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Short answer: CoreWeave has the lowest listed price in the September 2026 eight-H100 comparison, while AWS, Azure, and Google Cloud justify their higher or variable rates with broader enterprise platforms, specialized networking, and different capacity options. The cheapest hourly quote is not automatically the cheapest completed training run: quota, usable capacity, storage, data transfer, interruptions, support, and commitment terms can change the total.
Eight-H100 price snapshot
The following figures are a September 2026 comparison of an eight-GPU H100 node. They are snapshots, not universal rates. Region, taxes, host resources, storage, egress, billing term, and whether capacity is on-demand, reserved, a capacity block, or spot can materially change the bill.
| Provider and machine | Eight-GPU node price | Approximate GPU-hour | What the figure represents |
|---|---|---|---|
| CoreWeave HGX H100 | $49.24/hour on demand; $19.71/hour spot | $6.16 on demand; $2.46 spot | US table price; eight H100 GPUs |
| AWS p5.48xlarge | $55.04/hour | $6.88 | Eight-H100 instance in the cited September 2026 snapshot |
| Google Cloud a3-highgpu-8g | $88.49/hour | $11.06 | Eight-H100-80GB machine in the cited snapshot |
| Azure ND96isr H100 v5 | $98.32/hour | $12.29 | Eight-H100 VM in the cited snapshot |
Dividing the node price by eight is useful for a first comparison, but it does not normalize CPU, RAM, local storage, network charges, or software services. A node that finishes a job sooner can have a lower total cost even when its hourly rate is higher.
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What each eight-GPU option includes
| Provider | GPU configuration | Interconnect and topology | Host resources and local storage | Availability or scaling note |
|---|---|---|---|---|
| CoreWeave HGX H100 | Eight H100 GPUs, 80 GB VRAM per GPU | HGX platform; exact fabric details depend on the deployed configuration | 128 vCPUs and 2,048 GB system RAM | US list price is region-specific; capacity and quota must be confirmed |
| AWS P5 | p5.4xlarge has one H100; p5.48xlarge has eight H100s and 640 GB total HBM3 | 3,200 Gbps Elastic Fabric Adapter (EFA) and 900 GB/s NVSwitch on the eight-GPU instance | Local NVMe is included on P5; exact CPU and storage allocations vary by instance shape | P5, P5e, and P5en UltraClusters can scale to 20,000 H100 or H200 GPUs |
| Azure ND H100 v5 | Eight H100 GPUs, 80 GB per GPU | 3.2 Tbps interconnect per VM, 400 Gbps Quantum-2 InfiniBand per GPU, NVLINK 4.0 | 96 physical fourth-generation Intel Xeon cores, 1,900 GiB system memory, 1,024 GiB local storage | Microsoft documents scaling to thousands of GPUs; retail price depends on region and term |
| Google Cloud A3 High (a3-highgpu-8g) | Eight H100 80GB GPUs | Google publishes the machine type and zone-specific networking details for the selected A3 deployment | Host CPU, memory, and attached storage depend on the A3 machine specification and selected disks | GPU devices are limited to specified zones; spot pricing is dynamic |
CoreWeave: lowest listed H100 price when capacity fits
CoreWeave is purpose-built around accelerated computing rather than offering GPUs as one category inside a broad general-purpose cloud. Its US pricing table lists the HGX H100 as an eight-GPU system with 80 GB of VRAM per GPU, 128 vCPUs, and 2,048 GB of system RAM. The listed rate is $49.24 per hour on demand or $19.71 per hour on spot capacity.
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The same catalog includes newer and alternative accelerators such as H200, B200, B300, A100, L40S, L40, GH200, and RTX PRO 6000. That breadth can make it easier to move a workload between generations when an H100 queue is constrained or a smaller inference GPU is more economical.
Where CoreWeave is compelling
- Specialist GPU capacity and a simple eight-GPU node price are the primary buying criteria.
- Your required region has confirmed capacity and acceptable quota lead time.
- Your training stack can use the offered network, storage, Kubernetes, and bare-metal or virtualized operating model without depending on a particular hyperscaler service.
Questions to settle before ordering
- Is the quoted rate for the exact US region you need, and is that region accepting new quota?
- What storage service will hold datasets and checkpoints, and what are its read, write, and egress charges?
- What support response and replacement process apply if a GPU or host fails?
- Can your scheduler restart work if spot capacity is reclaimed?
AWS P5: strongest fit for EFA, UltraClusters, and an existing AWS estate
AWS P5 offers both a one-H100 shape and an eight-H100 shape. The eight-GPU p5.48xlarge combines 640 GB of HBM3 with 3,200 Gbps EFA networking, 900 GB/s NVSwitch, and local NVMe. This topology is designed for distributed deep-learning and high-performance-computing jobs that communicate heavily between GPUs.
AWS says P5, P5e, and P5en UltraClusters can scale to as many as 20,000 H100 or H200 GPUs. That scale is relevant when a training run needs many nodes and the team already operates placement groups, EFA-aware software, and the surrounding AWS network and security controls.
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- Direct integration with S3 for datasets, FSx for high-throughput file systems, EKS for Kubernetes, and SageMaker for managed machine-learning workflows.
- Fine-grained IAM, private networking, logging, tagging, and consolidated billing for organizations already governed on AWS.
- One-GPU P5 instances for development or debugging before moving to an eight-GPU job.
Interpreting AWS performance claims
AWS states that P5 can accelerate time to solution by up to four times and reduce machine-learning training cost by up to 40% compared with previous-generation GPU instances. Those are AWS claims, not an independent benchmark; your model, batch size, framework, checkpoint frequency, and scaling efficiency determine whether they apply.
Azure ND H100 v5: InfiniBand and Microsoft enterprise integration
Microsoft documents ND H100 v5 as an eight-H100 VM with 80 GB per GPU. The VM provides 3.2 Tbps of interconnect bandwidth, 400 Gbps Quantum-2 InfiniBand per GPU, and NVLINK 4.0. The documented host allocation is 96 physical fourth-generation Intel Xeon cores, 1,900 GiB of system memory, and 1,024 GiB of local storage.
ND H100 v5 deployments can scale to thousands of GPUs. That makes the series a candidate for distributed training that relies on InfiniBand collectives, especially when the organization already uses Microsoft Entra ID, Azure networking, Azure Machine Learning, or an enterprise Azure agreement.
Pricing and procurement cautions
- Use the Azure calculator for the target geography, operating-system choice, storage, and billing term; the September comparison is not a global retail quote.
- Confirm that the required ND-series quota is approved in the region before scheduling a large run.
- Include managed disks, Blob Storage transactions, network egress, monitoring, and support in the estimate.
Google Cloud A3 High: zone-aware availability and flexible spot economics
Google Cloud identifies A3 High machine type a3-highgpu-8g as an eight-H100-80GB configuration. GPU devices are available only in specified zones, so checking the zone is part of capacity planning rather than an afterthought.
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Google describes spot pricing as dynamic and says it can change as often as once every 30 days. For most machine types and GPUs, Google reports discounts of 60% to 91% from corresponding on-demand prices, while A3 and local-SSD components generally receive smaller discounts. Treat those percentages as provider guidance, not a guaranteed H100 rate.
When A3 High makes sense
- The required zone has confirmed A3 capacity and is close to the data or users that matter.
- Your team already uses Google Kubernetes Engine, Vertex AI, Cloud Storage, or Google’s identity and networking model.
- You can adapt job placement and checkpointing to changing spot economics.
Interconnect and topology: why GPU-hour price is only half the story
Single-GPU experiments are often limited by model size, memory, or input pipelines. Multi-GPU training adds synchronization: every step may require all-reduce operations, activation exchange, or parameter sharding across GPUs and nodes. A cheaper node can lose its advantage if communication leaves the GPUs waiting.
- Within a server: NVLink and NVSwitch reduce the cost of GPU-to-GPU transfers compared with routing every exchange through host memory.
- Between servers: EFA or InfiniBand-class fabrics, placement controls, and an optimized collective library affect scaling efficiency.
- Across a cluster: the provider must offer enough adjacent capacity at the same time; a theoretical 1,000-GPU limit is not useful if only scattered hosts are available.
Benchmark the complete training step at the intended scale. Record tokens or samples per second, GPU utilization, communication wait time, checkpoint duration, and failures. Compare cost per completed epoch or per million tokens rather than multiplying the hourly list price by an assumed runtime.
Capacity, quota, and lead time
H100 inventory is constrained and location-specific. A provider can publish a price while having no immediately allocatable capacity in your zone or account. Quota approval can also take longer than a short experiment.
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- Select acceptable regions or zones: rank them by data location, legal requirements, latency, and expected capacity.
- Request quota before reserving time: include peak GPU count, node count, and whether you need on-demand, reserved, capacity-block, or spot allocation.
- Run a capacity probe: launch the intended instance shape, attach the real storage path, and execute a short distributed test.
- Obtain a written commercial quote: confirm rate validity, minimum term, cancellation rules, support level, taxes, and any negotiated discount.
Do not move a production dataset or announce a training date until the probe succeeds in the selected region and the quota is visible to the scheduler.
Storage, data movement, and hidden costs
The GPU meter is only one line item. A training run may read terabytes repeatedly, write frequent checkpoints, retain optimizer state, and export models to another region or provider.
| Cost area | Questions to answer | Why it changes the comparison |
|---|---|---|
| Dataset storage | Object or file storage? Required throughput? Number of reads? | Low-cost storage can bottleneck GPUs or add request and retrieval fees. |
| Local scratch | How much NVMe or local SSD is included, and is it ephemeral? | Rebuilding a cache after interruption can erase spot savings. |
| Checkpoint storage | How many checkpoints, for how long, and in which region? | Large optimizer states increase capacity and egress requirements. |
| Network ingress | Is data entering the GPU region from another cloud or site? | Ingress policy differs by provider and route. |
| Network egress | Will results, checkpoints, or logs leave the provider or region? | Egress can exceed the apparent GPU-price difference. |
| Control plane | Are Kubernetes, managed ML, monitoring, secrets, and registries billed separately? | Convenience services improve operations but add recurring charges. |
For a fair estimate, model the full lifecycle: data staging, warm-up, training, evaluation, checkpoint retention, artifact export, and teardown. Include idle time while a job waits for data or a replacement host.
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On-demand, reserved, capacity-block, and spot choices
On-demand
On-demand is the simplest baseline: no long commitment, predictable interruption behavior, and a rate you can use for short tests. It is often the right reference point even when you expect to negotiate a discount.
Commitments and reservations
Reserved terms or negotiated commitments can reduce the effective rate, but they exchange flexibility for utilization risk. Commit only the baseline GPU count you can keep busy. Keep burst capacity on a more flexible purchase model unless the provider guarantees the capacity you need.
Spot
Spot can be dramatically cheaper, but an interruption turns elapsed time into lost work. The correct metric is expected completed-work cost:
expected cost = hourly price × (runtime + restart overhead) ÷ probability of completing without a reclaim
This simplified expression is not a provider quote; it is a planning model. Measure interruption frequency in the exact region and instance family, checkpoint often enough to bound lost work, and test restoration before relying on spot for a deadline.
Checkpoint design for reclaimable GPUs
- Write checkpoints to durable storage rather than only to ephemeral local disks.
- Save model weights, optimizer state, scheduler state, random seeds, data-loader position, and software version.
- Use atomic writes or versioned directories so a reclaim during upload cannot corrupt the newest checkpoint.
- Automate detection of termination notices and resubmission to another approved zone or provider.
- Track effective tokens or samples completed per dollar, not just the spot discount.
Managed ML and orchestration fit
| Need | Most natural fit | Reason |
|---|---|---|
| Existing AWS governance and data estate | AWS P5 | EC2, EFA, S3, FSx, EKS, and SageMaker can use the same identity, networking, and billing controls. |
| Microsoft identity and enterprise agreement | Azure ND H100 v5 | Azure networking, Azure Machine Learning, and Entra-based access can reduce integration work. |
| Google Kubernetes or Vertex workflows | Google Cloud A3 High | A3 can be placed into Google’s zone and container ecosystem when capacity is available. |
| GPU-specialist procurement | CoreWeave | A focused accelerator catalog and explicit HGX pricing can simplify a GPU-first deployment. |
This is an integration decision, not a performance ranking. A managed service may shorten setup time while increasing platform charges or reducing portability.
How to choose by workload
Large distributed pretraining
Prioritize fabric bandwidth, placement guarantees, collective-communication performance, and the provider’s ability to deliver the full node count simultaneously. AWS UltraClusters and Azure’s InfiniBand-oriented ND series are natural candidates when their ecosystems and capacity align; CoreWeave or Google Cloud can be competitive if the required cluster is available and your software runs efficiently there.
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Fine-tuning and batch inference
These workloads often tolerate smaller allocations and more flexible scheduling. Compare one-GPU or fractional alternatives where available, then scale to eight GPUs only when throughput or memory requires it. Storage locality and queue time can matter more than peak interconnect bandwidth.
Interactive development
Use a small, readily available shape for notebooks, debugging, and data-pipeline work. Reserve eight-GPU nodes for measured runs. A lower hourly rate does not help if engineers spend days waiting for a large allocation.
Deadline-bound production training
Favor confirmed capacity, support escalation, durable checkpointing, and a tested recovery path over the lowest spot quote. Price a backup region or provider if missing the deadline is more expensive than paying a premium.
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- Write the workload contract: model size, precision, framework, target throughput, maximum runtime, checkpoint interval, and failure tolerance.
- Shortlist two node shapes: one optimized for memory and one for throughput, each with the required GPU count.
- Normalize the quote: calculate GPU-hour, node-hour, storage-month, data-transfer, support, and orchestration costs separately.
- Verify topology: confirm GPU locality, interconnect mode, placement behavior, and the collective library used by your framework.
- Test at realistic scale: run enough steps to include input loading, checkpointing, validation, and synchronization.
- Stress recovery: stop a worker, revoke spot capacity where possible, restore from a checkpoint, and measure lost work.
- Confirm operations: quotas, monitoring, alerts, access controls, image registry, secrets, incident contacts, and deletion procedures.
- Choose the purchase model: combine a committed baseline with flexible burst or spot capacity only after utilization is known.
Common mistakes and fixes
Comparing unlike hardware bundles
Symptom: the cheapest GPU-hour produces the highest total bill.
Fix: include CPU, RAM, local scratch, attached storage, network, and idle time in the same worksheet for every provider.
Assuming published capacity is immediately usable
Symptom: quota is approved but the scheduler cannot place the requested node count.
Fix: probe capacity in the exact zone, request alternate zones, and keep a tested fallback shape.
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Using spot without restart automation
Symptom: a reclaim discards many hours of training.
Fix: checkpoint to durable storage, capture optimizer state, and automatically requeue failed workers.
Ignoring data gravity
Symptom: GPUs are idle while data crosses a region or cloud boundary.
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Trusting vendor performance claims as guarantees
Symptom: projected completion time does not match production.
Fix: treat claims such as AWS’s “up to” improvements as hypotheses and validate with your model and software stack.
Bottom line for each provider
- Choose CoreWeave when specialist GPU capacity, the lowest listed H100 node rate, and a focused accelerator platform matter most—and the required region, quota, networking, storage, and support are confirmed.
- Choose AWS when EFA, UltraCluster scale, S3/FSx/EKS/SageMaker integration, or established AWS governance outweighs a modestly higher instance price.
- Choose Azure when Microsoft identity, enterprise contracting, Azure Machine Learning, or ND-series InfiniBand integration is decisive.
- Choose Google Cloud when A3 capacity exists in the required zone and Google Kubernetes, Vertex workflows, or dynamic spot and commitment economics fit the job.
Make the final decision from a measured cost-per-completed-work result and a capacity confirmation, not from a list-price leaderboard alone.
Quick Recap
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.
