The Tool Desk
Outbyte PC Repair FREEClear out junk files and repair common Windows errorsFree Scan →Outbyte Driver Updater FREEScan for outdated or missing drivers - takes under a minuteDriver Scan →A neocloud is a cloud provider that specializes in GPU compute and AI workloads, particularly model training and inference. AWS, Microsoft Azure, and Google Cloud also offer GPUs; the key difference is that they combine them with much broader cloud platforms. Which option fits depends on the workload, available hardware and region, total costs, operational needs, and the services your team requires.
What is a neocloud?
“Neocloud” is shorthand for a cloud provider built around accelerated computing and AI rather than general-purpose enterprise applications. Microsoft for Startups describes the category as recent and names CoreWeave, Crusoe, Lambda, and Nebius as providers associated with it. The term describes a specialization; the available evidence does not establish an official industry-wide certification or a fixed list of qualifying providers.
Neoclouds may provide GPU clusters as bare metal, with fast links among GPUs in a server and networking between servers for distributed workloads. This direct access can suit large training jobs, but it can also leave customers responsible for more of the infrastructure work.
How does a neocloud differ from a hyperscaler?
The distinction is depth versus breadth—not GPUs versus no GPUs. Neoclouds focus on GPU infrastructure and AI workloads. Hyperscalers offer GPU capacity alongside a wide range of services, such as storage, managed databases, identity, security tools, compliance offerings, and global regions.
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| Consideration | Neocloud | Hyperscaler |
|---|---|---|
| Provider focus | GPU compute and AI workloads | GPU compute within a broad cloud platform |
| Platform services | Scope varies by provider; NVIDIA describes Nebius as offering compute, storage, and managed services | Broader platform that can include storage, managed databases, identity, security, and compliance services |
| Infrastructure operations | Bare-metal access may provide direct hardware access while leaving more day-to-day operating tasks to the customer | GPU offerings sit within each provider’s wider cloud environment; specific management responsibilities depend on the service |
| GPU availability | Depends on provider, accelerator, location, and current capacity | Also depends on accelerator, region, SKU, and current capacity |
Microsoft’s guide says neoclouds can offer faster access and lower raw GPU-hour prices for AI-specific work. Those are general comparative claims, not a current price survey or a guarantee. A fair cost comparison must use the same date and account for the specific accelerator, region, contract term, networking, storage, and service level.
What GPU options do AWS, Azure, and Google Cloud offer?
All three hyperscalers document GPU compute products. Their product families and regional availability change, so check the current SKU and capacity in the region you need before committing.
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- AWS: Its accelerated-computing page lists EC2 P5 instances with NVIDIA H100 or H200 GPU configurations and P6 instances using Blackwell-family GPUs.
- Google Cloud: Its Compute Engine GPU documentation lists multiple GPU types and describes flexible machine configurations.
- Microsoft Azure: Its GPU-optimized VM documentation lists families including ND H100 and H200 series.
Which providers are associated with the neocloud category?
Microsoft for Startups names CoreWeave, Crusoe, Lambda, and Nebius as examples. NVIDIA’s partner directory describes Crusoe Cloud as an AI cloud, Lambda as an AI developer cloud with hosted GPUs and managed inference options, and Nebius as a full-stack AI cloud with compute, storage, and managed services. These are descriptions of provider positioning, not independent performance tests.
In a May 18, 2025 announcement about DGX Cloud Lepton, NVIDIA named CoreWeave, Crusoe, Lambda, and Nebius among the GPU cloud providers participating in its announced ecosystem. NVIDIA CEO Jensen Huang said, “NVIDIA DGX Cloud Lepton connects our network of global GPU cloud providers with AI developers.” This is NVIDIA’s description of its ecosystem, not an independent assessment of provider performance.
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How should you compare GPU cloud providers?
Start with the workload and then compare the complete service you would actually use. A low GPU-hour rate may not mean a lower overall cost if you need more networking, storage, data transfer, engineering time, or operational support.
- Workload: Is the service for model training, inference, or hosting an AI application? Training may require large, tightly connected clusters; inference needs depend on the model, traffic pattern, and latency requirements.
- Hardware and scale: Compare the GPU model, memory, number of GPUs, and the ability to obtain the cluster size you need.
- Networking: For distributed training, examine connections within servers and between nodes, not just the accelerator specification.
- Region and capacity: Check where the required hardware is available and how soon the provider can supply it.
- Total cost: Compare hourly and committed rates along with storage, networking, data transfer, and any applicable service-level costs. Use matching regions, terms, and configurations.
- Managed services and operations: Identify who handles scheduling, failed nodes, drivers and CUDA version alignment, security patches, and keeping hardware use efficient. Include the cost of your team’s time.
- Security and enterprise requirements: Verify that the provider and service meet your compliance, identity, security, support, and reliability needs.
- Portability and multi-cloud overhead: Account for data movement and egress charges, separate identity and security setups, and added operational complexity if workloads span providers.
When does each option make sense?
A neocloud may fit when GPU specialization is the priority
Consider one when you need AI-oriented GPU infrastructure, suitable cluster networking, and access to a specific accelerator or capacity. Confirm that the provider’s regions, service scope, support, and operating model match what your team can manage.
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A hyperscaler may fit when the wider platform matters
A hyperscaler can be a strong fit when your GPU workloads need to integrate closely with services you already use, such as managed databases, storage, identity, security, or compliance offerings. Its GPU capacity still needs to be checked for the exact region and configuration.
There is no universal cheapest or best provider
Provider fit depends on the workload, region, capacity, full cost, and operating requirements. Compare like-for-like configurations and include the engineering effort and platform services involved; the available evidence does not support a universal provider ranking.
Quick Recap
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Product prices and availability are accurate as of the date/time indicated and are subject to change. Any price and availability information displayed on Amazon at the time of purchase will apply.
