GPU Cloud Comparison: 17 Neoclouds for AI in 2025

If you’re running AI training workloads and hitting GPU availability limits or cost walls on AWS, GCP, or Azure, a wave of specialized GPU cloud providers (often called “neoclouds”) offer an alternative. These providers focus exclusively on GPU infrastructure, often with simpler pricing, immediate availability, and hardware optimized for AI workloads.

This guide compares 17 GPU cloud providers across the dimensions that matter for production AI training: GPU pricing, InfiniBand networking, storage options, and platform capabilities. We focus on what’s publicly documented, noting where information requires sales contact.

The Neocloud Landscape

The term “neocloud” refers to cloud providers primarily offering GPU-as-a-Service (GPUaaS). Unlike hyperscalers with broad service portfolios, neoclouds focus on delivering GPU compute with high-speed interconnects for AI and HPC workloads.

Between 10-15 neoclouds currently operate at meaningful scale in the US, with footprints growing across Europe, the Middle East, and Asia.

Why consider neoclouds over AWS, GCP, or Azure?

The hyperscaler GPU experience involves quota requests, waitlists, and premium pricing:

Even AWS, after cutting prices 44% in June 2025, is still 4.7x more expensive than SF Compute. GCP and Azure are 7-8x more expensive.

SF Compute’s marketplace model offers a unique advantage: flexible time-based reservations. You can book GPU clusters for arbitrary windows (7 days starting next Tuesday, 2 weeks in March) at guaranteed pricing, with infrastructure provisioned from vetted partners to your specifications. This eliminates the need for long-term contracts while avoiding spot instance interruptions.

Beyond cost, neoclouds eliminate the friction of quota approvals. On AWS, requesting H100 quota often requires a support ticket explaining your use case, with approval taking days to weeks. GCP and Azure have similar processes. Neoclouds typically offer self-service access: sign up, add payment, deploy GPUs in minutes.

Infrastructure is also optimized differently. Neoclouds treat InfiniBand as standard—400Gb/s per GPU for multi-node training. Hyperscalers charge premium tiers for similar networking (AWS EFA, GCP GPUDirect) and availability varies by region.

Market Segmentation

The GPU cloud market has fragmented into distinct tiers:

Many providers span tiers. FluidStack operates both wholesale (62% of revenue) and marketplace (38%). RunPod offers Secure Cloud (neocloud) and Community Cloud (marketplace). The bespoke tier is likely larger than publicly known, as these deals are rarely disclosed. This guide focuses on publicly available offerings (Tiers 2-4).

GPU Hardware & Pricing

Start here: price is the most visible differentiator and determines your baseline cost. H100 pricing varies 4x across providers ($1.45-6.15/hr), so this table alone can eliminate half your options. Budget-conscious teams should look at SF Compute ($1.45/hr), Vast.ai ($1.74/hr), and Hyperstack ($1.90/hr). Enterprise buyers typically choose from Nebius, CoreWeave, Crusoe, or Lambda.

On-Demand GPU Pricing

*Vultr’s B200 is available via 36-month reserved commitment at $2.89/hr ($23.12/hr for 8x B200 HGX system); on-demand pricing not published.

AMD GPU Availability

AMD GPUs are typically 20-50% less than NVIDIA equivalents and are less supply-constrained. If your workload runs on ROCm (PyTorch and JAX have good support), AMD is worth considering. Vultr offers one of the cheapest MI300X options at $1.85/hr with managed Kubernetes. Hot Aisle and TensorWave are AMD-only specialists.

Training Infrastructure

If you’re training models across 16+ GPUs (distributed training), this section determines whether you’ll actually get good performance. Single-GPU workloads or inference can skip ahead—standard networking and local NVMe storage are fine. For multi-node training, network bandwidth between GPUs and shared filesystem throughput often bottleneck more than GPU speed.

Infrastructure Ownership Models

Ownership model predicts pricing stability and support quality. Owners (Crusoe, CoreWeave, Nebius) control their hardware and can offer consistent SLAs. Aggregators (Vast.ai, SF Compute) provide cheaper pricing but variable quality. Owner (colo) is middle ground—they own GPUs but rent data center space. See Market Segmentation for how this maps to business model tiers.

InfiniBand and High-Speed Networking

InfiniBand only matters for multi-node distributed training (16+ GPUs). If you’re training on 1-8 GPUs or running inference, skip this table. Providers using RoCE (TensorWave, Hot Aisle, Nscale) offer RDMA over standard Ethernet with lower cost but potentially higher tail latency under network congestion.

Note: This table describes publicly available cloud offerings. Bespoke wholesale buildouts can support arbitrary configurations. “Not documented” indicates information not publicly available.

Storage Options

Storage matters for two scenarios: (1) multi-node training requiring shared filesystems so all GPUs access the same data, and (2) checkpointing large models to object storage. For single-node work, local NVMe is usually sufficient. This table describes publicly available offerings; bespoke buildouts integrate customer-specified storage.

Key differentiator: VAST Data integration (Lambda, Voltage Park, CoreWeave, GMI Cloud) provides high-performance shared storage with S3 compatibility. Several providers (Crusoe, FluidStack, TensorWave) lack native object storage—you’ll need to run MinIO or similar yourself.

Storage Performance

If you’re loading large datasets (100GB+) to GPUs for training, shared filesystem throughput determines how long you wait. Nebius and Lambda both achieve 11-12 GB/s per 8-GPU node with VAST Data, significantly faster than hyperscaler equivalents (AWS EFS maxes at 1.5 GB/s). Most providers don’t publish these specs.

Orchestration & Platform

How you’ll actually run workloads matters as much as the hardware. But there’s an important distinction: infrastructure orchestration (Kubernetes, Slurm) vs. the platform layer.

Neoclouds provide Kubernetes or Slurm to schedule containers or jobs on GPU nodes. That’s infrastructure orchestration—it gets your code running on hardware. But production AI teams need more: hosted dev environments where data scientists can iterate, distributed training orchestration that handles multi-node configurations, parallel job scheduling with automatic retries, and cost allocation by user and project.

Most neoclouds stop at infrastructure. The platform layer—the operational tooling that makes GPU infrastructure actually usable for teams—is what you build on top, or what Saturn Cloud provides out of the box.

Kubernetes and Orchestration

Most production AI workloads run on Kubernetes. Support varies from fully managed Kubernetes to bring-your-own orchestration.

Nebius’s Soperator is the first open-source Kubernetes operator for running Slurm clusters. CoreWeave’s SUNK supports 32,000+ GPU jobs.

The Platform Layer: Saturn Cloud

Beyond GPU interconnect, production AI workloads often need load balancers for inference endpoints, VPCs for network isolation, and VPN/peering for hybrid cloud connectivity.

Network Services Comparison

Operational Considerations

These tables cover costs and capabilities that aren’t obvious until you deploy to production. Egress fees can add 20-40% to bills at hyperscalers, but most neoclouds offer free egress. For inference deployments, you need load balancers and VPCs—providers without these force you to run HAProxy/nginx on VMs and manually configure networking.

Egress Pricing

Good news: free egress is now standard among neoclouds, unlike hyperscalers where moving data out costs $0.08-0.12/GB. If you’re serving inference traffic or moving large datasets between clouds, this matters. Exceptions: Vultr ($0.01/GB after 2TB free) and OVHcloud ($0.011/GB). Vast.ai varies by host and can be expensive.

Network Services

Production inference deployments need load balancers (to distribute traffic across GPU instances) and VPCs (for network isolation). Only Nebius, CoreWeave, Crusoe, Vultr, and OVHcloud provide both. Marketplace providers (Vast.ai, RunPod) use port mapping instead of dedicated IPs, which complicates production deployments. If you’re only doing training, you can skip this table.

Developer Experience & Enterprise Readiness

These tables determine how quickly you can get started and whether the provider meets enterprise requirements. If you’re managing infrastructure-as-code, official Terraform providers (Nebius, CoreWeave, Crusoe, Vultr, OVHcloud) save significant automation work. Self-service access means you can provision GPUs in minutes vs days of sales conversations. Compliance certifications (SOC 2, ISO 27001, HIPAA) gate enterprise adoption—if you need them, filter to providers with documented certs.

Terraform and API Support

Terraform providers and APIs enable infrastructure-as-code. Official Terraform providers (Nebius, CoreWeave, Crusoe, Vultr, OVHcloud) are actively maintained with full resource coverage. Providers with only REST APIs require custom Terraform provider development or manual scripting.

Self-Service Access

Self-service means you can sign up and provision GPUs immediately. Sales-gated (CoreWeave, TensorWave) means you submit a request and wait for organizational approval, which can take days.

Compliance and Enterprise Features

Enterprise buyers often require SOC 2 Type II (security controls audit) and ISO 27001 (information security management). HIPAA is required for healthcare data. If your procurement process gates on specific certifications, this table eliminates providers quickly. Also check regions—European teams may need EU data centers for data residency requirements.

Conclusion

The GPU neocloud market has matured significantly. Free egress is now standard, 400Gb/s InfiniBand is table stakes for serious providers, and pricing has compressed to $2-4/hr for H100s (vs $6-12/hr on hyperscalers).

For production AI training, Nebius, CoreWeave, and Crusoe offer the most complete platforms. For cost-sensitive experimentation, Vast.ai and SF Compute provide the lowest prices. For European data sovereignty, Nebius and DataCrunch/Verda combine EU data centers with enterprise compliance and 100% renewable energy.

The main remaining gap is documentation transparency. Most providers require sales conversations for pricing on reserved capacity, large clusters, and enterprise features. As the market matures, expect more self-service options and published pricing for these categories.

Last updated: December 2025. Pricing and features change frequently. Verify current offerings on provider websites before making decisions.