AWS and Google Cloud are hyperscale public-cloud providers. GPU Cloud is a delivery model for accessing GPU capacity. NeoCloud describes a provider category focused on GPU, AI, and high-performance computing. These terms therefore represent different layers of the buying decision.
Start with the cloud where the company's data and applications already live. Evaluate a dedicated GPU platform or hybrid architecture when multi-node training, fixed capacity, or access to new GPU generations becomes more important than keeping every workload in one cloud.
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If data, IAM, and applications already run on AWS, establish the AWS baseline first.
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If analytics, Vertex AI, GKE, or TPU workloads are already on GCP, start with GCP.
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For short tests or self-service single-GPU access, compare actual GPU availability, startup time, persistence, and the complete bill.
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For multi-node training or fixed next-generation capacity, evaluate NeoCloud and enterprise dedicated-GPU providers, including fabric, storage, SLA, security, and exit terms.
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A hybrid architecture often fits when data services remain in a public cloud but training requires a large, steady GPU fleet.
What are AWS, GCP, GPU Cloud, and NeoCloud?
AWS and GCP provide general-purpose cloud platforms in which GPU instances sit alongside identity, networking, object storage, databases, Kubernetes, monitoring, and managed AI services. GPU Cloud describes delivery through virtual machines, containers, bare metal, or managed clusters, billed hourly, monthly, or as reserved capacity. A NeoCloud puts high-density GPU infrastructure, AI software, and capacity supply at the center of its product.
AWS, GCP, GPU Cloud, and NeoCloud comparison
| Dimension | AWS | GCP | General GPU Cloud | NeoCloud / dedicated GPU provider |
|---|---|---|---|---|
| Category | Hyperscale public cloud | Hyperscale public cloud | GPU delivery model | AI- and GPU-first provider category |
| Primary value | Broad services and AWS integration | Data and AI tooling, GKE, Vertex AI, TPU and GPU | Rapid access to a specific GPU | High-density GPUs and dedicated clusters |
| Common delivery | EC2 GPU instances, Capacity Blocks, EKS, managed AI | Compute Engine GPU VMs, reservations, Spot or Flex-start, GKE, Vertex AI | VM, container, bare metal, serverless GPU | Bare metal, dedicated nodes, Kubernetes, Slurm, reserved clusters |
| Best fit | AWS-centered enterprise workloads | GCP data and AI toolchains | PoC, short tests, flexible use | Multi-node training, fixed inference capacity, new GPU generations |
| Often missed | Region, bundled instance resources, storage, data transfer | Quota, reservations, region, machine type and maintenance behavior | Dedicated vs shared resources, persistence, support scope | Compliance evidence, SLA detail, capacity commitments, exit cost |
Strengths, limitations, and appropriate use cases
AWS is a natural fit when data, IAM, VPC, EKS, and application services already run on AWS. Capacity Blocks can reserve accelerator capacity for a defined period, but buyers must still validate region, instance configuration, storage, and data-transfer cost. See AWS Capacity Blocks for ML.
GCP fits teams that use BigQuery, Cloud Storage, Vertex AI, GKE, or TPUs. GPU access is affected by quota, reservations, region, and machine-type constraints. See Google Cloud GPU machine types.
General GPU Cloud is useful for proofs of concept, validation, fine-tuning, and flexible jobs. Confirm whether the resource is shared or dedicated, the minimum rental unit, storage persistence, networking, and operational responsibility.
NeoCloud or a dedicated GPU provider can fit multi-node training, steady capacity, and new GPU generations. Lower advertised GPU pricing must be weighed against data movement, storage, support, compliance, long-term commitments, and exit cost.
Eight dimensions for enterprise AI compute procurement
| Dimension | What to evaluate | How to validate |
|---|---|---|
| Workload | Training, fine-tuning, batch inference, online inference; model and precision | Benchmark the same model, framework, and data |
| GPU and unit | Model, VRAM, single GPU, server, or cluster; shared or dedicated | Verify the instance, server, or cluster configuration |
| Capacity | Region, start date, GPU count, and guarantee | Put delivery date and remedies in the contract |
| Network | Intra-GPU, inter-node, and external topology | Measure collective communication and data transfer |
| Storage | Local, shared, and object capacity, throughput, persistence | Measure data loading and checkpoint writes |
| Software and operations | Drivers, images, Kubernetes or Slurm, monitoring, incidents | Use a responsibility matrix |
| Security and compliance | Residency, encryption, access, logs, vulnerability and incident handling | Review evidence, contract, and audit documents |
| Cost and exit | CPU, RAM, storage, traffic, support, and data export | Calculate full OPEX, effective GPU/hour, and exit cost |
A three-step selection process
Step 1: Segment workloads
Separate proof-of-concept, periodic training, continuous inference, and burst capacity. Different workloads may use different platforms; a company does not need one compute source for every use case.
Step 2: Compare the same benchmark and full cost
Hold the model version, precision, batch size, context length, dataset, framework, and software versions constant. Measure completion time, failures and reruns, throughput, and peak memory. Include compute, CPU and RAM, storage, traffic, software, deployment, and operations labor.
Step 3: Run an exit-ready trial before a capacity contract
Test data movement, environment recreation, checkpoint recovery, monitoring, hardware replacement, and support response. Long-term contracts should define GPU model or equivalent, start date, SLA, data export, hardware-generation changes, and early termination.
When does a hybrid architecture make sense?
A hybrid design can keep governance, application services, and sensitive master data in AWS or GCP while moving portable training data, checkpoints, or selected inference workloads to a dedicated GPU platform. It diversifies capacity risk but increases identity, network, security, synchronization, and observability complexity. It only works when data boundaries, transfer frequency, and responsibilities are explicit.
How KONST evaluates enterprise AI compute
KONST Group provides bare-metal rental and dedicated GPU clusters for customized environments and multi-node computing. Teams that need a self-service test environment can access the Glows.ai GPU Cloud platform through KONST's partner relationship. Glows.ai is a partner, not a KONST subsidiary.
Glows.ai offers on-demand cloud, virtualized on-demand clusters, cloud and dedicated inference, Datadrive, storage, and team controls. For dedicated servers or cross-region deployments, KONST provides H100, H200, B200, and B300 bare-metal configurations and validates location, capacity, networking, and delivery conditions.
Contact KONST. The KONST team will respond within three business days.
Related services: Glows.ai GPU Cloud and GPU bare-metal rental.
FAQ
How is GPU Cloud different from NeoCloud?
GPU Cloud is a way to deliver GPU capacity through cloud interfaces. NeoCloud is a provider category centered on AI and GPU infrastructure. AWS, GCP, and NeoCloud providers may all offer GPU Cloud.
Which is better for AI, AWS or GCP?
Neither is universally better. AWS often has lower integration cost when data, identity, and applications already run there. GCP is often more natural for teams using BigQuery, Vertex AI, GKE, or TPU. Compare capacity, completion time, and full cost for the same workload.
Is a NeoCloud always cheaper than AWS or GCP?
No. Advertised GPU rates do not include every effect of transfer, storage, support, operating labor, minimum commitment, contract length, and exit cost.
Should an enterprise use one cloud or multiple clouds?
A single cloud is easier when AI workloads depend heavily on existing data and managed services. Hybrid or multi-cloud can improve capacity options when GPU work is portable, but it also adds identity, network, security, and monitoring complexity.
What is the most important metric when comparing AI compute quotes?
Use the full cost per completed unit of work, such as one training run, one million successful tokens, or a defined throughput target. GPU/hour is an input, not the final measure of economics.
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KONST Editorial Team
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