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The cheapest advertised GPU is rarely the cheapest completed job. Compare CoreWeave, AWS, Azure, and Google Cloud only after matching the accelerator, GPU count, memory, interconnect, region, billing term, storage, data movement, and utilization. An eight-GPU H100 node with fast scale-out networking can finish training sooner—and cost less overall—than a lower hourly rate attached to a slower or unavailable configuration.
The comparison that actually answers the cost question
Start with the workload, not the provider. Define the model, precision, batch size, sequence length, dataset location, checkpoint frequency, target completion time, and whether the job is training, fine-tuning, inference, or hyperparameter search. Then compare equivalent systems: H100 against H100, H200 against H200, and the same number of GPUs in each run.
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Record the complete machine rather than a GPU name alone. The relevant specification includes GPU memory, CPU cores, host RAM, local SSD or scratch storage, GPU-to-GPU links, node-to-node networking, supported collective-communication libraries, region, and capacity commitment. A GPU-hour price that excludes storage, egress, idle time or queue time is not a completed-job price.
Documented configurations at a glance
The following figures come from provider documentation available on October 19, 2026. Prices and capacity can change by date, region and commitment.
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| Provider and family | Documented accelerator configuration | Memory and interconnect details | Published price detail |
|---|---|---|---|
| CoreWeave HGX H200 | Eight H200 GPUs | Configuration listing includes GPU count, VRAM, vCPUs, system RAM and local storage; the cited pricing page does not establish one universal interconnect specification for every listing. | $50.44 per hour on demand in CoreWeave’s North America table, accessed October 19, 2026. |
| CoreWeave L40S | Eight L40S GPUs | Configuration listing includes GPU count, VRAM, vCPUs, system RAM and local storage. | $18.00 per hour in the same North America table; regional and time-sensitive. |
| AWS EC2 P5 | Up to eight NVIDIA H100 GPUs | Up to 640 GB total HBM3, up to 900 GB/s NVSwitch peer-to-peer bandwidth and up to 3,200 Gbps EFA networking. | Hourly price is not stated in the cited P5 product documentation; obtain the rate for the exact Region, tenancy and purchase model. |
| AWS EC2 P5e and P5en | Up to eight H200 GPUs | Up to 1,128 GB total HBM3e, with EFA and NVSwitch capabilities documented for the P5 family. | Hourly price is not stated in the cited product documentation; verify current regional pricing and capacity. |
| Azure ND H100 v5 | Eight NVIDIA H100 80 GB GPUs | 96 vCPUs, 1,900 GiB system memory, NVLink 4.0 and 3.2 Tbps scale-out InfiniBand. | Hourly price is not stated in the cited Microsoft Learn documentation. |
| Google Cloud A3 Mega | Eight H100 GPUs | 640 GB total HBM3. | Hourly price is not stated in the cited Compute Engine documentation. |
| Google Cloud A3 Ultra | Eight H200 GPUs | 1,128 GB total HBM3e. | Hourly price is not stated in the cited Compute Engine documentation. |
| Google Cloud A4 | Eight B200 GPUs | 1,440 GB total HBM3e. | Hourly price is not stated in the cited Compute Engine documentation; some newer families require reservations and have region-specific availability. |
CoreWeave’s figures are examples from one North America table, not a global price list. AWS, Azure and Google Cloud prices must be pulled for the exact machine type, region and billing arrangement at the time you buy capacity.
Step 1: Match the accelerator and memory before comparing rates
H100 versus H200
H100 and H200 are not interchangeable for budgeting. The documented eight-GPU AWS P5 system provides up to 640 GB of HBM3, while AWS P5e/P5en and Google Cloud A3 Ultra document up to 1,128 GB of HBM3e across eight H200 GPUs. The larger H200 memory pool can let a model, longer context, larger batch, or optimizer state remain on GPU instead of spilling to slower memory or forcing model parallelism. That can change both throughput and engineering complexity.
B200 and newer systems
Google Cloud’s A4 documentation lists an eight-B200 configuration with 1,440 GB of HBM3e. More memory and a newer accelerator may improve a compatible workload, but it does not prove a lower cost per training run. Software support, reservation requirements, kernel availability and regional capacity must be verified first.
Host resources matter
Two eight-GPU machines can behave differently if one has less CPU, RAM, local scratch or storage bandwidth. Capture those values in your comparison sheet. Azure ND H100 v5, for example, documents 96 vCPUs and 1,900 GiB of system memory; CoreWeave’s pricing listings expose vCPU, system RAM and local-storage fields for each configuration. Do not compare a GPU-only quote with a full-node quote.
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Step 2: Compare topology and networking for distributed jobs
Single-GPU inference can be dominated by model execution and request batching. Multi-GPU training is often dominated by communication. Ask how GPUs connect inside a node and how nodes communicate across the cluster.
- Inside the node: NVLink or NVSwitch affects all-reduce, tensor parallelism and model-parallel traffic. AWS documents up to 900 GB/s NVSwitch peer-to-peer bandwidth for P5.
- Between nodes: RDMA-based fabrics reduce CPU overhead and latency. AWS documents up to 3,200 Gbps Elastic Fabric Adapter networking for P5, P5e and P5en. Azure ND H100 v5 documents 3.2 Tbps scale-out InfiniBand.
- Software path: Confirm NCCL support, provider-specific networking plugins, driver versions, container images and orchestration integration before committing.
- Topology: Determine whether the scheduler places your nodes in a tightly coupled group. A nominal bandwidth figure is less useful if the job receives a fragmented or oversubscribed topology.
AWS says its EC2 UltraClusters can scale to as many as 20,000 H100 or H200 GPUs. Microsoft describes ND H100 v5 deployments scaling to thousands of GPUs and supporting NCCL-based tools. Those ceilings describe platform capability, not a guarantee that your account can obtain the capacity or that your job will scale linearly.
Step 3: Price the completed job, not the GPU hour
Use a line-item model for every provider:
- Compute: instance hours multiplied by the exact hourly rate.
- Storage: boot volumes, local or attached scratch, durable datasets, checkpoints and snapshots.
- Data transfer: ingestion, cross-region movement, cross-provider transfer and internet egress.
- Orchestration: Kubernetes control planes, scheduler nodes, management instances and observability.
- Idle and queue time: time a reservation is running while data loads, workers synchronize or jobs wait for a dependency.
- Engineering overhead: migration work, image maintenance, failed runs, checkpoint recovery and operator time.
- Commercial terms: on-demand, reserved or committed use, and interruptible spot capacity.
The basic equation is completed-job cost = (GPU-node rate × running hours) + storage + transfer + platform services + idle time + failure and recovery cost. Keep on-demand, committed and spot scenarios separate; mixing them hides the risk of interruption and the value of a commitment.
Worked example using a published CoreWeave rate
At the cited North America on-demand rate of $50.44 per hour, an eight-GPU HGX H200 node running for 100 hours produces $5,044 in GPU-node charges before storage, transfer, orchestration or idle time. That is a transparent baseline, not a cross-cloud conclusion. You need the corresponding eight-H200 price and all ancillary charges from each other provider before declaring a winner.
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Use throughput to convert price into useful work
For training, calculate cost per successful token, sample or completed step: total completed-job cost divided by the measured work. For inference, calculate cost per million tokens or per request at the required latency and utilization. A system that is 20 percent cheaper per hour but 30 percent slower, or that loses more work when interrupted, is more expensive per result.
Step 4: Check capacity before selecting a region
GPU availability is a financial variable. Confirm quota, reservation requirements, expected queue time and the exact SKU in the region where your data resides.
- Ask whether the provider can supply the full node count simultaneously rather than in small batches.
- Check whether the family requires a reservation. Google documents reservation requirements for some newer A4X GB200- and GB300-oriented systems.
- Measure queue time separately from execution time. A low rate is irrelevant if a deadline forces you to reserve capacity elsewhere.
- Verify regional differences. CoreWeave’s published $50.44 and $18.00 examples are from its North America table; another region or date may differ.
- Plan a fallback SKU only after validating software compatibility and memory requirements.
How each provider fits a different operating model
CoreWeave: GPU-focused transparency and Kubernetes-oriented operations
CoreWeave publishes a broad GPU catalog with on-demand and spot options, and its pricing page lists GPU count, VRAM, vCPUs, system RAM, local storage and hourly rates. That makes initial cost modeling straightforward, especially when you need to compare several GPU generations. The trade-off is that every listed price and availability signal is regional and time-sensitive. Validate capacity, interruption behavior, networking topology and data-transfer charges for the specific cluster you need.
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Azure positions ND H100 v5 as a flagship GPU family for high-end training, tightly coupled generative-AI workloads and HPC. Its documented eight-H100 node combines 96 vCPUs, 1,900 GiB of RAM, NVLink 4.0 and 3.2 Tbps InfiniBand. Organizations already using Microsoft identity, networking and AI services may value a unified governance and billing boundary. Confirm quota, VM availability, reservation terms and the cost of moving data into the selected region.
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Google Compute Engine documents A3 Mega H100, A3 Ultra H200 and A4 B200 machines, along with newer A4X systems oriented toward GB200 and GB300 platforms. Integration with Compute Engine, GKE and AI Hypercomputer can simplify a Kubernetes-based stack. The important caveats are regional availability and reservation requirements for some families; obtain a capacity commitment before building a schedule around a machine type.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Step 5: Benchmark the workload you will actually run
Vendor peak figures are not cross-cloud benchmarks. Build a short acceptance test using the target model, tokenizer, precision, sequence lengths, dataset format, container image and distributed-training settings.
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- Run one full node and record tokens per second, samples per second, GPU utilization, host CPU utilization and input-pipeline wait time.
- Scale to the intended node count and measure communication time, step time, scaling efficiency and network errors.
- Perform a checkpoint and restart test. Record how much work is lost after a forced interruption and how long recovery takes.
- Repeat enough steps to remove startup effects, then calculate cost per token, sample or converged run using the provider’s exact billing meter.
- Test the production serving path separately, recording p50, p95 and p99 latency, throughput, batch size and utilization.
Keep software constant where possible: the same CUDA and driver compatibility, framework version, NCCL settings, container base, data shard and random-seed policy. Otherwise you are benchmarking software changes as much as hardware.
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Decision guide by workload
| Your priority | What to favor | What to verify before signing a commitment |
|---|---|---|
| Lowest transparent starting price | A provider and SKU with a published regional on-demand or spot rate, such as the CoreWeave examples. | That the advertised configuration has enough memory, storage and network performance; include interruption and egress costs. |
| Large H100 distributed training | An eight-H100 system with NVSwitch and high-bandwidth RDMA/EFA or InfiniBand, such as AWS P5 or Azure ND H100 v5. | Node placement, quota, NCCL performance and the actual multi-node rate. |
| H200 memory capacity | A documented eight-H200 configuration such as AWS P5e/P5en, Google A3 Ultra or CoreWeave HGX H200. | Regional capacity, reservation terms, software support and cost per completed token. |
| Existing Kubernetes and cloud-platform integration | The provider where identity, networking, registries, object storage and operations already live; Google Cloud documents GKE integration, while AWS and Azure provide broader surrounding ecosystems. | Cross-service charges, support requirements and migration effort. |
| Deadline certainty | Reserved or committed capacity with a tested fallback region or SKU. | Cancellation rules, minimum term, failover capacity and the cost of unused reservations. |
Common comparison mistakes and fixes
Comparing unlike GPUs
Fix the comparison set first. Do not place an H100 hourly quote beside an H200 or B200 quote and call the cheaper number a bargain. If a newer accelerator changes batch size or eliminates model sharding, compare cost per completed result instead.
Ignoring queue and idle time
Track wall-clock time from allocation through teardown. Include image pulls, dataset staging, initialization, synchronization, checkpoint uploads and time waiting for a missing worker.
Using a single-region price as a global promise
Label every rate with region, date, purchase model and configuration. CoreWeave’s North America examples illustrate why this matters; other providers also vary by region and capacity condition.
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Assuming advertised scale is guaranteed capacity
Documented ceilings such as AWS UltraClusters scaling to 20,000 GPUs or Azure deployments scaling to thousands describe engineering capability. Obtain a quota approval or reservation for your actual job.
Forgetting data movement
Keep large datasets and checkpoints close to the GPU region when practical. If the cheapest GPU requires repeated cross-region or cross-provider transfers, add those charges and transfer time to the job estimate.
Buying spot capacity for an interruption-intolerant run
Spot can reduce the compute line item, but interruptions create checkpoint, restart and operator costs. Use spot for fault-tolerant workers, experimentation or elastic inference only after measuring recovery.
A practical procurement checklist
- Write down model, precision, batch, context length, dataset size and deadline.
- Choose a like-for-like GPU set and record total HBM, CPU, RAM, local storage and topology.
- Request the exact regional on-demand, reserved or spot rate, including billing granularity.
- Confirm quota, reservation, node count, queue estimate and fallback capacity.
- Price storage, transfer, orchestration, observability, support and engineering time.
- Run the same benchmark container and workload on each finalist.
- Calculate cost per completed result and include failed-run recovery.
- Start with a small commitment, then expand only after utilization and capacity match the forecast.
Bottom line
Choose the provider that delivers the required work at the lowest reliable completed-job cost. CoreWeave is attractive when transparent GPU catalog pricing and GPU-focused infrastructure simplify your estimate. AWS P5/P5e/P5en, Azure ND H100 v5 and Google Cloud A3/A4 offer powerful eight-GPU systems and deep platform integrations, but their economic advantage depends on regional price, capacity, networking, software fit and data location. A measured workload benchmark—not a single hourly number—should make the final decision.
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