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There is no defensible universal cheapest provider in the available price examples: they do not match on region, GPU and VM configuration, billing model, or included services. A fair comparison starts with the same workload and compares the full VM cost—not just the accelerator rate—under the same purchase terms. The figures below show what can be established, what cannot, and how to build a quote that answers your actual buying question.
Which cloud is cheapest for GPU compute?
The available figures do not establish whether AWS, Azure, or Google Cloud is cheapest. They describe different configurations and billing scopes: Azure examples are monthly, one includes Databricks platform charges, and the Google Cloud example is a Spot VM rate. The source set also does not provide a matched AWS hourly price.
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Outbyte Driver Updater FREEScan for outdated or missing drivers - takes under a minuteDriver Scan →Outbyte PC Repair FREERepair Windows errors before they cause bigger problemsFix Now →A GPU price is meaningful only when its context is clear. Compare the same region and currency, GPU model and count, VM size, purchase model, and cost scope. A number for an accelerator alone is not necessarily the price of the VM that runs it.
What a fair price comparison needs
- Region and currency: Prices and availability can vary by location. State the region used for every quote rather than treating one region’s rate as global.
- GPU configuration: Record the accelerator model and quantity. One H100 is not equivalent to eight A100s, and a GPU count without the model is incomplete.
- Full VM shape: Include the VM or machine type, vCPUs, RAM, and GPU configuration. Check whether the displayed amount is for the whole machine or only its attached accelerators.
- Purchase terms: Distinguish on-demand or pay-as-you-go from Spot, reservations, and commitment plans. These terms affect price and, in some cases, availability or interruption risk.
- Cost scope: Identify whether storage, networking, operating system or other software, and a managed platform fee are included. Keep managed-service totals separate from base VM rates.
Price examples available for this comparison
| Provider and example | Configuration and billing | Displayed price | What the figure includes or does not establish |
|---|---|---|---|
| Azure Machine Learning pricing page | ND96asr A100 v4; eight A100 GPUs; Pay-As-You-Go and displayed one- and three-year plans | $19,853.810/month Pay-As-You-Go; $16,512.42/month for the displayed one-year plan; $9,946.77/month for the displayed three-year plan | Figures shown on that pricing page, accessed 2026. They are not a region-matched quote against the other examples. |
| Azure Databricks pricing page, US example | NC24ads A100 v4; Pay-As-You-Go | $7,500.75/month | The total includes VM compute and Databricks unit charges (DBUs), so it is not a standalone Azure VM price. Page accessed 2026. |
| Google Cloud Spot VM pricing page | A3 High; listed one-GPU H100 Spot VM | $6.307365068/hour | A page-displayed Spot whole-VM example, not an on-demand rate or a general H100 price. Machine configuration and region matter. Page accessed 2026. |
| AWS | Instance configurations are listed, but no matching hourly rate is supplied here. | Not stated (AWS pricing source) | Configuration facts alone do not establish a price for a comparable workload. |
The access date for the cited pricing pages is 2026-10-25; it is not the announcement or publication date of any price. Pricing pages can change, so treat the displayed values as examples to recheck rather than guaranteed quotes. They should not be ranked against one another as if they priced the same workload.
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Why can a GPU rate differ from the full VM price?
Because providers present GPU costs differently: an accelerator may be an added charge on top of the machine type, or its cost may be bundled into an accelerator-optimized machine price. The bill can also include services beyond the VM.
Google Cloud: attached versus bundled GPUs
Google Cloud says, “Each GPU adds to the cost of your instance in addition to the cost of the machine type.” That describes GPUs attached to a machine type: add the GPU charge to the machine cost to estimate compute. Google also says some accelerator-optimized machine families bundle GPU costs into the machine price, so do not add a separate accelerator charge to those totals unless the pricing details specify one.
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Its GPU pricing page lists regional prices and explains that resource-based committed-use discounts may apply to eligible GPU resources when the applicable reservation requirements are met. Spot GPU prices follow Spot pricing and do not receive sustained-use discounts. The displayed A3 High example is already a whole-VM hourly Spot total; it is not a separate GPU fee to add to another machine rate.
Azure: VM pricing versus managed-service totals
The Azure Databricks example is a useful warning about scope. Its NC24ads A100 v4 total combines VM compute and DBU charges. Comparing that total directly with a bare VM price from another provider would make the Azure number appear to cover the same thing when it does not.
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The Azure Machine Learning page separately displays monthly totals for an ND96asr A100 v4 configuration. Those figures are page-displayed service-pricing examples, not evidence of an equivalent quote in a specified region against AWS or Google Cloud. Before comparing them, open the pricing details and establish exactly what the displayed total covers.
Which GPU VM families fit different workloads?
The right family depends on the job, not just the price. Azure’s N-series descriptions distinguish AI and deep-learning work, compute-intensive workloads, and graphics or visualization; AWS and Google Cloud offer named accelerator configurations that must likewise be matched to the workload and full VM requirements.
Azure N-series
- ND-series: Intended for deep learning and AI, including large-scale work.
- NC-series: Intended for compute-intensive workloads, including AI/ML, high-performance computing, and rendering.
- NV-series: Intended for graphics-intensive workloads and visualization.
Azure’s VM-series information names NVIDIA A100, H100, H200, and GB200 GPUs, as well as AMD MI300X devices, across its available series. The specific accelerator, VM shape, and regional availability depend on the series; verify them for the region you plan to use rather than assuming every listed model can be provisioned everywhere.
AWS accelerated-computing examples
AWS’s accelerated-computing instance documentation lists P5 configurations including P5.4xlarge with one H100 and P5.48xlarge with eight H100 GPUs. It also lists P5e configurations with eight H200 GPUs. These details help identify possible configurations to quote, but they are not prices and do not establish that a similarly named machine at another provider is a like-for-like match.
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Google Cloud accelerator pricing
Google Cloud’s GPU pricing information distinguishes GPUs attached to machine types from accelerator-optimized families with GPU cost bundled into the machine price. For a quote, check whether the selected machine is in the latter category, then confirm the GPU count, region, and full machine configuration before comparing its total with another provider.
Do Spot prices or commitments change the comparison?
Yes. Spot can reduce the price in exchange for a different availability and interruption profile, while commitment options trade flexibility for a planned-term price. Neither should be treated as an on-demand rate with the same reliability or obligations.
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AWS purchasing options
AWS EC2 lists On-Demand, Savings Plans, Reserved Instances, Spot, Dedicated Hosts or Instances, and Capacity Reservations. These options solve different purchasing needs:
- On-Demand is a useful baseline when you need a flexible price without making a longer usage commitment.
- Savings Plans are a usage commitment; consider them in light of how consistently the workload will run.
- Reserved Instances are a distinct purchasing option, not simply another name for a usage commitment.
- Spot is aimed at workloads that can tolerate interruption. It is a different reliability choice as well as a different price.
- Capacity Reservations address capacity availability; distinguish the capacity question from the question of committing to usage.
- Dedicated Hosts or Instances are additional purchase paths whose fit depends on the workload’s requirements.
A price comparison should record the exact purchase path. Do not compare an AWS commitment price to another provider’s on-demand price and present the result as a simple hourly ranking.
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Google Cloud Spot and commitments
Google Cloud publishes separate Spot VM totals. Its pricing information notes that Spot prices can change, and that Spot GPU pricing does not receive sustained-use discounts. The listed $6.307365068/hour A3 High example is therefore specific to the displayed one-GPU H100 Spot configuration; it should not be interpreted as the standard H100 hourly price.
Google Cloud also describes resource-based committed-use discounts for eligible GPU resources when reservation requirements are met. Confirm the relevant reservation conditions and model the commitment separately from a flexible Spot workload; their terms and reliability are not interchangeable.
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For the ND96asr A100 v4, the Azure Machine Learning pricing page displays $19,853.810 per month Pay-As-You-Go, $16,512.42 per month for a one-year plan, and $9,946.77 per month for a three-year plan. These are the page’s displayed monthly figures. They do not by themselves show a break-even point: a buyer needs the exact commitment terms and expected paid usage to determine whether a longer plan fits.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How do I build a reliable three-cloud GPU quote?
Choose one workload, one region, and one purchase model first. Then request or calculate the full VM cost for each provider, keeping platform fees and ancillary charges visible instead of silently folding them into only one provider’s total.
- Describe the workload. Note whether it is training, inference, HPC, rendering, or visualization; whether it can be interrupted; and how many hours it is expected to run. Those facts help select an appropriate family and purchasing path.
- Choose a region and currency. Use the same geography for each quote where the required GPU is available. If a matching GPU is unavailable in one region, label the exception instead of treating another region’s price as directly comparable.
- Match the accelerator and VM shape. Record GPU model and count, vCPU, RAM, and machine type. Compare the actual configuration needed, not a GPU model name on its own.
- Set the purchase terms. Compare on-demand with on-demand first. Make separate comparisons for Spot or commitment plans, recording interruption conditions or term requirements alongside the price.
- Define what is included. Separate VM compute, attached GPU charges, managed-platform charges, storage, networking, and any other required software. The Databricks example demonstrates why managed-service totals must be identified explicitly.
- Estimate workload cost. Multiply the quoted hourly total by the expected billable hours for the chosen usage pattern, then account for separately priced storage, network, and platform services. For a commitment, compare the expected utilization and total term cost with the flexible alternative rather than relying on a headline monthly discount.
- Save the quote context. Record the source page, date checked, region, currency, machine type, GPU count, billing model, and included charges. Recheck dynamic pricing immediately before a purchase decision.
| Quote field | What to record |
|---|---|
| Provider and region | Provider name, exact region, and currency |
| GPU and machine | GPU model and count; VM/machine type; vCPU and RAM |
| Hourly totals | Full on-demand hourly total; Spot hourly total and interruption terms, if available |
| Commitment | Plan or reservation type, term, and displayed price |
| Scope and provenance | Storage, networking, and software charges; managed-service fee inclusion; source and date checked |
What should I take away from the available prices?
The examples are useful for understanding pricing structure, not for declaring a winner. Azure’s published examples show how a displayed plan or managed-service total may differ in scope; Google Cloud’s Spot example illustrates a whole-VM Spot price; AWS’s listed P5 and P5e configurations identify hardware options but do not supply a comparable rate. A buyer should make the decision from matched full-VM quotes, with purchase terms and add-on costs made explicit.
Frequently Asked Questions
How do I convert a monthly GPU quote into an hourly comparison?
Use the provider’s stated billing convention or the quote’s own hourly equivalent; do not assume a month contains a fixed number of billable hours. If the quote does not disclose that convention, ask the provider or use the documented billing calculator before comparing it with an hourly price.
Can I use one cloud’s GPU price to estimate another cloud’s bill?
Not reliably from the GPU model alone. Machine shape, regional availability, billing terms, and included services can differ, so treat another provider’s price as a prompt to obtain a quote rather than as a cost estimate.
Should I include storage and data transfer in my GPU budget?
Yes, if the workload requires them. Keep those line items separate from compute so the comparison shows both the VM cost and the broader cost of running the workload.
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.
