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What this comparison can—and cannot—tell you
The published figures below are vendor pricing or product details, not the results of a matched performance test. The available information does not establish a four-provider winner for performance per dollar, nor does it provide comparable regional quotes for all four platforms. In particular, there is no directly comparable Azure H100 or H200 VM price here. Treat the prices as useful starting points for a workload estimate, not as a ranking.
A GPU-hour is not necessarily the cost of a training hour. Machine type, CPU and memory, disks, data movement, idle time, reservations, and interruptions can change the total. Network capabilities also matter: a multi-node job can spend differently from a job that fits on one machine, even when both use the same number of GPUs.
Published GPU configurations and prices
This table keeps each price attached to its provider’s stated configuration and purchase model. Lambda and AWS figures are not directly comparable: the Lambda figures are listed per GPU-hour for one-GPU configurations, while the AWS examples are hourly Capacity Blocks rates for eight-GPU instances.
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| Provider | GPU configurations identified | Published price example | Pricing scope and important qualification |
|---|---|---|---|
| Lambda Cloud | Self-serve HGX B200, H100, A100, and GH200; listed configurations range from 1 to 8 GPUs. | B200 SXM6: $6.99 per GPU-hour; H100 SXM: $4.29 per GPU-hour; H100 PCIe: $3.29 per GPU-hour; A100 SXM 40 GB: $1.99 per GPU-hour; GH200: $2.29 per GPU-hour. | These are the rates listed on Lambda’s current page for one-GPU configurations, before applicable taxes. The page lists different per-GPU rates for larger configurations; confirm the exact plan and rate. Lambda says billing is by the minute and advertises no egress fees. Source: Lambda Cloud pricing page, accessed 2026. |
| AWS EC2 | P5 instances use H100 GPUs; P5e and P5en use H200 GPUs. The listed families support up to 8 GPUs per instance. | P5.48xlarge: $41.528 per hour for 8 H100s in several US regions ($5.191 per accelerator). P5e.48xlarge: $47.76 per hour for 8 H200s in several regions ($5.97 per accelerator). | These are AWS Capacity Blocks for ML rates, not universal On-Demand rates. Region and purchase terms matter; align them before comparing with another provider. Source: AWS Capacity Blocks for ML pricing page, accessed 2026. |
| Microsoft Azure | The reviewed pricing page does not establish a directly comparable H100 or H200 VM SKU price. | Not stated for a comparable H100/H200 configuration; use the Azure pricing calculator with a named GPU VM SKU and region. | Include VM usage, storage, and network transfer in the estimate. Azure says standard egress charges apply and persistent disks are charged separately. Source: Azure Linux Virtual Machines pricing page. |
| Google Cloud | A3 accelerator-optimized machine types include H100 80 GB GPUs. | Not stated as a comparable total VM price here; GPU pricing is regional and is charged in addition to the VM machine type. | Use the Google Cloud pricing calculator to combine GPU and machine-type costs. The GPU price page excludes some costs, including disks, images, networking, sole-tenant nodes, and VM instance pricing. Source: Google Cloud GPU pricing page, accessed 2026. |
How each provider fits AI workloads
Lambda Cloud: a direct GPU-hour model
Lambda lists B200, H100, A100, and GH200 instances in 1-, 2-, 4-, and 8-GPU configurations. Its official documentation describes the on-demand service as Linux GPU-backed virtual machines and says displayed instance types are as of December 2025. The documentation associates instances with geographic regions, so check that the GPU configuration you need is available where your data and users require it. Lambda describes self-serve access as first-come; verify live availability and any applicable capacity conditions before scheduling a run.
The per-minute billing and advertised lack of egress fees make the stated pricing model relatively easy to interpret, but neither feature means every workload has no additional costs. Confirm any storage, taxes, and other applicable charges, and compare the exact multi-GPU plan rather than multiplying a one-GPU rate by the GPU count.
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AWS EC2: multi-GPU systems and regional reservations
AWS positions P5 for H100 workloads and P5e/P5en for H200 deep-learning and high-performance computing workloads. AWS describes these systems as combining high-bandwidth GPU interconnect, NVSwitch, and Elastic Fabric Adapter (EFA) networking, with support for scaling clusters. Those are vendor specifications relevant to distributed training, not evidence of faster performance than Lambda, Azure, or Google Cloud on a matched job.
Capacity Blocks provide a specifically named purchase model with regional rates. Do not treat their quoted hourly figures as AWS On-Demand prices or assume they are available in every region. Match the reservation window, region, GPU count, and instance resources to the alternative quote.
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Azure: build the estimate from a named VM SKU
The reviewed Azure Linux Virtual Machines pricing page directs customers to the Azure pricing calculator rather than providing a directly comparable H100 or H200 VM price. Create an estimate using the specific GPU VM SKU, region, Linux image, expected hours, storage, data transfer, and purchase option that match your workload. Without those inputs, a price or ranking against the other providers would be unsupported.
Azure’s billing details affect run planning: persistent disks are billed separately, standard egress charges apply, and a VM that is stopped but remains allocated can continue to incur charges. Deallocating the VM ends compute allocation billing, so include shutdown and restart behavior in the operating plan.
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Google Cloud: price the GPU and VM together
Google Cloud identifies H100 80 GB GPUs with A3 accelerator-optimized machine types. Its pricing guidance makes the key calculation explicit: the GPU charge is added to the VM machine-type charge, and the GPU rate depends on region. The GPU figure alone is therefore not a complete instance price.
Discounts depend on how the resources are purchased and used. Eligible GPU resources may receive sustained-use discounts. Spot GPU usage follows Spot prices and does not receive sustained-use discounts. Resource-based committed-use discounts require GPU reservations. Add the VM, disks, images, networking, and any other excluded items when estimating total cost.
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Build a fair GPU cloud cost comparison
Choose one representative job and hold its requirements constant across providers. Record the estimate date and identify what the quote includes and omits.
- Fix the hardware target. Specify GPU model, GPU count, memory per GPU, and whether the workload fits on one node. An H100 configuration is not interchangeable with a B200, H200, A100, or a different H100 form factor.
- Choose the location. Name the region or zone and account for data residency, latency, and whether the requested GPU configuration is actually available there.
- Measure the full runtime. Include startup, data staging, training, checkpointing, idle periods, shutdown, and any reruns—not only the time spent actively computing.
- State the purchase model. Identify on-demand, Spot or preemptible, committed-use, reservation, or capacity-reservation terms. Include interruption exposure and the risk of paying for unused committed capacity.
- Include the rest of the system. Add CPU, RAM, local and persistent storage, checkpoint retention, and data staging. Account for ingress and egress charges where applicable.
- Check the network for distributed jobs. For multi-GPU and multi-node training, compare the relevant interconnect and network capabilities as well as GPU count. A GPU price alone does not describe the cost or suitability of scaling out.
- Verify capacity and operational constraints. Check quotas, live availability, lead time, and any reservation requirements before basing a schedule on a published configuration.
Report the resulting estimate with the provider, named configuration, region, purchase model, expected hours, and included cost categories. A single price table cannot resolve those variables for every workload.
Why older price comparisons can mislead
AWS announced reductions for several EC2 NVIDIA GPU instance families effective June 1, 2025 for On-Demand pricing and after June 4, 2025 for Savings Plan purchases. Those dates describe a historical price change, not a current quote. They illustrate why an older article or saved estimate should be checked against the live provider price page and the intended purchase model before a budget decision.
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.
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