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There is no defensible universal “cheapest” winner among AWS, Azure, and Google Cloud from the currently published figures. The numbers available are not like-for-like: they use different regions, GPU counts, machine shapes, billing models, and service scopes. A Google Cloud A3 High H100 Spot example is a whole-VM interruption-prone rate, while Azure examples show monthly totals for either Azure Machine Learning or Azure Databricks. AWS documentation confirms the available GPU configurations and purchasing paths but does not provide a matching hourly price in the supplied figures. To compare fairly, quote the same region and currency, GPU model and count, vCPU/RAM shape, purchase term, and cost scope, then add storage and network charges.
What does cloud GPU pricing actually include?
A GPU price can mean an attached accelerator only, a complete virtual machine, or a managed platform bill. Those are different products and cannot be ranked in one table without labeling the scope.
- Attached GPU pricing: Google Cloud states that an attached GPU is charged in addition to the machine type. You pay for the base VM and the accelerator separately.
- Accelerator-optimized machines: Some Google Cloud machine families bundle the GPU cost into the machine price, so adding a second GPU rate would double-count.
- Whole-VM pricing: A Spot VM page can show one hourly total that already combines the machine and GPU. The published Google A3 High example below is this kind of total.
- Managed-service pricing: A platform can add its own metering. Azure Databricks says its displayed totals include VM compute and DBU charges, so they are not bare Azure VM rates.
Every quote should therefore state the GPU model, number of GPUs, GPU memory when listed, vCPU and RAM, region, currency, billing model, and whether platform, storage, network, and software charges are included. Prices on dynamic cloud pages change; the figures cited here were checked on October 25, 2026, but that is an access date, not a claimed publication date.
How does AWS GPU pricing work?
AWS gives you several ways to buy EC2 GPU capacity, and the best rate depends on whether your workload can move, stop, or wait for capacity. The supplied AWS material establishes configurations and purchase mechanics, not a comparable hourly quote.
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Which AWS GPU machines are relevant?
AWS accelerated-computing documentation lists P5 systems with NVIDIA H100 GPUs and P5e systems with H200 GPUs. A P5.4xlarge has one H100; a P5.48xlarge has eight H100 GPUs; the P5e configurations listed use eight H200 GPUs. These are hardware configurations, not prices, and a one-GPU and eight-GPU machine should never be compared as though they were equivalent capacity.
What are the AWS purchase options?
- On-Demand: pay for running capacity without a long-term commitment. This is the cleanest baseline for a short experiment, but it generally carries the least commitment discount.
- Savings Plans: commit to a usage level for a term in exchange for discounted eligible usage. This is a usage commitment, not a promise of a particular physical GPU or location.
- Reserved Instances: reserve defined instance attributes for a term. They suit predictable configurations better than rapidly changing GPU requirements.
- Spot: bid on spare capacity at a lower, variable rate. AWS describes Spot as appropriate when jobs are flexible and interruptible; an interruption can stop or reclaim the instance.
- Dedicated Hosts or Dedicated Instances: pay for isolation or tenancy requirements rather than simply the lowest compute rate.
- Capacity Reservations: reserve capacity in a particular Availability Zone. This addresses placement certainty, while a Savings Plan addresses a usage commitment; they solve different problems.
For an AWS comparison, record the exact EC2 instance type, Availability Zone or region, number of GPUs, purchase option, and whether the quote includes attached EBS storage, data transfer, and any licensing. Without those fields, an AWS “GPU hourly price” is incomplete.
How does Azure GPU pricing work?
Azure’s N-series is divided by workload, so choosing the family is part of the price decision. ND targets deep learning and large-scale AI, NC targets compute-intensive AI/ML, HPC, and rendering, and NV targets graphics-intensive applications and visualization.
Which Azure families fit common workloads?
- ND-series: designed for deep learning, AI training, and large-scale work. The documented families include NVIDIA A100, H100, H200, GB200, and AMD MI300X devices across specific series.
- NC-series: aimed at compute-intensive AI and machine learning, high-performance computing, and rendering.
- NV-series: intended for graphics, remote visualization, and other graphics-intensive workloads.
Availability varies by series and region. Verify the exact SKU in the Azure portal before comparing it with a machine in another cloud; an apparently similar family can have a different GPU count, CPU, memory, or regional availability.
What Azure prices are actually published?
The Azure Machine Learning pricing page displays an ND96asr A100 v4 configuration with eight A100 GPUs. Its monthly Pay-As-You-Go total is $19,853.810; the same page displays $16,512.42 per month for a one-year savings-plan option and $9,946.77 per month for a three-year option. These are figures shown on that Azure Machine Learning pricing page, not a region-matched quote against AWS or Google Cloud. The page context matters: they are monthly totals for the specified Azure Machine Learning configuration, not a generic A100 hourly tariff.
A separate US example on the Azure Databricks pricing page lists an NC24ads A100 v4 GPU cluster at $7,500.75 per month Pay-As-You-Go. That amount includes both VM compute and Databricks DBU charges. It is therefore a managed-service total and must not be placed beside a bare Azure VM price or treated as evidence that the NC machine itself costs $7,500.75 per month.
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How should Azure commitments be compared?
Keep Azure Machine Learning, Databricks, and standalone Azure VM quotes in separate columns or tables. For each one, capture the term, payment structure, region, machine SKU, GPU count, and included platform fees. A one-year or three-year saving is meaningful only when your workload can run for that term and the quoted service remains the same.
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How does Google Cloud GPU pricing work?
Google Cloud separates attached accelerator charges from machine-type charges unless you choose an accelerator-optimized family whose machine price already includes the GPU. That distinction is the main reason a GPU-only number cannot be used as a complete VM comparison.
What does an attached GPU cost?
Google’s GPU pricing page explains that each attached GPU adds to the cost of the instance in addition to the machine type. In practical terms, calculate the selected machine type plus the GPU resource, then add persistent disk, network egress, and any operating-system or software charges that apply. For accelerator-optimized families, use the bundled machine price instead of adding a second GPU line.
How do Google Spot and commitments affect the bill?
Google publishes separate Spot VM totals, and Spot prices can change. Spot GPU resources do not receive sustained-use discounts. GPU resources may qualify for resource-based committed-use discounts when the applicable reservation requirements are met, so a commitment quote should identify the reservation and term rather than simply applying a percentage to a Spot rate.
The Google Cloud Spot VM page displays $6.307365068 per hour for a listed one-GPU A3 High H100 Spot configuration. This is a page-displayed whole-VM Spot example, not an on-demand H100 price or a universal H100 rate. The machine configuration and region shown on the page control its meaning, and the instance remains subject to Spot interruption and changing prices.
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What do the available published figures show?
The following table preserves each figure’s scope instead of turning unlike prices into a ranking. “Not stated” means the supplied page excerpt did not establish that field; it is not an assertion that the provider never publishes it.
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| Provider and region | GPU model, count, and memory | VM size (vCPU/RAM) | Full on-demand hourly total | Spot hourly total and interruption terms | Commitment price and term | Attached storage, network, or software charges | Source and date checked |
|---|---|---|---|---|---|---|---|
| AWS, region not stated | P5.4xlarge: 1 H100; P5.48xlarge: 8 H100; P5e configurations: 8 H200; memory not stated | Not stated | Not stated | Not stated | Purchase paths documented; prices not stated | Not stated | AWS accelerated-computing and EC2 purchasing documentation, checked 2026-10-25 |
| Azure, region not stated | ND96asr A100 v4: 8 A100; memory not stated | Not stated | Not stated as an hourly VM figure | Not stated | $19,853.810/month Pay-As-You-Go; $16,512.42/month displayed one-year plan; $9,946.77/month displayed three-year plan | Azure Machine Learning service scope; other charges not stated | Azure Machine Learning pricing page, checked 2026-10-25 |
| Azure, US example | NC24ads A100 v4; GPU count and memory not stated in the excerpt | Not stated | Not stated as an hourly VM figure | Not stated | $7,500.75/month Pay-As-You-Go | Includes VM compute and Databricks DBU charges | Azure Databricks pricing page, checked 2026-10-25 |
| Google Cloud, region not stated | A3 High: 1 H100; memory not stated | Not stated | Not stated | $6.307365068/hour Spot whole-VM total; interruption terms apply | Resource-based committed-use eligibility described; price and term not stated | Bundling depends on machine family; attached-GPU storage and network charges not stated | Google Cloud Spot VM pricing page, checked 2026-10-25 |
Because the rows differ in region, service scope, and purchase model, the table cannot identify a cheapest provider. It is useful as a warning against comparing a managed monthly total, a Spot whole-VM rate, and an undocumented on-demand configuration as if they were the same product.
How can you build a fair three-way GPU comparison?
Use one workload definition and one quote worksheet for all three providers. This procedure prevents the most common apples-to-oranges errors.
- Fix the geography and currency. Choose one region that offers the required GPU on all three platforms, and keep every quote in the same currency. Record the region exactly; “US” is not precise enough when availability and network pricing vary by location.
- Specify the accelerator. Select one GPU model and count, such as one H100 or eight A100s. Record GPU memory when the page provides it. Do not compare one H100 with an eight-H100 machine merely because both are labeled P5, A3, or A100.
- Match the host shape. Capture vCPU and RAM as well as the GPU. If one provider’s closest shape has materially different CPU or memory, disclose the mismatch instead of hiding it in a single hourly number.
- Choose the billing model. Obtain on-demand prices for the baseline, then quote Spot separately with interruption behavior. Add savings-plan, reserved, or committed-use options only with their exact term and commitment requirement.
- Define service scope. Decide whether the comparison is bare VM, a managed machine-learning service, or a data platform. Keep platform fees such as Databricks DBUs outside a bare-VM table, or include equivalent platform fees for every provider.
- Add non-compute charges. Estimate boot and persistent storage, snapshots, data transfer, public IPs, operating-system licenses, orchestration, and idle attached disks. State which of these are included in each quote.
- Model utilization. Multiply the hourly total by expected running hours, then add fixed and transfer charges. For a commitment, compare that result with the committed monthly obligation and include the cost of unused capacity.
- Recheck immediately before purchase. Dynamic pages and regional capacity change. Save the SKU, quote timestamp, currency, region, and billing assumptions so a later invoice can be reconciled with the number you used.
When is Spot worth the risk?
Spot is appropriate for checkpointed training, batch inference, rendering, and other jobs that can restart or move. It is not a like-for-like substitute for on-demand capacity because interruption and price variability change the effective cost.
Use Spot when the workload can recover
- Checkpoint frequently enough that an interruption loses only a bounded amount of work.
- Keep images, checkpoints, and logs in durable storage rather than on the temporary VM disk.
- Measure completed work per dollar, not just the advertised hourly rate.
Prefer on-demand or a commitment when continuity matters
Interactive development, latency-sensitive services, and jobs that cannot be restarted need predictable availability. A commitment can lower the rate for steady use, but it introduces a term obligation; a capacity reservation addresses placement certainty and is a separate decision from a usage discount.
How do you calculate break-even for a commitment?
Start with the same workload hours for both choices. An hourly baseline is:
on-demand cost = hourly VM total × billed hours + storage + network + software
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For a commitment, use:
commitment cost = committed term payments or hourly commitment × covered hours + uncovered charges + cost of unused commitment
The break-even utilization is the point where those totals match. Do not derive it from the GPU rate alone when the machine has separate CPU, RAM, disk, or platform charges. Azure’s displayed one-year and three-year savings-plan figures, for example, are monthly amounts for the specified Azure Machine Learning configuration; they cannot be converted into a general A100 discount without the page’s full assumptions.
Which provider should you choose?
Choose based on the workload and commercial constraint, not an unsupported headline ranking.
- Choose AWS when you need the documented P5 H100 or P5e H200 configurations and want to select among On-Demand, Savings Plans, Reserved Instances, Spot, dedicated tenancy, or capacity reservations.
- Choose Azure when the N-series workload fit or an integrated Azure Machine Learning or Databricks workflow matters. Keep the managed-service fee visible in the comparison.
- Choose Google Cloud when its available accelerator-optimized or attached-GPU shape fits the workload, and model Spot, attached-resource pricing, and committed-use eligibility separately.
For a procurement decision, request current full-VM quotes from all three providers using the same region, GPU quantity, host shape, term, and service scope. The supplied figures support this method and explain each provider’s mechanics, but they do not establish a fair current price winner.
Frequently Asked Questions
Why can a GPU-only rate be misleading?
The accelerator may be billed separately from the host machine, or its cost may already be bundled into an accelerator-optimized VM. A complete estimate must include both components when they are separate and avoid adding the GPU twice when they are bundled.
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Only after converting both to the same expected utilization and keeping service scope identical. A monthly managed-platform total and an hourly bare-VM rate still differ even after multiplying by the same number of hours because platform fees may be included in only one figure.
What should I record when saving a cloud price quote?
Save the exact SKU, region, currency, GPU count, host shape, billing model, commitment term, included services, and the date and time checked. This audit trail lets you explain invoice differences when a dynamic pricing page or regional availability changes.
Does a lower Spot rate guarantee a lower project cost?
No. Interruptions can cause reruns, lost progress, queueing, or engineering work to make the job restartable. Evaluate completed workload output and recovery overhead alongside the advertised Spot price.
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.
