than ecosystem integrations.
  • Burst capacity beyond your colocation footprint. You run a private cluster but need extra GPUs for a 2-week training run without buying them.
  • Region-pinned inference. You serve a model to Asian users with strict latency or residency requirements and need GPU capacity in-region.
  • Startup-to-scale compute. You're a young company that wants per-hour GPU economics without committing to multi-million-dollar capex.

Neoclouds are less suited when you need deep integrations with the hyperscaler ecosystem (BigQuery, S3, IAM, Azure AD), or when your workload is CPU-dominated — at which point a general cloud or colocation is the better fit.

How to evaluate a neocloud provider

Five axes matter, in roughly this order:

  1. GPU inventory. Which accelerators, how many, what generation. A provider with 5,000 H100s and a provider with 200 H100s and 800 MI300X are very different businesses.
  2. Interconnect. InfiniBand NDR / XDR vs. 200/400 GbE. Intra-cluster bandwidth decides whether multi-node training is viable.
  3. Data residency. Where the GPUs physically sit, and whether the provider offers region pinning or sovereign-cloud options for regulated workloads.
  4. Pricing model. Hourly on-demand, monthly reserved, multi-year commit. Egress fees. Whether you can mix GPU types in one cluster.
  5. Support. Slack channel access to engineers, or ticket-only. Onboarding help with scheduler setup, or self-serve.

A practical short-list approach: pick 2–3 providers on each axis that fit your workload, run a 1–2 day benchmark on a representative training job, and let cost-per- GPU-hour × actual throughput pick the winner.

Find a neocloud or data center in Asia

Neocloud Asia catalogs 300+ data center facilities across 20+ countries, including GPU-first neocloud operators alongside colocation, hyperscaler regions, and edge facilities. Browse by country, by city, or by provider; each facility entry lists published power capacity, interconnect count, fiber connections, and cloud on-ramps where available.

If you're comparing neoclouds specifically, filter for high power capacity (≥ 20 MW per site is a reasonable heuristic for a GPU-dense neocloud) and high interconnect counts. The same data also helps you evaluate colocation options for your own private GPU cluster.

Neocloud FAQ

The questions buyers ask most often when evaluating neocloud providers.

What is a neocloud?
A neocloud is a category of cloud provider that emerged in the 2020s, purpose-built for AI training and inference workloads. Unlike hyperscalers (AWS, Azure, GCP) that retrofit AI onto general-purpose infrastructure, neoclouds operate GPU-dense data centers, expose bare-metal or low-overhead compute primitives, and price on GPU-hours rather than virtual machines. The category also differs from traditional colocation: neoclouds sell managed GPU capacity rather than rented rack space.
How is a neocloud different from a hyperscaler?
Hyperscalers (AWS, Azure, Google Cloud, Oracle Cloud) offer broad service catalogs across compute, storage, databases, and AI — but their GPU inventory is shared across millions of customers, capacity is region-pooled, and AI workloads are typically afterthoughts on a general cloud. Neoclouds are GPU-first: their entire infrastructure stack (networking, storage, scheduler) is optimized for tightly-coupled AI training and high-throughput inference. They offer faster access to the newest accelerators (H100, B200, MI300), lower per-GPU-hour pricing in many cases, and direct engineering support.
How is a neocloud different from colocation?
Colocation providers rent physical rack space, power, and cooling — you bring and operate your own servers. Neoclouds sit one layer up: they own and operate the GPU servers themselves and sell them as managed compute (hourly, monthly, or reserved). You consume GPUs the way you consume AWS EC2, but with capacity purpose-built for AI rather than general workloads.
Why are neoclouds growing so fast in Asia?
Three forces converge in Asia: (1) enterprise AI adoption in finance, manufacturing, and e-commerce is growing 30%+ year-over-year; (2) regional data-residency rules push AI workloads to local GPU capacity rather than US/EU hyperscalers; (3) several Asian governments (Singapore, India, Japan, South Korea) offer tax incentives or sovereign AI programs that favor local GPU providers. The combination has made Asia the second-largest neocloud market after North America.
How do I choose a neocloud provider?
Evaluate on five axes: (1) GPU inventory — which accelerators (H100, H200, B200, MI300X, TPU) and how many; (2) interconnect — InfiniBand vs Ethernet, intra-region bandwidth, and peering to public clouds; (3) data-residency — physical region of the GPUs and whether the operator offers regional pinning; (4) pricing model — hourly, reserved, or committed-use, and egress fees; (5) support tier — whether engineering staff are available for architecture work or just break-fix tickets.
Is Neocloud Asia a neocloud?
No. Neocloud Asia is an Asia-wide marketplace that catalogs and connects buyers with data center facilities, including neocloud operators, traditional colocation, hyperscaler regions, and edge facilities. We do not sell GPU compute ourselves; we help buyers compare and contact providers across 300+ facilities in 20+ countries.

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