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For raw, on-demand GPU rental, GPU-focused providers are usually cheapest in the current published examples. A RunPod H100 SXM is listed at $3.99 per GPU-hour, compared with $6.88 on AWS, $12.29 on Azure and $11.06 on Google Cloud in a US Linux comparison read October 15, 2026. CoreWeave lists an eight-GPU H100 instance at $6.155 per GPU-hour on demand and about $2.464 on spot. Those are price-sheet comparisons, not proof that one provider completes every training or inference job more cheaply. Accelerator variant, region, interruptions, host resources, storage, networking, commitments and the value of your existing cloud integration determine the real bill.
What is the difference between a GPU-focused cloud and a hyperscaler?
A GPU-focused cloud primarily sells access to accelerators and the surrounding infrastructure. AWS, Microsoft Azure and Google Cloud sell GPUs as one component of a much broader cloud platform that also includes identity, networking, storage, databases, security controls and managed data services.
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“Cheapest” therefore has two meanings:
- Lowest accelerator-hour price: the amount shown for one GPU-hour, before the rest of the instance and workload bill.
- Lowest cost for a finished workload: GPU time plus host CPU and memory, disks, object storage, network transfer, orchestration, idle capacity, interruptions and engineering effort.
The prices below answer the first question. The decision framework later in this guide addresses the second.
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How much does each provider charge for the same GPU?
The clearest like-for-like snapshot is a US Linux on-demand comparison from CloudRift, read October 15, 2026. It divides multi-GPU instance prices by GPU count and excludes commitments, so it should be treated as a reference point rather than a guaranteed quote.
| GPU | AWS | Azure | Google Cloud | RunPod |
|---|---|---|---|---|
| H100 | $6.88/GPU-hour | $12.29/GPU-hour | $11.06/GPU-hour | $3.99/GPU-hour |
| H200 | $7.91/GPU-hour | $10.60/GPU-hour | $10.60/GPU-hour | $5.29/GPU-hour |
| B200 | $14.24/GPU-hour | $27.04/GPU-hour | $8.06/GPU-hour | $7.99/GPU-hour |
| A100 80GB | $3.43/GPU-hour | $4.10/GPU-hour | $5.07/GPU-hour | $1.79/GPU-hour |
CloudRift’s comparison is specifically a US Linux, on-demand snapshot. Prices, availability and instance configurations can change, and the table does not include commitments or non-GPU charges.
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RunPod’s pricing page, updated October 8, 2026, lists the following Pod rates:
| GPU | Published price |
|---|---|
| H200 | $5.29/GPU-hour |
| B200 | $7.99/GPU-hour |
| H100 SXM | $3.99/GPU-hour |
| H100 PCIe | $2.89/GPU-hour |
| A100 PCIe and SXM | $1.79/GPU-hour |
| L40S | $1.09/GPU-hour |
These are dedicated GPU Pod prices, not a universal RunPod rate for every product. The provider says Pods are dedicated GPU instances, Serverless is priced separately for inference workloads, and Clusters are intended for multi-node jobs. Check the current RunPod pricing page for the region and product you will actually deploy.
CoreWeave’s eight-GPU instance rates
CoreWeave publishes prices for complete eight-GPU instances. Dividing each instance price by eight gives an approximate per-GPU figure, but your bill is for the instance and its attached resources, not for an abstract GPU-hour.
| Instance | On-demand, 8 GPUs | Approx. on-demand per GPU-hour | Spot, 8 GPUs | Approx. spot per GPU-hour |
|---|---|---|---|---|
| HGX H100 | $49.24/hour | $6.155 | $19.71/hour | $2.464 |
| HGX H200 | $50.44/hour | $6.305 | $20.93/hour | $2.616 |
| A100 | $21.60/hour | $2.700 | $9.65/hour | $1.206 |
| L40S | $18.00/hour | $2.250 | $7.88/hour | $0.985 |
The figures come from CoreWeave’s North America pricing table. An H100 spot instance is about 60% below its on-demand instance price, but spot capacity can be reclaimed. See the CoreWeave pricing page for current regions, machine configurations and terms.
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What does Google Cloud publish?
Google Cloud lists GPU charges separately from the virtual machine. Its published examples include a T4 at $0.35 per GPU-hour, a V100 at $2.48 and a P100 at $1.46. Each GPU is added to the cost of the machine type, and the page says those figures do not include disk, images, networking or the VM-instance charge.
| GPU example | Published GPU price | What remains outside that number |
|---|---|---|
| T4 | $0.35/GPU-hour | VM machine type, disk, images, networking and other applicable charges |
| V100 | $2.48/GPU-hour | VM machine type, disk, images, networking and other applicable charges |
| P100 | $1.46/GPU-hour | VM machine type, disk, images, networking and other applicable charges |
Google also documents sustained-use discounts, resource-based committed-use discounts and separate Spot GPU pricing. Spot prices are dynamic and can change up to once every 30 days. Consult the Google Cloud GPU pricing page and the provider’s calculator for your region and machine type.
Is RunPod cheaper than AWS for an H100?
On the cited US Linux on-demand snapshot, yes: RunPod’s H100 SXM is $3.99 per GPU-hour versus $6.88 on AWS, while RunPod’s H100 PCIe is listed at $2.89. The comparison is meaningful only when the accelerator variant, region, billing mode and included host resources match.
For 100 GPU-hours, the published GPU line items alone would be approximately $399 on RunPod H100 SXM and $688 on AWS. That arithmetic excludes CPU and system memory, disks, data transfer, orchestration, taxes and any discounts or commitments.
Do not treat H100 SXM and H100 PCIe as interchangeable labels. Memory configuration, interconnect and the resulting throughput can change how long a training or inference job runs. A lower hourly price can cost more overall if the slower or less suitable configuration takes substantially longer to finish.
What is the cheapest cloud for training an AI model?
There is no universal winner for a completed training run. The lowest raw rate in the cited examples belongs to a GPU-focused provider, but total training cost depends on how many GPUs you need, how efficiently the model uses them, whether the job tolerates interruption and where the data already resides.
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| 100 GPU-hours, on-demand snapshot | Approximate GPU-only cost |
|---|---|
| H100 — RunPod | $399 at $3.99/hour |
| H100 — AWS | $688 at $6.88/hour |
| H100 — Azure | $1,229 at $12.29/hour |
| H100 — Google Cloud | $1,106 at $11.06/hour |
| H200 — RunPod | $529 at $5.29/hour |
| H200 — AWS | $791 at $7.91/hour |
| H200 — Azure | $1,060 at $10.60/hour |
| H200 — Google Cloud | $1,060 at $10.60/hour |
| B200 — RunPod | $799 at $7.99/hour |
| B200 — AWS | $1,424 at $14.24/hour |
| B200 — Azure | $2,704 at $27.04/hour |
| B200 — Google Cloud | $806 at $8.06/hour |
| A100 80GB — RunPod | $179 at $1.79/hour |
| A100 80GB — AWS | $343 at $3.43/hour |
| A100 80GB — Azure | $410 at $4.10/hour |
| A100 80GB — Google Cloud | $507 at $5.07/hour |
These calculations use the CloudRift snapshot and are not estimates of a finished model run. They omit every non-GPU charge and assume exactly 100 billed GPU-hours, so they cannot account for different throughput, scaling efficiency or interruptions.
When a hyperscaler can still be cheaper overall
- Your data is already there. Keeping training data, checkpoints and production services in one cloud can avoid transfer charges and data-copy time.
- You need integrated controls. Existing identity, private networking, audit, security and compliance tooling can reduce the engineering work required to operate a separate GPU environment.
- You have discounts. Reserved capacity, committed-use discounts and sustained-use discounts can materially change the effective rate. Compare the discounted price for your expected utilization rather than the public on-demand figure.
- You need a particular managed service. A higher GPU line item may be offset by managed storage, orchestration or deployment features that you would otherwise have to build and maintain.
- The cheaper card is unavailable. Waiting for capacity, moving regions or redesigning a job can erase a nominal hourly advantage.
Should I use CoreWeave or Google Cloud for GPU workloads?
Choose between them by comparing the complete instance and operating model, not just the GPU column. CoreWeave’s published eight-GPU H100 on-demand price is $49.24 per hour, while Google Cloud’s GPU page adds each GPU to a separately priced VM. The two prices are not directly comparable until you select Google’s machine type, region, disk and network configuration.
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|---|---|---|
| Published pricing model | Complete GPU instance prices, including eight-GPU HGX examples | GPU line item added to VM machine-type cost |
| Spot/preemptible option | Published spot rates for the listed instances | Separate Spot GPU prices; dynamic and subject to Google’s Spot rules |
| Best fit | Dedicated multi-GPU capacity when the required configuration is available | Workloads already integrated with Google Cloud storage, identity, networking or managed services |
| Main comparison risk | Comparing an eight-GPU instance with a single-GPU quote | Forgetting VM, disk, image, networking and region charges |
For a distributed job, also verify the interconnect and topology your framework expects. A nominally lower per-GPU rate does not help if communication overhead reduces scaling efficiency.
Are spot GPUs reliable enough for training?
Spot GPUs are suitable for interruption-tolerant work, but they are not equivalent to guaranteed capacity. A provider can reclaim a spot instance, forcing the job to stop or restart. CoreWeave’s H100 example illustrates the trade-off: approximately $2.464 per GPU-hour on spot versus $6.155 on demand.
Use spot for these workloads
- Experiments that can restart from a recent checkpoint.
- Hyperparameter sweeps with many independent trials.
- Batch inference or preprocessing that can be placed back in a queue.
- Distributed training designed to tolerate worker loss and retry.
Prefer on-demand for these workloads
- Jobs with no reliable checkpoint or recovery process.
- Interactive sessions where a sudden reclaim directly blocks a team.
- Time-critical launches with a fixed completion deadline.
- Stateful services that cannot be drained or restarted safely.
How to make spot training recoverable
- Write model, optimizer and scheduler state to durable storage at a defined interval; saving only final weights is not enough.
- Record the exact code revision, container image, dataset version and random seeds with each checkpoint.
- Make startup automatically discover the newest valid checkpoint and resume rather than begin at epoch zero.
- Use a queue or scheduler that requests replacement capacity and removes failed workers cleanly.
- Budget extra wall-clock time for requeues and validate recovery on a small run before committing a large job.
How do I compare GPU clouds accurately?
Use the following sequence before choosing a provider. It prevents the most common mistake: selecting the lowest displayed GPU-hour and discovering later that the configuration cannot run the job or costs more after ancillary charges.
- Define the workload. Record model size, framework, precision, dataset location, expected GPU count, target completion time, checkpoint frequency and whether interruption is acceptable.
- Normalize the accelerator. Match the exact model and variant—such as H100 SXM versus H100 PCIe—and confirm memory, interconnect and topology. Do not combine unlike cards in one price table.
- Choose the billing mode. Compare on-demand with spot, reserved capacity, committed-use and sustained-use pricing. Keep a flexible on-demand fallback if spot capacity is essential to your schedule.
- Price the whole instance. Add vCPUs, host memory, boot and attached disks, operating-system images, local scratch space and any minimum instance size required by the GPU.
- Add data movement. Include object-storage requests, transfers between zones or regions, ingress and egress, checkpoint writes and the cost of copying a dataset to a new provider.
- Check the region and supply. Verify that the exact GPU is offered in the region where your data and users are located. A cheaper distant region can add latency, transfer cost or operational complexity.
- Estimate utilization. Multiply the hourly rate by billed hours, including idle time while waiting for data, compiling kernels, loading checkpoints or reserving capacity. A low rate with low utilization may cost more than a higher rate on a consistently busy instance.
- Run a representative pilot. Measure samples per second, memory headroom, scaling efficiency, restart behavior and total ancillary charges. No published price table establishes a universal cost-per-training-run or cost-per-token winner.
A practical total-cost formula
For a first-pass estimate, use:
Total cost = (GPU rate × number of GPUs × billed hours) + VM/CPU/RAM charges + storage + network transfer + orchestration and service charges + expected interruption/restart cost.
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For an eight-GPU CoreWeave H100 on-demand job running 20 hours, the GPU instance component is $984.80 (20 × $49.24), before any separately applicable storage, networking or other charges. The same multiplication should be performed on the actual Google Cloud VM configuration rather than on the GPU line alone.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What hidden costs most often erase a low GPU rate?
Host resources
GPU workloads still need CPU cores, system RAM, boot volumes and scratch disks. Google explicitly separates the GPU charge from the machine-type cost; a comparison that omits host resources is incomplete.
Storage and checkpoints
Large datasets and frequent checkpoints generate both capacity and request costs. Spot training can increase checkpoint frequency, so include the storage footprint and write volume in the estimate.
Networking and egress
Moving a dataset into a new region or exporting completed results can cost more than expected. Keep data and compute close when possible, and price the transfers required for every training cycle, not just the initial upload.
Idle and failed capacity
Billing generally follows allocated time, not the percentage of time your code is doing useful tensor work. Include startup, synchronization, data-loader stalls, failed launches and requeued spot jobs in billed hours.
Commitment risk
Committed or reserved capacity can lower an effective rate when utilization is predictable, but an unused commitment is not a saving. Compare the break-even utilization with your forecast and retain an on-demand path for unexpected demand.
Which provider should you choose?
| Your priority | Most sensible starting point | Why |
|---|---|---|
| Lowest published on-demand GPU-hour | GPU-focused provider such as RunPod | The cited H100, H200, B200 and A100 examples are below the corresponding hyperscaler snapshot prices. |
| Large, multi-GPU nodes with an interruption-tolerant job | CoreWeave spot, if capacity and topology fit | Its published eight-GPU spot rates are substantially below its on-demand rates. |
| Existing data, identity and production stack on one cloud | The hyperscaler already hosting that stack | Integration, transfer and operations savings can outweigh a higher GPU line item. |
| Predictable, sustained utilization | Any provider after pricing commitments | Reserved, committed-use or sustained-use discounts can change the effective rate. |
| Strict deadline or no restart capability | On-demand capacity with verified availability | Predictability is worth more than a spot discount when interruption is unacceptable. |
| Uncertain demand or short experiments | Flexible on-demand GPU rental | Avoids a long commitment while you measure real throughput and utilization. |
Start with the provider that minimizes your complete, measured workload cost. Use the published GPU-hour tables to shortlist options, then validate the exact model, region, instance and billing mode with a representative run.
What should I check before placing a large order?
- Is the quoted price for the exact accelerator variant and memory configuration?
- Is it per GPU or for a complete multi-GPU instance?
- Does the quote say on-demand, spot, preemptible, reserved or committed?
- Which region and operating system does the price cover?
- Are CPU, RAM, local disks, images, storage and networking included?
- What happens when capacity is unavailable or a spot job is reclaimed?
- Can your framework use the available interconnect and topology efficiently?
- Where will checkpoints live, and what will recovery cost after an interruption?
- Do existing commitments or sustained-use rules change your effective hyperscaler rate?
- Have you measured throughput and total bill on the exact model rather than comparing card names alone?
Frequently Asked Questions
Can I combine GPUs from different providers in one training run?
Usually not without substantial engineering and a network design that supports cross-provider communication. Most distributed training jobs keep workers in one provider and region, then use another provider for burst capacity, preprocessing or inference rather than mixing GPUs inside a single tightly synchronized job.
How often should I recheck these hourly prices?
Recheck immediately before a purchase and whenever you change region, GPU model or billing mode. Google states that Spot prices can change up to once every 30 days, while the cited RunPod list was updated October 8, 2026 and the cross-provider snapshot was read October 15, 2026.
What is the safest way to move a workload from a hyperscaler to a GPU specialist?
Keep the container image, infrastructure definitions and checkpoints portable, then copy a representative dataset subset and run a small validation job. Measure data-transfer cost, startup time, throughput, failure recovery and security controls before moving production data or signing a capacity commitment.
Does a lower GPU-hour rate guarantee a lower cost per token?
No. Cost per token also depends on throughput, batching, precision, memory limits, interconnect performance, utilization and time spent loading data or serving requests. Benchmark the same model and serving configuration on each candidate rather than inferring output cost from the hourly rate.
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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