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Repair common Windows errors and clear accumulated junk for a smoother, more stable PC - no reinstall needed.Free scan · no reinstallThere is no evidence-based universal winner among CoreWeave, AWS, Azure and Google Cloud for AI workloads. The right choice depends on the accelerator generation, interconnect, capacity you can actually reserve, and the full cost of storage, data movement, operations and support. CoreWeave publishes unusually visible GPU list prices and focuses on tightly integrated AI infrastructure. AWS and Azure document large H100/H200 systems, networking and reservation products in detail. Google Cloud belongs on the shortlist when Vertex AI, Google data services or TPUs fit, but its current regional accelerator price and capacity must be verified before a numeric comparison.
The practical answer to “What is the cheapest cloud for model training?” is to run the same representative job in provisionable regions on equivalent hardware, then compare cost per completed training step or token—not one advertised hourly number.
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At a glance: which provider fits which situation?
| Provider | Documented 2026 evidence | Most defensible fit | Important caution |
|---|---|---|---|
| CoreWeave | North America list prices of $42.00 per hour for a four-GPU NVIDIA GB200 NVL72 configuration and $68.80 per hour for an eight-GPU NVIDIA HGX B200 configuration. | Teams prioritizing dedicated AI infrastructure, current GPU systems and transparent specialist-cloud pricing. | List prices depend on region, availability and contract terms. Vendor-hosted TCO and performance claims are not universal benchmarks. |
| AWS | P5 instances support up to eight H100 GPUs; P5e/P5en support up to eight H200 GPUs. Large systems offer up to 3,200 Gbps EFA and 900 GB/s NVSwitch. Capacity Blocks list $41.528 per p5.48xlarge instance-hour and $98.84 per p6-b200.48xlarge instance-hour in listed US regions. | Organizations already using AWS identity, storage, networking, compliance controls or enterprise agreements. | Capacity Block rates are a specific reservation product, not a universal on-demand price. |
| Azure | ND H100 v5 is an eight-H100 VM family with 3.2 Tbps interconnect bandwidth, 400 Gbps InfiniBand per GPU and NVLINK 4.0. | Large distributed training tied to Microsoft data, identity, contracts or Azure AI services. | Detailed estimates require the Azure Pricing Calculator and confirmation of regional quota and supply. |
| Google Cloud | Current regional product, price and capacity details were not established for a normalized comparison. | Teams using Vertex AI, Google data services or TPUs, subject to accelerator and region verification. | Do not assume a current price, availability level or performance ranking without checking the exact configuration. |
This table is a starting point, not a ranking. An eight-GPU H100 node, an eight-GPU B200 node and a four-GPU GB200 NVL72 configuration have different memory, precision, networking and throughput characteristics.
Why a GPU-hour is not a comparable unit
Accelerator generation changes the economics
H100, H200, B200 and GB200 systems are not interchangeable. They differ in GPU memory, memory bandwidth, supported precision, host design and the amount of work completed per hour. A lower hourly rate on an older or smaller system can cost more for a fixed training run if the job takes longer or requires more nodes. Conversely, a newer accelerator can have a higher rental price but finish the same workload in fewer hours.
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Compare the hardware named in the quote, not just the provider. Record the exact GPU model, number of GPUs per host, GPU memory, host CPU and RAM, local or attached storage, and whether the quoted system is a complete tightly coupled instance.
Interconnect determines multi-node scaling
Distributed training repeatedly exchanges gradients, activations or optimizer state. NVLink and NVSwitch govern traffic inside a host; InfiniBand or Elastic Fabric Adapter (EFA) governs traffic between hosts. A cluster with the same nominal GPU count can have very different step times when topology, bandwidth or latency differs.
AWS documents up to 3,200 Gbps EFA and 900 GB/s NVSwitch on its largest P5-class configurations. Azure ND H100 v5 specifies 3.2 Tbps interconnect bandwidth, dedicated 400 Gbps InfiniBand per GPU and NVLINK 4.0. Those figures describe infrastructure capabilities, not a guaranteed end-to-end training speed for every model. CoreWeave quotes must be checked for the exact networking and storage topology attached to each configuration.
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CoreWeave’s displayed figures are provider list prices for named configurations. AWS’s published $41.528 p5.48xlarge and $98.84 p6-b200.48xlarge figures are Capacity Block rates in listed US regions. A Capacity Block reservation, on-demand rental, spot or preemptible allocation and a long-term committed contract can have different prices, cancellation rules and availability. Comparing one Capacity Block number with another provider’s flexible hourly rate can produce a false conclusion.
Utilization and idle time matter
You pay for allocated capacity while data is staging, checkpoints are writing, jobs are waiting on a scheduler or a failed worker is being replaced. Measure productive GPU time and include the hours your workflow leaves instances running. A nominally cheap cluster can lose its advantage if it is difficult to obtain, frequently interrupted or poorly utilized.
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CoreWeave: specialist AI infrastructure with visible GPU pricing
Published configurations and rates
| CoreWeave configuration | GPU count in listed configuration | Displayed rate | Simple rate per listed GPU |
|---|---|---|---|
| NVIDIA GB200 NVL72 (North America table) | 4 | $42.00 per hour | $10.50 per GPU-hour |
| NVIDIA HGX B200 (North America table) | 8 | $68.80 per hour | $8.60 per GPU-hour |
The per-GPU arithmetic is only a normalization aid. It does not account for memory capacity, software performance, host resources, network topology or the amount of training completed per hour. CoreWeave’s 2026 figures are list prices and must be checked for the region, current supply, minimum term and contract conditions attached to an actual quote.
Where the specialist model can help
- A focused catalog can make it easier to target a current AI accelerator rather than assemble a general-purpose cloud design.
- Integrated compute, networking and storage may reduce the number of infrastructure decisions for a dedicated training cluster.
- Public configuration pricing gives you a visible starting point for a request for quote.
How to interpret the vendor-hosted study claims
A CoreWeave-hosted Signal65 report states “up to 52% lower three-year TCO on NVIDIA HGX B300” and “up to 65% per-token cost/performance advantage running DeepSeek-R1.” Those are vendor-hosted study claims with “up to” qualifiers, not independent cross-provider benchmarks. The result may depend on utilization, software, workload, financing assumptions and the comparison set. Treat it as a hypothesis to test with your own job, not as a guaranteed discount.
Questions to ask before committing
- Can the exact GB200 or B200 configuration be provisioned in the region and dates required?
- What are the host-to-host network specifications, storage throughput and checkpoint durability?
- Are there minimum hours, term commitments, reservation deposits or cancellation fees?
- Which Kubernetes, container, driver and distributed-training versions are supported?
- How are failed nodes replaced, and how is unused capacity billed during recovery?
AWS: broad services plus documented large H100, H200 and B200 systems
Training-oriented EC2 families
AWS documentation describes P5 instances with up to eight H100 GPUs and P5e/P5en instances with up to eight H200 GPUs. The largest configurations are positioned for large-model training and include up to 3,200 Gbps EFA and 900 GB/s NVSwitch. For an organization already storing datasets in Amazon S3 and operating through AWS identity, logging and networking, those surrounding services can be worth more than a small difference in GPU rent.
Capacity Block figures
| AWS product | Listed Capacity Block rate | Provider’s per-GPU arithmetic | Qualification |
|---|---|---|---|
| p5.48xlarge | $41.528 per instance-hour | $5.191 per H100 | Capacity Block rate shown for several US regions; reservation product, not a universal on-demand rate. |
| p6-b200.48xlarge | $98.84 per instance-hour | $12.355 per B200 | Capacity Block rate shown for listed regions; verify the current region and term. |
The per-GPU figures inherit the assumptions of the instance and the Capacity Block. They do not include storage, data transfer, orchestration, support or idle time. A p5 quote and a B200 quote also represent different accelerator generations, so the hourly numbers cannot establish which one is cheaper for a completed model.
When AWS’s breadth is financially valuable
- Existing S3, VPC, IAM, monitoring and security controls can reduce migration and operating work.
- Capacity Blocks provide a reservation mechanism when a defined training window matters.
- Large EFA and NVSwitch configurations support tightly coupled jobs when the software stack is tuned for them.
- Enterprise support, compliance programs and procurement processes may outweigh a specialist provider’s lower compute list price.
Confirm quota, placement, block duration, release rules and regional stock before budgeting. A published rate without a guaranteed allocation is not a usable training plan.
Azure: tightly coupled ND H100 v5 for scale-out training
Documented topology
Microsoft describes ND H100 v5 as an eight-H100 VM family designed for tightly coupled training and scale-out. Each VM has 3.2 Tbps interconnect bandwidth, dedicated 400 Gbps InfiniBand per GPU and NVLINK 4.0. These details make the family a serious candidate for jobs that spend substantial time in all-reduce and other collective operations.
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How to price an Azure design
Azure directs buyers to the Azure Pricing Calculator for detailed estimates. Enter the exact ND H100 v5 size, region, operating-system choice, reservation or pay-as-you-go term, managed disks, storage transactions, data transfer and support plan. Then verify that the subscription has enough quota and that the selected region can deliver the required node count at the intended start date.
Azure-specific decision factors
- Microsoft identity, networking, security and compliance controls can reduce the cost of integrating training into an existing estate.
- InfiniBand and NVLINK specifications should be matched to the framework’s communication libraries and container images.
- Regional capacity and quota can be the binding constraint even when the calculator returns a price.
- Reservation terms and enterprise agreements can materially change the effective hourly rate.
Do not infer Azure’s completed-training cost from the network specification alone. Run a fixed workload and record step time, scaling efficiency and paid idle hours.
Google Cloud: include it for ecosystem fit, verify the numbers
Google Cloud remains a credible candidate when your data is already in Google services, when Vertex AI’s managed workflow is valuable, or when a TPU is a better match than a GPU. However, current regional product, price and capacity details were not established for a normalized comparison here. That means no defensible claim can be made that Google Cloud is the cheapest, fastest or most available option.
For a fair quote, identify the exact accelerator or TPU generation, machine type, region, quota, reservation or preemptible terms, storage path and network requirements. Verify the price in Google’s current documentation and calculator on the same date you check the other providers. Record whether Vertex AI management charges, data processing, persistent disks and egress are included.
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Illustrative 720-hour arithmetic—and why it is not a forecast
The following multiplication shows how a month of continuous allocation would look using the published hourly figures. It is not a monthly quote, because none of these rows includes storage, transfer, support, taxes, discounts, interruptions or contract requirements.
| Configuration | Hourly figure | 720-hour arithmetic | What the result means |
|---|---|---|---|
| CoreWeave GB200 NVL72, four GPUs | $42.00 | $30,240 | Illustrative continuous allocation at the displayed North America list rate. |
| CoreWeave HGX B200, eight GPUs | $68.80 | $49,536 | Illustrative continuous allocation at the displayed North America list rate. |
| AWS p5.48xlarge | $41.528 | $29,900.16 | Illustrative use of the listed Capacity Block instance-hour rate. |
| AWS p6-b200.48xlarge | $98.84 | $71,164.80 | Illustrative use of the listed Capacity Block instance-hour rate. |
The arithmetic demonstrates why “cheapest GPU-hour” is an incomplete question. The CoreWeave GB200 row and AWS p5 row use different generations and pricing products; the B200 rows likewise differ in configuration and contract. Compare the cost to complete your actual training run.
How to run an apples-to-apples training comparison
- Define one representative job. Use the model size, sequence length, batch strategy, optimizer, precision, dataset sample and target number of tokens you expect in production. Include checkpoint frequency and evaluation passes.
- Choose equivalent hardware. Compare H100 with H100, H200 with H200, B200 with B200 or GB200 with GB200 where possible. If generations differ, state the technical reason and measure completed work, not just rental price.
- Freeze the software stack. Pin the framework, CUDA or equivalent runtime, driver, compiler, collective-communication library, container image and data-loader settings. A software mismatch can look like a hardware price difference.
- Match topology and node count. Record GPUs per host, NVLINK or NVSwitch arrangement, InfiniBand or EFA bandwidth, host memory, local scratch and shared storage. Use the same number of workers and the same parallelism strategy.
- Verify supply before testing. Obtain quota approval, a reservation or written capacity confirmation in each region. A benchmark on an unrepeatable allocation cannot support a procurement decision.
- Warm up and repeat. Discard startup and compilation effects, then run enough steps to capture steady-state throughput, checkpoint pauses and failure behavior. Repeat at least enough times to expose normal variance.
- Measure useful output. Capture tokens per second, seconds per optimizer step, scaling efficiency, GPU utilization, memory headroom, checkpoint duration and paid idle time. Note any preemptions or node replacements.
- Normalize the bill. Divide all-in cost by completed tokens or by the target training run. Add storage, snapshots, data transfer, orchestration, support and engineering time. Report a range for utilization and interruption assumptions.
Build a total-cost-of-training worksheet
Use one row per provider, region and configuration. Keep one-time and recurring costs separate so a short experiment is not priced like a full production program.
| Cost category | Questions to answer |
|---|---|
| Accelerator allocation | What is the exact instance or VM rate, pricing mode, minimum term and number of billed hours? |
| Storage | How much object, block, local scratch and snapshot storage is required? What are read, write and transaction charges? |
| Data movement | Will data cross regions, availability zones or provider boundaries? Are egress and interconnect charges included? |
| Checkpointing | How often are checkpoints written, how long do they pause training and how many copies are retained? |
| Orchestration | What do Kubernetes, managed training, schedulers, logging and monitoring add per run? |
| Engineering operations | How many hours are spent on drivers, images, scaling, failed workers, quota requests and incident response? |
| Support and compliance | Is a paid support plan, audit control or dedicated account team required? |
| Idle and retry allowance | What percentage of allocated time is spent staging, waiting, debugging or rerunning failed work? |
A useful formula is: total training cost = compute allocation + storage + data movement + orchestration and support + engineering time + idle and retry cost. Then calculate cost per completed token or cost per finished run. Keep the assumptions beside every number.
Distributed-training questions that decide the outcome
Can the job fit on one host?
If model and optimizer state fit within one host’s GPU memory, a single node avoids inter-node synchronization and simplifies failure recovery. In that case, the provider with the best provisionable single-node throughput and all-in rate may win even if its large-cluster networking is irrelevant.
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Does the job require many nodes?
For multi-node training, test collective operations at the intended scale. Compare scaling efficiency from one to several nodes, not just the advertised link speed. Check whether the provider exposes the required topology, placement controls and communication libraries.
How expensive is a failure?
Frequent checkpoints reduce lost work but consume storage bandwidth and pause the job. A cheaper but interruption-prone allocation can be more expensive when a restart loses hours of computation. Include recovery time in the benchmark.
Capacity, quotas and procurement risk
- Request capacity early: ask for the exact GPU, node count, region and start/end window rather than a generic “H100 quote.”
- Separate quota from supply: an approved quota does not guarantee that inventory is available when you need it.
- Clarify reservation mechanics: document block length, cancellation, release, extension and no-show rules.
- Price interruption behavior: record notice periods, checkpoint expectations and whether replacement capacity is automatic.
- Plan a fallback: keep a second region or provider tested if a missed training window has material financial cost.
For a fixed launch date, capacity certainty can be worth more than a nominally lower rate. Put the provider’s written capacity commitment and the financial consequence of delay into the business case.
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Platform integration, portability and lock-in
Compute is only one part of an AI training platform. AWS, Azure and Google Cloud offer broad identity, networking, data, observability and managed-service ecosystems. Those capabilities can lower the cost of operating a secure pipeline, especially when your data and staff already live in that ecosystem. A specialist cloud may reduce infrastructure complexity for GPU-focused work but require more deliberate integration, portability testing and support planning.
Keep training code, containers, checkpoints and data formats portable where practical. Document provider-specific assumptions such as EFA, InfiniBand, storage APIs, managed orchestration and identity roles. Portability is not free, but it gives you leverage when prices, quotas or availability change.
Procurement checklist before signing
- Write down the exact accelerator generation, GPU count, memory and host configuration.
- Obtain a region-specific quote with the pricing mode, term, minimum allocation and renewal rules.
- Confirm quota and written capacity for the dates of the training run.
- Validate NVLINK/NVSwitch, InfiniBand or EFA topology and the storage throughput required by checkpoints.
- Run the same container and representative workload on every provisionable option.
- Record tokens per second, step time, scaling efficiency, utilization, failures and checkpoint pauses.
- Add storage, egress, orchestration, support, engineering and idle-time costs to the worksheet.
- Calculate cost per completed token and cost per finished run under conservative, expected and optimistic utilization.
- Review data residency, security, compliance, incident response and support obligations.
- Document an exit path: exported checkpoints, reproducible environments and a tested fallback provider or region.
Bottom line
CoreWeave can be compelling when a provisionable GB200 or B200 system, specialist operations and a visible GPU rate match your workload. AWS is strong when large P5/P5e/P5en systems and the surrounding AWS platform reduce integration cost. Azure’s ND H100 v5 is designed for tightly coupled scale-out training and may fit Microsoft-centered organizations. Google Cloud deserves a measured trial when Vertex AI, Google data services or TPUs are central, but its current regional price and capacity must be verified.
No published figure here establishes a universal cheapest or fastest provider. Make the decision with an identical workload, equivalent hardware, confirmed capacity and a total-cost model that includes every billed and operational component.
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