AI Neoclouds 2026: 20 GPU Cloud Providers Compared

Buyer's map

20 AI Neocloud Providers

Verified through October 26, 2026. This compares provider positioning and access—not real-time inventory, benchmark performance, or quoted contract terms.

20

Providers

5

Types

4

Regions

12

Self-serve

SearchProvider typeAll typesFull-stack AI cloudEnergy-integratedModel & inference cloudDeveloper GPU cloudSovereign / regionalRegionAll regionsNorth AmericaEuropeIndiaAsia-PacificAccessAll accessSelf-serveSelf-serve + salesContract / reservationSortName A–ZProvider typeRegion

20 of 20 providersHardware and availability must be reconfirmed before procurement.

Provider	Type / region	Access	Accelerators	Best for	What stands out
Civo Florida, US Private	Developer GPU cloud North America	Self-serve + salesVerify source	NVIDIA GPU instances and Kubernetes clusters	Cloud-native teams that want GPU compute attached to a simple managed-Kubernetes experience	Developer experience and Kubernetes simplicity are the product center, with GPU capacity integrated underneath.
CoreWeave New Jersey, US Public · NASDAQ: CRWV	Full-stack AI cloud North America	Contract / reservationVerify source	Large NVIDIA clusters, including Blackwell systems	Frontier training, production inference, and very large reserved clusters	The largest pure-play AI cloud, with a Kubernetes-native platform and hyperscale customer commitments.
Crusoe Colorado, US Private	Full-stack AI cloud North America	Contract / reservationVerify source	NVIDIA GPU clusters and managed AI services	Large training and inference deployments tied to purpose-built data centers	Integrates energy, data-center construction, and cloud operations instead of treating compute as a resale layer.
E2E Networks New Delhi, India Public · NSE: E2E	Sovereign / regional India	Self-serve + salesVerify source	NVIDIA B200, H200, H100, and regional clusters	Indian startups and enterprises seeking local, self-service AI infrastructure	Combines a long operating history in Indian cloud with rapid GPU provisioning and domestic support.
Firmus Tasmania, Australia Private	Energy-integrated Asia-Pacific	Contract / reservationVerify source	NVIDIA AI factory infrastructure	Australian and Asia-Pacific sovereign or dedicated AI capacity	Builds liquid-cooled AI factories around renewable-energy access across Australia and Southeast Asia.
Fluidstack London, UK Private	Full-stack AI cloud Europe	Contract / reservationVerify source	Dedicated NVIDIA superclusters	AI labs and enterprises procuring large dedicated training capacity	Pairs managed GPU clusters with a fast-growing portfolio of purpose-built AI data-center campuses.
GMI Cloud California, US Private	Model & inference cloud North America	Self-serve + salesVerify source	NVIDIA H100, H200, and Blackwell infrastructure	Teams progressing from serverless inference to dedicated bare-metal clusters	One control plane spans GPU infrastructure, optimized inference, and agent runtime services.
Hyperstack London, UK Private	Developer GPU cloud Europe	Self-serve + salesVerify source	NVIDIA GPU virtual machines and clusters	European on-demand training, inference, rendering, and private clusters	Minute-level billing and a broad SKU range on renewable-powered European and North American infrastructure.
IREN Sydney, Australia Public · NASDAQ: IREN	Energy-integrated Asia-Pacific	Contract / reservationVerify source	NVIDIA AI cloud capacity at renewable-powered campuses	Large customers procuring long-duration AI compute tied to owned power and data centers	Repurposes an energy-and-data-center operating base into vertically integrated AI cloud capacity.
Lambda California, US Private	Full-stack AI cloud North America	Self-serve + salesVerify source	NVIDIA GPU instances and reserved superclusters	AI research teams moving from single instances to dedicated clusters	A long-running developer brand spanning workstations, on-demand cloud, and large private AI infrastructure.
Nebius Amsterdam, Netherlands Public · NASDAQ: NBIS	Full-stack AI cloud Europe	Self-serve + salesVerify source	NVIDIA Hopper, Blackwell, and next-generation systems	European and US teams needing clusters, managed services, and regional control	Combines owned AI infrastructure with a developer cloud, managed Kubernetes, Slurm, storage, and inference.
Nscale London, UK Private	Energy-integrated Europe	Contract / reservationVerify source	Dense NVIDIA clusters across European and US campuses	Sovereign, enterprise, and hyperscale deployments requiring dedicated capacity	Builds across the stack from energy and data centers to bare metal, Kubernetes, Slurm, and inference.
Runpod New Jersey, US Private	Developer GPU cloud North America	Self-serveVerify source	Broad NVIDIA fleet from workstation GPUs to Blackwell	Fast experiments, containerized GPU instances, serverless inference, and elastic clusters	Low-friction global self-service with per-second billing and a path from one pod to reserved clusters.
Scaleway Paris, France Subsidiary	Sovereign / regional Europe	Self-serve + salesVerify source	NVIDIA GPU instances and European AI supercomputers	European teams prioritizing EU regions, data control, and a wider cloud platform	A European cloud with transparent self-service GPUs plus larger managed AI infrastructure.
TensorWave Nevada, US Private	Full-stack AI cloud North America	Contract / reservationVerify source	AMD Instinct GPU clusters and ROCm	Memory-intensive training and inference teams willing to use the AMD software stack	The clearest independent neocloud built around AMD rather than an NVIDIA-first fleet.
Together AI California, US Private	Model & inference cloud North America	Self-serve + salesVerify source	On-demand and reserved NVIDIA GPU clusters	Open-model inference, post-training, fine-tuning, and clusters under one platform	Couples raw GPU capacity with an optimized model-serving and training software stack.
Voltage Park Washington, DC, US Private	Developer GPU cloud North America	Self-serve + salesVerify source	Owned NVIDIA H100 clusters	Bare-metal H100 access from small experiments through multi-thousand-GPU clusters	Emphasizes owned hardware, simple cluster access, and predictable pricing without egress charges.
Vultr Florida, US Private	Full-stack AI cloud North America	Self-serve + salesVerify source	NVIDIA and AMD cloud GPUs	Enterprise AI that needs broad geographic reach and conventional cloud services	Adds AI accelerators to a mature independent cloud footprint rather than starting with a single GPU campus.
Yotta Shakti Cloud Mumbai, India Private	Sovereign / regional India	Self-serve + salesVerify source	Large NVIDIA H100, L40S, and expanding Blackwell fleet	Indian enterprises, government programs, researchers, and regulated workloads	A sovereign full-stack AI cloud with Indian data residency, GPU infrastructure, serverless, and model services.
YTL AI Cloud Kuala Lumpur, Malaysia Subsidiary	Sovereign / regional Asia-Pacific	Contract / reservationVerify source	NVIDIA accelerated systems in Malaysia	Southeast Asian sovereign AI and data-residency-sensitive enterprise workloads	Pairs Malaysian data-center and power assets with an NVIDIA-aligned national AI platform.

TL;DR

• A neocloud is a GPU-first cloud designed primarily for AI training, inference, and high-performance computing—not a general-purpose hyperscaler with GPUs added to a much larger catalog.
• The market now spans at least five distinct models: full-stack AI clouds, developer GPU clouds, model and inference platforms, sovereign regional clouds, and energy-integrated operators.
• The cheapest advertised GPU-hour is rarely the cheapest completed workload. Networking, storage, availability, cluster efficiency, engineering time, and contract risk matter just as much.
• This tracker compares 20 providers across four regions. It intentionally avoids a live price ranking because inventory and enterprise quotes change too quickly.

The first neoclouds won customers by making scarce GPUs available faster than AWS, Azure, or Google Cloud. That definition is already too narrow. The leading providers now build liquid-cooled data centers, secure power, finance accelerator fleets, operate high-performance networks, and add managed Kubernetes, Slurm, storage, inference, and model tooling on top.

This guide is a buyer's map, not a stock ranking or an exhaustive directory. It asks a practical question: which kind of AI cloud is structurally suited to your workload and procurement model?

What Counts as a Neocloud?

We include a provider when most of these signals are present:

1. GPU-first product design — accelerated compute is central to the business, not a small add-on.
2. AI-specific infrastructure — clusters use high-bandwidth networking, fast storage, optimized images, schedulers, or model-serving software.
3. External customer access — organizations can buy instances, clusters, inference, or contracted capacity.
4. A distinct operating model — the provider owns or orchestrates capacity rather than acting only as a comparison website or broker.
5. A credible scale path — newer providers show a route from a few GPUs to dedicated clusters or regional AI factories.

The boundary is intentionally porous. Together AI and GMI Cloud expose more of the model layer than a conventional infrastructure provider. IREN, Firmus, and Nscale integrate power and data centers more deeply. Scaleway, Yotta, E2E Networks, and YTL compete partly on sovereignty and geography. Those differences are exactly why one ranked list is misleading.

The Five Neocloud Archetypes

Shortlist by Use Case

The right shortlist begins with the workload, not the logo.

This table is a starting point. A provider that is excellent for an eight-GPU fine-tuning job may be wrong for a 2,000-GPU training run, even when both advertise the same accelerator.

How to Compare Neoclouds Without Being Fooled by GPU Price

The useful unit is not dollars per GPU-hour. It is cost per successful workload:

Effective workload cost = compute + storage + network + idle time + retries + platform labor + contract commitments

A lower hourly price can lose if jobs wait for capacity, nodes fail frequently, distributed training scales poorly, checkpoints move slowly, or engineers spend days rebuilding the environment.

Ask every shortlisted provider for evidence in seven areas:

1. Availability: Is the advertised GPU live in the required region, or merely orderable for a future date?
2. Cluster topology: Which network fabric connects nodes, and what bandwidth is non-blocking in the configuration you will receive?
3. Storage path: How quickly can workers load datasets and write checkpoints under concurrent demand?
4. Operational controls: Are Kubernetes, Slurm, IAM, private networking, audit logs, and observability production-ready?
5. Failure semantics: Who replaces failed nodes, how quickly, and what happens to billing while a cluster is degraded?
6. Portability: Can you bring standard containers, schedulers, weights, and checkpoints without proprietary conversion?
7. Commercial exposure: What is prepaid, cancellable, reserved, usage-based, or dependent on a construction milestone?

Seven Trends Reshaping the Neocloud Market

1. Power has become as strategic as the accelerator

The limiting factor is increasingly energized land, grid interconnection, cooling, and delivery time. That is why Nscale markets an integrated path from energy to cloud, while Crusoe, IREN, and Firmus tie their AI offerings to energy and data-center development.

2. The category is splitting between self-service and infrastructure finance

Runpod can put a developer on one GPU in seconds. A frontier lab may sign a multi-year agreement for an entire campus. Both are called GPU cloud, but they have different sales cycles, capital structures, reliability models, and customer risks.

3. Raw GPU rental is becoming a commodity layer

Providers are moving upward into managed clusters, inference engines, post-training, model endpoints, agent runtimes, and vertical workflows. Together AI and GMI Cloud make this most explicit. Nebius, CoreWeave, Lambda, Nscale, and others are adding enough services that “specialized hyperscaler” is becoming a more accurate description.

4. Sovereign AI is creating durable regional providers

Data residency, language ecosystems, public procurement, and national industrial policy create demand that a remote US region cannot always satisfy. Yotta and E2E Networks serve India; YTL is building in Malaysia; Scaleway emphasizes European infrastructure. Locality is a product feature, not merely a latency number.

5. NVIDIA remains the default, but AMD has a credible wedge

NVIDIA's software ecosystem and integrated rack systems still define most of the market. TensorWave is the clearest test of whether an AMD-first neocloud can win memory-intensive training and inference through different economics and an improving ROCm stack.

6. Announced capacity and delivered capacity are different assets

Press releases often combine operating GPUs, contracted power, future campuses, and long-term expansion options. Buyers should label each milestone: powered land, construction started, equipment ordered, cluster commissioned, or generally available. Do not add them together.

7. Counterparty risk now belongs in cloud architecture

Large neocloud contracts may depend on GPU-backed debt, one or two anchor customers, equipment delivery, and future power. A second provider, portable checkpoints, tested restore procedures, and an exit clause are technical safeguards as much as procurement terms.

A Practical Procurement Test

Before a serious pilot, give each provider the same reproducible workload. Measure:

• time from account approval to working capacity;
• effective tokens, samples, or training steps per dollar;
• multi-node scaling efficiency;
• checkpoint read and write time;
• node failure and replacement behavior;
• time required to reproduce the environment;
• support response during an actual incident;
• total cost after storage, network, public IPs, and idle resources.

Run the test in the region and cluster shape you intend to buy. A benchmark on one isolated GPU says little about a distributed production system.

Frequently Asked Questions

Is CoreWeave a neocloud?

Yes. CoreWeave is the category's largest pure-play example: a GPU-first cloud built for training and inference rather than a broad catalog of general enterprise IT services.

Are AWS, Azure, Google Cloud, and Oracle neoclouds?

No. They sell major AI infrastructure products, but they are hyperscalers with much broader businesses. This page focuses on specialized providers where accelerated AI infrastructure is the organizing principle.

Is a GPU marketplace a neocloud?

Not automatically. A marketplace can be useful, but aggregating third-party hosts creates different guarantees for hardware consistency, security, networking, and support. This tracker favors providers that operate or directly orchestrate the customer experience.

Which neocloud is cheapest?

There is no durable universal winner. On-demand prices, spot capacity, reservations, region, networking, storage, and sales quotes change independently. Compare a representative workload and its full bill rather than one advertised accelerator price.

Should production inference use a GPU cloud or a model API?

Use a model API when speed of integration matters most and its model choices are acceptable. Use dedicated or serverless GPU infrastructure when you need custom weights, deeper performance control, data isolation, predictable high utilization, or provider portability.

How often will this list be updated?

We review it when providers launch a material region or accelerator generation, change access models, complete major infrastructure, merge, shut down, or materially change their product focus.

Methodology and Caveats

This is an editorial snapshot, not a live inventory feed or investment recommendation. “Available” can mean self-service capacity, sales-assisted reservation, or a future contract depending on the provider. Hardware descriptions summarize public product materials and should be reconfirmed in the console or contract. Announced campuses are not treated as delivered capacity.

For the research organizations buying this infrastructure, see AI Neolabs 2026. For model-layer costs after deployment, compare the latest LLM API pricing and use the LLM cost calculator.