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HYPERSCALER LIST PRICES. SPECIALIST RATES.

AWS, Azure, and Google Cloud GPU pricing.
Against the specialists.

Compare published H100 prices from AWS, Microsoft Azure, and Google Cloud with specialist GPU clouds. See what on-demand costs, what commitments save, and when a specialist is the better buy.

Compare the rates Meet with an advisor

01 / ON-DEMAND LIST PRICES

Eight H100 GPUs.
Three clouds.

Each cloud sells H100 capacity as an eight-GPU instance. These are the published on-demand rates for Linux in each cloud’s main US region, with no commitment.

USD · Linux · On-demandChecked

Scroll the table horizontally to see configurations and source links.

Published on-demand prices for eight-GPU H100 instances on AWS, Google Cloud, and Microsoft Azure, checked September 21, 2026.
CloudInstance and regionPer instance-hourPer GPU-hourOfficial source
AWSp5.48xlarge: 8 × H100, 192 vCPUs, 2,048 GiB memory. US East (N. Virginia).$55.04$6.88On-Demand pricing
Google Clouda3-highgpu-8g: 8 × H100, 208 vCPUs, 1,871 GB memory, 6,000 GiB SSD. Iowa (us-central1).$88.49$11.06Accelerator-optimized pricing
Microsoft AzureND96isr H100 v5: 8 × H100 80 GB, 96 vCPUs, 1,900 GiB memory. East US.$98.32$12.29Linux VM pricing

Per GPU-hour is our calculation: the instance rate divided by eight. The Azure figure was read from Microsoft’s Retail Prices API, and the AWS figure from AWS’s published on-demand price data. The instances differ in CPU, memory, storage, and networking, so the hourly rate alone is not a like-for-like comparison. Prices vary by region and change over time.

02 / WHAT COMMITMENTS SAVE

Lower rates,
with strings attached.

Each cloud publishes cheaper rates for the same instance, in exchange for a commitment, a fixed window, or the risk of interruption. Per GPU-hour figures are our calculation.

AWS

EC2 Capacity Blocks reserve GPU instances for a set future window. The p5.48xlarge lists at $41.528 per instance-hour, about $5.19 per GPU-hour, in US East (N. Virginia). AWS says these prices update regularly with supply and demand, next in October 2026.

Google Cloud

For a3-highgpu-8g in Iowa, a 3-year committed use discount lists at $38.86 per hour, about $4.86 per GPU-hour, and a 1-year commitment at $61.38, about $7.67. Flex-start, where the job waits for capacity, lists at $38.32, about $4.79. Spot lists at $52.96.

Microsoft Azure

For ND96isr H100 v5 in East US, a 1-year reservation lists at $551,221 for the term, about $62.92 per hour or $7.87 per GPU-hour. A 3-year reservation lists at $1,134,310, about $5.40 per GPU-hour. Spot lists at $18.17 per hour, and can be evicted.

Read the fine print on every discount.

A multi-year commitment locks in spend for years. Spot capacity can be taken back mid-job. A queued or scheduled start means you wait for the GPUs. Compare the discounted rate with the terms you would actually accept, and ask whether the commitment guarantees capacity or only lowers the bill.

03 / THE SPECIALIST SIDE

What specialist
GPU clouds publish.

Specialist providers, sometimes called neoclouds, focus on GPU capacity. These on-demand H100 rates come from our GPU cloud pricing comparison, checked .

Published per GPU-hour

  • Runpod: $3.49 for an H100 SXM GPU Pod listing
  • Nebius: $3.85 for an HGX H100 on-demand instance
  • Crusoe: $3.90 for an H100 HGX on-demand instance
  • Lambda: $4.29 for a single H100 SXM instance
  • CoreWeave: about $6.16, from $49.24 for an eight-GPU instance

How to read the gap

On demand, every specialist rate above sits below all three hyperscalers. Hyperscaler commitment and reservation rates of about $4.79 to $5.40 per GPU-hour reach specialist on-demand territory, but only with a multi-year commitment, a fixed reservation window, or a queued start. Azure Spot lists lower, at about $2.27 per GPU-hour, but can be evicted.

Configurations and billing units differ between providers. Compare the whole configuration and total cost, not the GPU line alone.

04 / CHOOSING BETWEEN THEM

Price is one reason.
Here are the others.

The cheapest GPU-hour is not always the cheapest project. These factors often decide which side of the table fits.

A hyperscaler may fit when

  • Your data, applications, and security tooling already live in that cloud
  • You already hold a spending commitment or credits you need to use
  • Procurement and compliance have approved that provider
  • You need GPUs in many regions alongside other managed services

A specialist may fit when

  • GPU compute is the main cost and you want the lowest published rates
  • You need multi-node clusters with a fast network between servers
  • You want shorter terms than a multi-year commitment
  • Your team can run workloads outside your primary cloud

Moving data between clouds can add transfer charges and time. Check each provider’s data transfer pricing before splitting training from the rest of your stack. Planning multi-node training? See bare metal GPU clusters.

05 / COMPARE QUOTES FAIRLY

Put every quote
on the same basis.

Ask each provider, hyperscaler or specialist, for the same details before comparing totals.

Configuration

  • GPU model, variant, and memory
  • Billing unit: per GPU or per instance
  • CPU, system memory, and local storage included
  • Region and network between servers

Total cost and terms

  • Storage, data transfer, and support charges
  • Commitment length, and whether capacity is guaranteed
  • Quota or approval needed before launch
  • Interruption terms for spot or queued capacity

A dated snapshot of published prices, not a live quote. Rates, regions, and availability change. Confirm current pricing and terms with each provider before budgeting.

LET’S FIND YOUR WAY FORWARD

Your next move
starts with a conversation.

Bring your current cloud bill or a GPU quote. An advisor can help you compare it with specialist options and agree on the next step for your project.

Our advisory services are free to you. We’re compensated by whichever provider you choose through us.

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YOUR GPU PRICING CONSULTATION

Bring your questions.
We’ll bring perspective.

A few details help us make the most of your time:

  • Your current cloud and GPU spend, if any
  • GPU model and count you need
  • Target region and timeline
  • An existing quote or commitment
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HYPERSCALER GPU PRICING QUESTIONS

Before you
compare bills.

Bring your current bill or an existing quote.
Meet with an advisor

Which big cloud has the lowest on-demand H100 price?

In our September 21, 2026 check, AWS listed the lowest on-demand rate of the three at $6.88 per GPU-hour for p5.48xlarge in US East (N. Virginia). Google Cloud listed $11.06 in Iowa and Azure $12.29 in East US. Rates vary by region and change over time.

Are specialist GPU clouds cheaper than AWS, Azure, or Google Cloud?

On published on-demand rates, usually. The specialists in our comparison list $3.49 to $6.16 per GPU-hour for H100. Hyperscaler discounts can come close, but with commitments, fixed reservation windows, or interruptible capacity. Compare total cost, including storage, data transfer, and support.

What is the difference between a commitment and a capacity guarantee?

A spending commitment lowers your rate in exchange for committed spend. It does not always reserve GPUs for you. A capacity reservation, like an AWS Capacity Block, holds specific capacity for a set window. Ask exactly what each offer secures.

Can I keep my main cloud and rent GPUs elsewhere?

Yes. Many teams keep data and applications in their main cloud and run GPU work with a specialist. Account for data transfer charges and the time needed to move data before you split the workload.

What does your advisory service cost?

Our advisory services cost you nothing. We’re compensated by whichever provider you choose through us. You pay the provider for your infrastructure and services, with no obligation to choose a provider.