There is no universal cheapest or most reliable GPU cloud. CoreWeave publishes attractive regional spot and on-demand rates, AWS offers the broadest documented P5 portfolio and Capacity Blocks, Google Cloud exposes detailed A3 H100 spot prices, and Azure ND H100 v5 is a tightly integrated eight-GPU system whose final price is calculator- and scope-dependent. A fair decision requires matching GPU generation, memory, host resources, region, billing term, interruption policy, storage, egress and quota—not comparing a headline hourly number.
What the published numbers actually show
The figures below are the current examples available for 2026. They are not a provider-wide price ranking: each uses different machine shapes, billing units, regions and reservation rules.
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| Provider and product | Published rate | Billing context | What is and is not comparable |
|---|---|---|---|
| CoreWeave L40 | $10.00 per hour on-demand; $6.27 per hour spot | CoreWeave North America regional table, 2026 | Configuration shown in CoreWeave’s table; confirm whether your quote is per GPU or per node. |
| CoreWeave L40S | $18.00 per hour on-demand; $7.88 per hour spot | CoreWeave North America regional table, 2026 | Different GPU generation and memory profile from H100; not an H100 substitute for every workload. |
| CoreWeave H200 | $50.44 per hour on-demand; $20.93 per hour spot | CoreWeave North America regional table, 2026 | H200 is a newer model than H100, so a raw hourly comparison with an H100 is misleading. |
| CoreWeave A100 | $21.60 per hour on-demand; $9.65 per hour spot | CoreWeave North America regional table, 2026 | A100 capacity, memory and interconnect must be matched to the competing shape. |
| AWS EC2 P5.48xlarge | $41.528 per instance-hour, equivalent to $5.191 per H100 accelerator-hour | Capacity Block in the US East (Atlanta) Local Zone; rate fixed when purchased | This is a reservation product with a defined term and region, not ordinary pay-as-you-go pricing. |
| Google Cloud A3 High, eight H100s | $88.490000119 per hour on-demand; $50.458920548 per hour spot | Displayed accelerator-optimized pricing, 2026 | Machine-family and VM configuration are part of the total cost. |
| Google Cloud a3-highgpu-1g | $6.307365068 per hour spot | One-H100 A3 Spot shape in the displayed regional data | Spot price is region-specific and interruption-prone; it is not an on-demand quote. |
| Google Cloud a3-highgpu-8g | $50.458920548 per hour spot | Eight-H100 A3 Spot shape in the displayed regional data | Use the exact region and VM configuration when reproducing the estimate. |
| Azure ND H100 v5 | Not stated as a single universal rate | Microsoft directs buyers to its pricing calculator; rates vary by scope and deployment | The SKU specification is public, but a valid comparison needs your region, term, operating system and associated services. |
CoreWeave’s North America table does not provide a directly comparable H100 line in these published examples. It therefore cannot support the claim that CoreWeave is cheaper than AWS for H100s. Its H200, A100, L40 or L40S rates answer different hardware questions. CoreWeave also marks some current-generation systems, including GB300 and HGX B200 entries, as “Contact sales,” which means public list-price comparisons are incomplete for those systems.
AWS’s $5.191 figure is an arithmetic conversion of one P5.48xlarge Capacity Block instance-hour divided by its eight H100 accelerators. The $41.528 instance rate is fixed at purchase for that block, but the term, minimum commitment, Atlanta Local Zone and capacity-block conditions remain part of the deal. It should not be presented as a universally available hourly P5 price.
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Google’s A3 figures illustrate why the machine shape matters. The eight-H100 on-demand value includes the A3 High context, while the one- and eight-GPU Spot values identify specific A3 shapes. Google explicitly says to add the VM machine-type configuration when calculating total cost, so the accelerator number alone is not an all-in rate.
Match the hardware before comparing a GPU hour
Start with the workload’s actual bottleneck. Two instances that each advertise “H100” can differ in GPU count, 80 GB memory configuration, CPU-to-GPU ratio, local NVMe, networking and placement. For training, interconnect topology and collective-communication performance can outweigh a small price difference. For inference, memory capacity, latency and the number of replicas may matter more than peak tensor throughput.
- GPU model and memory: Compare H100 with H100, H200 with H200, and the same memory tier. An L40S or A100 may be economical for a model that fits, but it is not a like-for-like H100 result.
- Billing unit: Record whether the quote is per accelerator, VM, bare-metal node or reserved block. Divide a node price by GPU count only when all GPUs are usable by your job.
- Host resources: Include vCPUs, system RAM, local NVMe and attached storage. A GPU waiting on data is still accruing GPU charges.
- Region: Keep the region constant where possible. Egress, storage rates, tax treatment and capacity differ by geography.
- Interconnect: Distributed training needs a documented fabric, topology and bandwidth, not just a GPU model name.
- Billing term and interruption policy: On-demand, spot, reserved and committed capacity have different prices and different operational risks.
Architecture and scale differences
| Provider | Relevant GPU systems | Networking and scale details | Planning implication |
|---|---|---|---|
| AWS | P5 supports one or eight H100 GPUs. P5e and P5en provide eight H200 GPUs. | Up to 3,200 Gbps Elastic Fabric Adapter networking and 900 GB/s NVSwitch peer-to-peer bandwidth. P5, P5e and P5en can run in UltraClusters scaling to as many as 20,000 H100 or H200 GPUs. | Useful for large, tightly coupled jobs, but placement, quota and the selected capacity product determine whether that scale is obtainable. |
| Azure | ND H100 v5 is an eight-H100 VM. | 96 vCPUs, 1,900 GiB memory and 3.2 Tbps interconnect bandwidth. | Budget the complete VM and deployment scope; the public SKU specification is not itself a final invoice. |
| Google Cloud | A3 High uses H100 80GB GPUs; one- and eight-GPU A3 shapes are shown in the Spot data. | Google presents accelerator-optimized pricing together with machine-family configuration. | Use the exact A3 machine type, region and VM configuration in the estimate. |
| CoreWeave | Published regional examples include L40, L40S, H200 and A100; some GB300 and HGX B200 systems are contact-sales. | Rates and availability are separated by region in CoreWeave’s tables. | Ask for the precise node shape, GPU count, interconnect, host resources and region in a quote; do not infer H100 economics from another model. |
Is CoreWeave cheaper than AWS for H100s?
Not from the published figures above. AWS supplies an explicit H100 accelerator-hour conversion for an Atlanta Capacity Block. CoreWeave’s cited North America table supplies no H100 rate, so there is no same-model, same-region, same-term comparison. A CoreWeave quote could be lower, higher or unavailable depending on region, block size and term.
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To answer the question for your project, request both providers’ quotes using one specification: eight H100 80GB GPUs (or one, if your job is single-GPU), the same region, identical term length, host CPU and RAM, local storage, network requirements and start date. Ask whether the price is per GPU or node and whether the capacity is guaranteed. Then add storage, data transfer and the cost of keeping a replacement capacity warm.
Availability: capacity is regional and product-specific
Availability is a live operational property rather than a permanent provider attribute. A cloud can have stock in one region and a long queue in another, or offer on-demand GPUs while withholding a large reserved block. CoreWeave publishes separate regional tables and labels some systems contact-sales. AWS publishes the regions and terms for Capacity Blocks. Google exposes region-specific A3 Spot tables. Azure documents the ND H100 v5 SKU but sends buyers to its calculator for an actual offer.
What to record during a capacity request
- Exact GPU SKU, memory size, GPU count and host shape.
- Region, zone or Local Zone and acceptable substitutes.
- Quota requested, quota granted and the time each change took.
- Whether the allocation is on-demand, spot, reserved, committed or a Capacity Block.
- Time from API request to a usable VM, including image boot and network attachment.
- Failure message, retry behavior and whether capacity disappeared between approval and provisioning.
How to run a practical availability test
- Define a two-week test window and the exact SLO: for example, eight GPUs provisioned within 15 minutes in two approved regions.
- Submit the same shape at fixed intervals without starting production workloads. Log successful, delayed and failed attempts separately.
- Measure quota lead time from request to approval, then provisioning time from approved request to a healthy node.
- Repeat with the intended on-demand or reserved product and with the spot product if interruption tolerance is part of the design.
- Record maintenance events, preemption notices, API errors, image failures, network attachment failures and storage-mount failures.
- Run a small representative training or inference job to verify that the allocated topology meets throughput and latency targets.
This produces evidence for your regions and SKU. It is more useful than a global “best availability” claim, which the available public material does not establish.
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Reliability: measure the workload, not a marketing label
None of the cited sources provides a common provider-wide GPU uptime statistic, so they cannot justify naming a single reliability winner. Reliability includes more than whether a VM stays running: quota approval, successful placement, network and storage dependencies, interruption handling, checkpoint speed, recovery automation and failover all affect the result.
| Reliability dimension | Questions to answer | Evidence to collect |
|---|---|---|
| Provisioning | Can the required shape be created within the SLO? | Success rate, queue time, API errors and quota lead time. |
| Runtime | How often are nodes interrupted or degraded? | Spot interruption frequency, maintenance notices, hardware errors and job loss. |
| Recovery | How quickly can a failed worker rejoin? | Checkpoint interval, restart time, automatic retry success and lost GPU-hours. |
| Network | Does the fabric sustain distributed-job performance? | Collective-operation throughput, packet errors, cross-zone behavior and congestion. |
| Storage | Can data and checkpoints be read and written at the required rate? | Mount success, throughput, latency, durability and restore time. |
| Failover | What happens when a region or provider is unavailable? | Multi-zone or multi-provider drill results, image portability and data-transfer time. |
| Support | Can a human resolve quota or capacity incidents? | Response time, escalation path and documented service commitments. |
Spot capacity requires an interruption budget
Spot can reduce the GPU line item substantially, but the discount is only real if the job survives interruptions. Design for preemption before choosing it: checkpoint to durable storage frequently, make workers replaceable, keep images and environments reproducible, and test resumption from the latest checkpoint. Calculate expected cost as:
effective GPU cost = paid GPU time + rework after interruption + idle time while replacing capacity + storage and transfer + engineering overhead
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A lower spot rate may lose to on-demand when checkpoints are infrequent, datasets must be reloaded across regions, or a failed eight-GPU job blocks a larger pipeline. Conversely, short, restartable inference batches can tolerate spot much better than a long synchronous training run.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Total cost of ownership beyond the accelerator
Use a common worksheet for every provider:
| Cost component | How to normalize it | Common omission |
|---|---|---|
| Accelerator or instance | Convert the quoted node or VM rate to the usable GPU count, preserving the billing term. | Treating a reserved Capacity Block as pay-as-you-go. |
| CPU, RAM and local NVMe | Include the host resources required by the job, even when bundled. | Comparing an eight-GPU host with a single-GPU VM. |
| Persistent and object storage | Price dataset capacity, checkpoint retention, IOPS and snapshot copies. | Ignoring storage while GPUs sit idle during data staging. |
| Data ingress and egress | Estimate bytes moved into the region, between zones and out to users or another cloud. | Assuming ingress is the only transfer that matters. |
| Interruption loss | Multiply interruption probability by recompute, reload and recovery time. | Using the spot discount without a restart model. |
| Operations | Include monitoring, schedulers, images, support and multi-cloud failover work. | Calling portability “free” when every provider needs a separate integration. |
For a fair monthly estimate, multiply the effective hourly cost by productive hours, not by every hour the VM exists. Also price idle reservations, warm standby capacity and the data-transfer path used during failover. Keep the date and region on every worksheet because public rates and capacity change.
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Choosing by workload
Large distributed training
Prioritize placement guarantees, interconnect bandwidth, synchronized scaling and checkpoint recovery. AWS documents P5 networking up to 3,200 Gbps EFA, 900 GB/s NVSwitch peer-to-peer bandwidth and UltraClusters of up to 20,000 H100 or H200 GPUs. Azure’s ND H100 v5 specifies a 3.2 Tbps interconnect for its eight-H100 VM. CoreWeave and Google Cloud should be evaluated with the same collective-communication benchmark and placement requirement; a lower list price is not useful if workers cannot be allocated together.
Single-GPU development and inference
Compare one-GPU shapes, memory headroom, startup time and regional latency. Google’s displayed a3-highgpu-1g Spot rate is $6.307365068 per hour, but it remains region-specific and interruptible. CoreWeave’s L40, L40S and A100 examples may be more appropriate than an H100 for models that fit their memory and performance envelope. Include model-loading time and the cost of keeping replicas warm.
Scheduled or predictable training runs
A Capacity Block or another committed product can make capacity and price predictable. AWS’s Atlanta P5.48xlarge example fixes the prevailing rate at purchase, but you must use the purchased term and location. Compare the value of that certainty with an on-demand or spot plan that can be moved between regions.
Production services with strict recovery targets
Use on-demand or reserved capacity for the baseline, then test a second region or provider for failover. Verify that images, drivers, container runtimes, checkpoints and network policies can be reproduced. A nominally cheaper provider is not a reliability improvement if recovery requires moving terabytes of data or rewriting deployment automation.
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- Write the workload contract: GPU model and memory, GPU count, throughput or latency target, checkpoint interval, maximum interruption recovery time and approved regions.
- Request itemized quotes from CoreWeave, AWS, Azure and Google Cloud for the same shape. Require GPU-versus-node billing, host specifications and all commitment terms in writing.
- Confirm quota and lead time before signing. Ask what happens if the requested region cannot supply the capacity on the start date.
- Run the availability and performance test, including a controlled interruption and restore from checkpoint.
- Price storage, egress, support, monitoring and failover. Keep one dated worksheet per provider and region.
- Choose the lowest-cost option that meets the workload SLO with an acceptable recovery path—not the lowest accelerator-hour number.
Bottom line: use a dated, workload-level comparison
CoreWeave can be compelling when its regional inventory and spot rates match your model, but the cited public table does not establish an H100 price. AWS offers the clearest documented H100 Capacity Block example and a broad P5/P5e/P5en architecture, with reservation and quota conditions that must be honored. Google Cloud provides granular A3 H100 spot and on-demand figures, while requiring the rest of the VM configuration in the total. Azure’s ND H100 v5 supplies a well-defined eight-GPU platform, but its final price is calculator- and deployment-dependent.
For production, collect provisioning, interruption, recovery, networking, storage and failover measurements in the target regions. Recalculate when rates, quotas or capacity change. That evidence, rather than a permanent provider ranking, tells you which GPU cloud is economical and reliable for your workload.
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