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There is no universally cheapest GPU cloud. In the September 2026 comparison published by iTechGuides, CoreWeave was the lowest-cost option among comparable eight-H100 nodes, followed by AWS, Google Cloud and Azure. That ranking is only a dated snapshot: purchasing mode, region, capacity, quota, CPU and RAM, interconnect, storage, egress, support and interruption recovery can change the winner for your job.
For a defensible decision, compare identical hardware and calculate the cost of completed work rather than looking only at a GPU-hour. The sections below show the published prices, explain what each billing mode means, and provide a method you can apply to training, inference or batch workloads.
What the published eight-H100 comparison shows
The clearest like-for-like data is a September 2026 comparison of nodes containing eight NVIDIA H100 GPUs. iTechGuides reported CoreWeave as the least expensive of the four providers, with AWS at $55.04 per hour, Google Cloud at $88.49 per hour and Azure at $98.32 per hour. The comparison was published in October 2026, so treat it as a point-in-time result rather than a standing price list.
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1Fix the driver behind crashes, sound loss and screen glitches2Repair Windows errors before they cause bigger problems3Scan for outdated or missing drivers - takes under a minute| Provider | Compared configuration | Price and mode | How to interpret it |
|---|---|---|---|
| CoreWeave | Eight-H100 node (the comparison’s configuration) | Lowest in the cited comparison; exact hourly figure was not stated | Confirm the current H100 SKU, region, contract terms and availability directly before committing. |
| AWS | Eight-H100 node, aligned with EC2 p5.48xlarge specifications | $55.04/hour, comparison snapshot | Independent comparison figure; AWS says regional prices must be checked in its live pricing system. |
| Google Cloud | A3 High VM with eight H100 GPUs | $88.49/hour, comparison snapshot | Matches the official A3 High DWS Flex-start rate shown on the Google Cloud pricing page accessed October 20, 2026. |
| Microsoft Azure | ND96isr H100 v5 eight-H100 node | $98.32/hour, comparison snapshot | Independent figure, not an Azure pricing-page quotation; verify region and billing mode in Azure. |
The table does not prove that CoreWeave will be cheapest for every H100 job. A provider can have the lowest nominal rate but still produce a higher completed-job cost if the required region has no capacity, your quota request is delayed, the network topology is unsuitable, or interruptions force expensive restarts.
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Normalize the hardware before comparing prices
A GPU count alone is not a comparable specification. Record every item that affects throughput and operating cost.
- GPU model and memory: An H100, A100, H200, B200 and GB200 are different products. Memory capacity and bandwidth can determine whether a model fits and how quickly it trains.
- GPU count and topology: Eight GPUs connected through NVLink or NVSwitch can behave very differently from eight GPUs linked only through a host network. Distributed training is especially sensitive to collective-communication bandwidth and latency.
- CPU and system RAM: Data loading, tokenization, checkpointing and preprocessing can leave expensive GPUs idle if the host is undersized.
- Local and attached storage: Include local NVMe, persistent disks, object-storage reads and writes, and checkpoint retention. A low compute rate can be outweighed by storage or I/O charges.
- Network and egress: Measure the data path between storage, workers, users and other clouds. Data-transfer egress can dominate inference or multi-cloud pipelines.
- Region and quota: The same SKU may have different prices and different availability by region. A listed price is not useful if your account cannot obtain the required quota.
AWS’s EC2 accelerated-instance specification lists p5.48xlarge with eight NVIDIA H100 GPUs and 640 GiB of total accelerator memory. AWS On-Demand Instances are billed by the hour or second with a 60-second minimum, but the actual regional price must be checked in the live AWS pricing system. Google Cloud lists a3-highgpu-8g as an accelerator-optimized machine type with eight H100 80GB GPUs attached. These descriptions establish the GPU configuration; they do not make the surrounding CPU, RAM, storage or network identical across providers.
Purchasing mode can reverse the ranking
Published hourly figures often mix products with different availability guarantees. Put the billing mode beside every price.
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On-demand capacity
On-demand is the simplest baseline: you request a VM and pay the published rate without a long-term commitment. It is usually the right reference for an urgent, interruption-sensitive job or an uncertain workload. AWS explicitly describes On-Demand Instances as capacity paid by the hour or second, with a 60-second minimum and no long-term commitment. The trade-off is that you pay the highest flexible rate and still face regional capacity limits.
Spot and preemptible capacity
Spot or preemptible instances trade a lower price for the possibility that the provider will reclaim the VM. They suit fault-tolerant batch inference, hyperparameter sweeps and training jobs with frequent checkpoints. They are risky for a job that cannot resume from a checkpoint, has expensive initialization, or must finish by a fixed deadline. Your calculation must include expected retries and the engineering work needed to detect and recover from interruption.
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Calendar, flex-start and scheduled capacity
Google Cloud’s A3 pricing illustrates why the product label matters. DWS Flex-start, DWS Calendar Mode and spot are not interchangeable offers: each has its own scheduling, eligibility and interruption behavior. A scheduled window can be much cheaper when you can plan ahead, but it does not provide the same flexibility as an immediately available on-demand VM.
Reservations and committed-use discounts
Reservations or commitments reduce the unit rate in exchange for a term, spending obligation or usage commitment. They can be economical for a stable, continuously running service, but they create financial risk if demand falls or the workload moves to another GPU generation. Check whether the commitment is tied to a region, machine family, GPU type or minimum spend before comparing it with an unconstrained on-demand price.
Google Cloud A3 High: the same eight H100s at several prices
The Google Cloud accelerator table accessed on October 20, 2026 lists the following rates for the A3 High eight-H100 configuration. These are provider-published figures, and the exact region, mode, eligibility and billing conditions apply.
| Google Cloud pricing mode | Listed hourly rate | Important qualification |
|---|---|---|
| DWS Flex-start | $88.490000119/hour | Flexible start product; availability and scheduling rules apply. |
| DWS Calendar Mode | $38.3200/hour | Scheduled calendar capacity; not equivalent to unrestricted on-demand use. |
| Current spot | $41.6000/hour | Interruption risk and reclaim behavior apply. |
| Listed commitment tier | $50.458920548/hour | Commitment eligibility and term apply. |
| Three-year commitment tier shown in the table | $38.864383195/hour | Requires the stated three-year commitment conditions. |
The difference between $88.490000119 and $38.3200 per hour is not a typo; it reflects different purchasing products. Comparing the first number with a competitor’s spot or committed rate would produce a misleading conclusion. Build separate comparisons for flexible on-demand, interruptible, scheduled and committed capacity.
CoreWeave prices: compare the generation, not just the provider
CoreWeave’s current North America table lists GB200 NVL72 on-demand at $42.00 per hour and HGX B200 on-demand at $68.80 per hour, with separate spot prices where available. GB200 and B200 are newer GPU generations than H100, so neither figure is a like-for-like H100 comparison. A newer accelerator may complete a job faster even when its hourly rate is higher; conversely, your software, memory footprint or framework support may favor H100.
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| CoreWeave SKU | Generation/configuration | On-demand rate | Comparison warning |
|---|---|---|---|
| GB200 NVL72 | GB200 system | $42.00/hour | Different generation and topology from an H100 node. |
| HGX B200 | B200 system | $68.80/hour | Different generation from H100; do not use as an H100 price. |
For an H100 decision, request the exact CoreWeave H100 configuration, including GPU memory, host resources, interconnect, region, storage and interruption terms. The cited comparison establishes that CoreWeave was lowest among the four eight-H100 options, but it does not supply a universal CoreWeave H100 tariff.
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The useful quantity is the cost to produce a finished training run, inference batch or service-hour, not the advertised GPU-hour. Use this sequence:
- Estimate required accelerator time. Use measured throughput or a small pilot to determine the GPU-hours needed for the job at the target batch size and precision.
- Adjust for utilization. Divide required accelerator-hours by the fraction of time GPUs will be doing useful work. Data stalls, evaluation, checkpointing and orchestration reduce utilization.
- Add interruption recovery. Multiply by expected retry overhead for spot or preemptible capacity. Include lost progress, restart time and any repeated data staging.
- Add non-GPU charges. Include VM host charges, persistent and local storage, object-storage requests, snapshots, network egress, load balancers, observability and support.
- Apply discounts only when you can actually use them. A commitment rate is valid only if your workload meets its term, region and product requirements.
Worked example
Assume a hypothetical training run needs 1,000 hours of useful eight-GPU compute. At 70% average utilization, the run requires 1,000 ÷ 0.70 = 1,428.6 billed hours before failures. At the AWS comparison rate of $55.04 per hour, compute would be about $78,629, excluding storage, egress and other services.
Now assume an interruptible option has a 15% retry overhead. The billed time becomes 1,428.6 × 1.15 = 1,642.9 hours. Using the Google Cloud A3 current-spot rate of $41.6000 per hour, the illustrative compute cost is about $68,343. That is cheaper in this example, but only if the job can tolerate interruptions and the assumed retry rate reflects your region and workload. A higher failure rate, slower restart, or deadline penalty can eliminate the apparent saving.
For a production estimate, replace the assumptions with measurements from a short run: examples include samples processed per second, checkpoint duration, restart time, observed reclaim frequency and bytes transferred. Keep one worksheet per provider and purchasing mode so unlike offers are not blended.
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Availability, quota and schedule are part of the price
GPU supply is variable. A $40 hourly offer that takes two days to obtain may cost more than a $60 offer available immediately if engineers, reserved test windows or a launch deadline are waiting. Before selecting a provider, verify:
- Quota approval for the exact GPU family and count.
- Capacity in the required region at the time window you need.
- Whether the offer can be interrupted, delayed or rescheduled.
- Maximum VM lifetime and maintenance behavior.
- Checkpoint and restart support in your orchestration stack.
- Data-residency, compliance and network placement requirements.
Availability also affects reproducibility. If a training run spans several weeks, a commitment or reserved capacity may be worth more than a lower spot price because it reduces scheduling uncertainty. For bursty experiments, flexible or interruptible capacity can win despite occasional retries.
A practical provider-selection workflow
- Define the workload. Record model size, precision, dataset volume, target throughput, deadline, checkpoint interval and tolerance for interruption.
- Choose one hardware generation. Start with H100, A100, H200, B200 or GB200; do not compare unlike generations in one price table.
- Match the node shape. Align GPU count, memory, CPU, RAM, local NVMe, network topology and storage path.
- Build mode-specific quotes. Create separate rows for on-demand, spot/preemptible, scheduled, reserved and committed use.
- Check operational constraints. Confirm quota, region, capacity, egress pricing, support response and maximum runtime.
- Run a pilot. Measure throughput, utilization, startup time, checkpoint time and recovery behavior with your actual software.
- Compute total job cost. Include all retry, storage, transfer and orchestration costs, then compare the cost per completed job or delivered inference.
- Recheck immediately before purchase. GPU prices and capacity move quickly; the rates above are dated snapshots, not guarantees for a later booking.
Which option fits common workload patterns?
Interruptible research and batch jobs
Spot or preemptible capacity is often the lowest nominal-cost choice when checkpoints are frequent and a delayed completion is acceptable. Use multiple availability zones or providers only if the data-transfer and orchestration overhead does not erase the saving.
Deadline-bound training
Prefer capacity that you can schedule and hold for the full run. A higher hourly rate can be rational when a failed reservation would delay a product launch or a contracted delivery.
Steady production inference
Compare committed or reserved capacity with on-demand flexibility. Include idle headroom, autoscaling behavior, network ingress and egress, observability and support. The cheapest GPU-hour is not necessarily the cheapest served request.
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Place compute near the data when possible. A lower GPU rate in another cloud can be overwhelmed by egress, replication and synchronization costs. Also verify that the provider’s interconnect and driver stack support your framework and collective-communication requirements.
How to read the four-provider result responsibly
Use the iTechGuides eight-H100 ranking as a starting hypothesis: CoreWeave was cheapest in that dated comparison, AWS followed at $55.04 per hour, Google Cloud at $88.49, and Azure at $98.32. Then rebuild the comparison for your own region and mode. Google Cloud’s official A3 table demonstrates why: its eight-H100 configuration spans rates from $38.3200 in Calendar Mode to $88.490000119 in Flex-start, with spot and commitment prices in between.
Azure’s $98.32 figure is specifically an independent September 2026 comparison of an ND96isr H100 v5 node. Because it was not taken from an Azure pricing page in the cited evidence, verify the current Azure region, machine SKU and billing mode directly. Likewise, verify the live AWS p5.48xlarge price and the exact CoreWeave H100 offer rather than assuming that a newer GB200 or B200 price applies.
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The final choice is the provider and purchasing mode that delivers your required result, by your deadline, at the lowest measured total cost. Recalculate whenever the GPU generation, region, contract, utilization or interruption tolerance changes.
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.
