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Compare complete, equivalent systems—not isolated GPU-hour prices. Match the accelerator (H100 with H100, H200 with H200), GPU count, memory, host CPU and RAM, local and remote storage, interconnect, region, billing model, data movement and orchestration. Then measure the cost and elapsed time of a completed training or inference job. A cheaper hourly GPU can lose once queue time, idle capacity, storage, egress and engineering work are included.
What a fair AI-GPU cloud comparison includes
An AI workload runs on a system, not on a GPU SKU in isolation. Before requesting prices, write down the configuration your job actually needs:
- Accelerator model and quantity: for example, eight H100s or eight H200s.
- GPU memory type and capacity, such as HBM3 or HBM3e.
- GPU-to-GPU topology: NVLink, NVSwitch or an equivalent fabric.
- Host CPU count, system memory and local SSD capacity.
- Storage for datasets, checkpoints, logs and container images.
- Inter-node network technology, bandwidth and collective-communication support.
- Region, availability zone, quota and any reservation requirement.
- Billing model: on-demand, committed or reserved capacity, and interruptible spot capacity.
- Data-transfer charges, orchestration services and the labor required to operate the cluster.
Record these inputs in a comparison sheet. If a provider’s page describes a maximum, treat it as a capability rather than a promise that every region or account can obtain it.
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Documented GPU configurations by provider
The following specifications come from each provider’s product documentation. They describe published configurations, not a cross-cloud performance test.
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| Provider and family | Published configuration | GPU memory | Networking or scale-out detail |
|---|---|---|---|
| AWS EC2 P5 | Up to eight NVIDIA H100 GPUs per instance | Up to 640 GB HBM3 per instance | Up to 3,200 Gbps EFA; 900 GB/s NVSwitch peer-to-peer bandwidth |
| AWS EC2 P5e/P5en | Up to eight NVIDIA H200 GPUs per instance | Up to 1,128 GB HBM3e per instance | Up to 3,200 Gbps EFA; UltraClusters can scale to as many as 20,000 H100 or H200 GPUs |
| Azure ND H100 v5 | Eight NVIDIA H100 80 GB GPUs; 96 vCPUs | 1,900 GiB system memory in addition to GPU memory | NVLink 4.0 and 3.2 Tbps scale-out InfiniBand; deployments can scale to thousands of GPUs |
| Google Cloud A3 Mega | Eight H100 GPUs | 640 GB HBM3 | Availability and reservation requirements vary by region and machine family |
| Google Cloud A3 Ultra | Eight H200 GPUs | 1,128 GB HBM3e | Availability and reservation requirements vary by region and machine family |
| Google Cloud A4 | Eight B200 GPUs | 1,440 GB HBM3e | Region-specific availability; newer A4X systems target GB200/GB300-class configurations |
AWS describes P5, P5e and P5en as instances for deep learning, generative AI and HPC. Azure positions ND H100 v5 for high-end deep-learning training, tightly coupled generative-AI workloads and HPC, with NCCL-based tools supported. Google Cloud’s A3 and A4 documentation covers H100, H200 and B200 accelerators, while some newer families require reservations. CoreWeave’s catalog includes NVIDIA GB200 NVL72, HGX H200, L40 and L40S, and A100 systems.
CoreWeave price examples
CoreWeave’s pricing page lists on-demand and spot instances with GPU count, VRAM, vCPUs, system RAM, local storage and hourly prices. In its North America table accessed on October 19, 2026, an eight-GPU HGX H200 configuration is listed at $50.44 per hour on demand, and an eight-GPU L40S configuration at $18.00 per hour on demand. Those are regional, date-sensitive figures; they are not universal prices and should not be compared with another provider until the region, billing term and complete machine configuration match.
Step 1: Match the accelerator and topology
Start with the hardware that determines whether your model fits and how quickly GPUs can exchange tensors.
Match GPU model and count
Compare H100 against H100 and H200 against H200. Do not use an H100 hourly rate as a proxy for an H200 workload. Keep the GPU count equal when comparing a single-node job. If one offer uses eight GPUs and another uses four, normalize by the throughput and elapsed time of the completed job rather than by the nominal hourly rate.
Check memory capacity and type
GPU memory can decide whether a model runs without sharding, offload or aggressive quantization. AWS P5 documentation specifies up to 640 GB of HBM3 across eight H100 GPUs. AWS P5e/P5en and Google Cloud A3 Ultra specify up to 1,128 GB of HBM3e across eight H200 GPUs. Google Cloud’s eight-B200 A4 configuration is documented with 1,440 GB of HBM3e. These capacities are per published instance and do not describe usable memory after the framework, communication buffers and CUDA runtime reserve space.
Record intra-node topology
For tensor or pipeline parallelism, note whether the GPUs share NVLink or NVSwitch and what peer-to-peer bandwidth is documented. AWS lists 900 GB/s NVSwitch peer-to-peer bandwidth for P5-family systems. Azure ND H100 v5 uses NVLink 4.0. A topology with fast GPU-to-GPU paths can reduce synchronization time even when two systems contain the same GPU model.
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Record host resources
Capture vCPUs, system RAM, local NVMe or SSD capacity and the storage path used for training data. Azure ND H100 v5 documents 96 vCPUs and 1,900 GiB of system memory. CoreWeave’s pricing listings expose vCPUs, system RAM and local storage for each listed configuration. The AWS and Google Cloud pages cited here establish GPU and network capabilities but do not provide a single comparable host-memory figure for every family; obtain the exact machine-type specification before pricing a job.
Step 2: Calculate completed-job cost
The useful number is the cost to finish a defined run, not the advertised GPU-hour:
Completed-job cost = infrastructure runtime + attached storage + data transfer + orchestration + checkpoint and recovery overhead + idle time + engineering labor.
Infrastructure runtime
Multiply the hourly rate for the complete node or instance by the time the resource is allocated. Include boot, image-pull, data-staging and teardown time if billing continues during those phases. Keep on-demand, committed or reserved pricing and spot pricing in separate scenarios. A spot rate may look attractive but can increase total cost if interruptions force checkpoint reloads or extend wall-clock time.
Storage and checkpointing
Count persistent datasets, local scratch space, object-storage requests and checkpoint retention. A training job that writes frequent multi-gigabyte checkpoints may spend substantial time and money moving data even when GPU utilization appears high. Price the storage duration, not just the amount written during one run.
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Locate the dataset, container registry, experiment tracker and serving endpoint before selecting a region. Moving data across regions or providers can erase a GPU-rate advantage. Measure both one-time staging and recurring transfers for every run. Keep the training data near the GPUs where policy and latency permit.
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Idle and queue time
Include time spent waiting for capacity, reserving nodes, debugging launch failures and leaving instances running between experiments. If a lower-price SKU is unavailable during your required window, its effective cost is the cost of the alternative plus the value of the delay.
Engineering overhead
Account for cluster setup, identity and network configuration, monitoring, checkpoint recovery, driver or container changes and job scheduling. A managed workflow can cost more per hour while reducing the engineering time needed to keep utilization high.
Step 3: Check capacity before choosing a price
GPU availability changes by region, model and account quota. Before committing to a benchmark or production schedule, confirm all of the following with the exact SKU:
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- Account quota and whether a quota increase is required.
- Reservation or capacity commitment requirements.
- Current on-demand and spot availability.
- Maximum cluster size and the lead time for a multi-node allocation.
- Interruption policy for spot capacity and the provider’s recommended checkpoint strategy.
Google Cloud explicitly documents region-specific availability and reservation requirements for some A3, A4 and newer A4X families. CoreWeave’s published prices are also region- and date-sensitive. Treat an online price as a current reference, then verify that your account can obtain the quantity at the required time.
Step 4: Evaluate distributed-training networking
Multi-node training can be network-bound. Compare the complete path used by the collective library, not only the headline link speed.
AWS EFA and UltraClusters
AWS documents up to 3,200 Gbps Elastic Fabric Adapter networking for P5, P5e and P5en. P5-family systems also use NVSwitch inside the node, and AWS documents UltraClusters scaling to as many as 20,000 H100 or H200 GPUs. Verify the exact instance size, EFA placement and software configuration for your cluster; a maximum documented scale does not guarantee immediate capacity.
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Azure InfiniBand
Azure ND H100 v5 documents 3.2 Tbps scale-out InfiniBand, NVLink 4.0 and support for NCCL-based tools. This combination targets tightly coupled training where all-reduce and other collectives occur frequently. Confirm that the selected VM deployment exposes the intended InfiniBand topology and that your container image includes compatible drivers and NCCL versions.
Google Cloud networking and AI Hypercomputer integration
Google Cloud’s A3 and A4 machine families are available through Compute Engine, with integrations that also extend to GKE and AI Hypercomputer. Compare the machine type’s interconnect, placement and reservation terms with the topology expected by your framework. The same GPU model can show different scaling efficiency when placement or network contention changes.
CoreWeave networking and Kubernetes-style operation
CoreWeave is organized around GPU cloud and Kubernetes-style AI infrastructure. For a distributed job, confirm the node topology, fabric, scheduler behavior, NCCL support and storage path in the specific cluster you will use. Do not infer multi-node performance from a single-GPU listing.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Step 5: Compare the surrounding platform
Choose AWS when its existing platform reduces integration work
AWS combines EC2 P5-family instances with EFA, FSx for Lustre and managed options such as SageMaker. This can be valuable when your data, identity, observability and deployment pipelines already use AWS. Price the services and data paths that your workflow actually consumes rather than assuming that a GPU instance is the whole bill.
Choose Azure when Microsoft integration and InfiniBand matter
Azure ND H100 v5 is designed for high-end training and HPC, and Azure integrates GPU VMs with Microsoft identity, networking and AI services. Teams already standardized on those controls may reduce operational work, but should still validate quota, VM availability and the exact network placement.
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Google Cloud provides A3 H100 and H200 machines and A4 B200 machines through Compute Engine, with GKE and AI Hypercomputer integration. Reservation rules and regional supply are important inputs, especially for newer A4 and A4X systems.
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Choose CoreWeave when GPU-focused infrastructure and transparent SKU pricing fit
CoreWeave exposes a broad catalog and on-demand or spot pricing details for GPU configurations, including GPU count, VRAM, CPU, RAM and local storage. That transparency can simplify initial cost modeling. Confirm capacity, network topology, storage performance and Kubernetes operations for the region and cluster you need.
Step 6: Benchmark your actual workload
Vendor peak specifications are not a cross-cloud benchmark. Run the same software and data path on each candidate, then record:
- Tokens per second for language-model training or inference.
- Samples or sequences per second for the target model.
- Time to reach a defined validation loss or quality threshold.
- GPU utilization, memory utilization and communication overhead.
- Inference latency at the concurrency and batch size you expect.
- Checkpoint duration, restart time and recovery success after a simulated failure.
- Queue time and the percentage of allocated time spent doing useful work.
Make the benchmark reproducible
- Pin the model revision, dataset version, container image, CUDA and framework versions.
- Use the same global batch size, precision, sequence length, optimizer settings and number of training steps.
- Warm the data path and run enough steps to exclude one-time image and cache effects.
- Measure at least one single-node configuration and the multi-node size you expect to operate.
- Repeat the run to expose capacity variation, startup delays and transient failures.
- Convert results to cost per million tokens, cost per training step or cost to reach the target metric.
Decision guide by workload
Single-node fine-tuning
Prioritize GPU memory, local storage, startup time and an environment that your team can launch repeatedly. An eight-GPU H100 or H200 node may be unnecessary if the model fits on fewer GPUs; compare the smallest configuration that meets memory and throughput requirements. Include data staging and checkpoint time in the cost.
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Large language-model pretraining
Prioritize memory capacity, NVLink or NVSwitch, scale-out fabric, NCCL behavior, checkpoint bandwidth and the ability to obtain many identical nodes at once. AWS P5/P5e/P5en, Azure ND H100 v5 and Google A3 systems all publish high-bandwidth multi-GPU configurations, but your benchmark must establish scaling efficiency and real capacity.
Batch inference
Prioritize cost per generated token, sustained utilization, model-loading time, latency at production concurrency and autoscaling behavior. L40S-class configurations can be relevant for workloads that do not require H100 or H200 memory, but compare quality, throughput and memory headroom with the same model and precision.
Interactive or bursty experimentation
Prioritize immediate capacity, fast provisioning and the ability to stop resources cleanly. Spot capacity can reduce the rate, but only if your checkpoints and restart automation prevent interruptions from dominating elapsed time.
Common comparison mistakes and fixes
- Comparing different GPUs: normalize model, count and memory before looking at price.
- Using a per-GPU price for a full-node requirement: include host CPU, RAM, storage and network charges.
- Ignoring regional supply: verify quota, reservations and live capacity for the exact region.
- Treating peak bandwidth as measured performance: benchmark NCCL collectives and model throughput.
- Leaving egress out of the budget: map every dataset, registry, checkpoint and endpoint transfer.
- Using spot for an uncheckpointed job: automate frequent checkpoints and recovery before relying on interruptions being harmless.
- Assuming platform familiarity is free: include migration, identity, Kubernetes, monitoring and scheduler engineering time.
- Choosing a newer GPU without checking software: validate driver, CUDA, framework and kernel support in the exact image.
A practical comparison worksheet
For each candidate, create one row containing the exact provider, region, machine type, GPU model and count, total GPU memory, CPU and RAM, local storage, remote storage path, intra-node topology, inter-node fabric, billing term, hourly rate, expected utilization, queue time, transfer volume, checkpoint policy and measured throughput. Add calculated columns for runtime, infrastructure cost, storage, transfer, recovery overhead and total cost per completed job.
Use separate rows for on-demand, committed or reserved and spot scenarios. Mark figures with their date and region. CoreWeave’s $50.44 eight-GPU HGX H200 example and $18.00 eight-GPU L40S example are North America on-demand listings accessed October 19, 2026; they should remain labeled that way in your worksheet. AWS, Azure and Google Cloud figures cited above are documented capabilities, not quoted hourly rates.
Bottom-line selection criteria
No provider wins every AI-GPU workload. Select the option that can supply the required accelerator and topology in your region, at the time you need it, and that finishes your measured job at the lowest total cost with acceptable operational risk. Use published specifications to narrow the field, capacity checks to remove unavailable choices, and a controlled benchmark to make the final decision.
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.
