Short answer: Lambda and Crusoe publish the lowest transparent self-serve H100 rates in the figures dated 18 October 2026: $3.99 and $4.50 per GPU-hour. CoreWeave’s North American eight-GPU H100 host is $49.24 per hour, or about $6.16 per GPU-hour before accounting for the host’s memory, storage and networking. AWS, Azure and Google Cloud do not offer one universal GPU price; their effective cost depends on region, machine type, discounts, quota and the surrounding cloud services.
Choose AWS for tightly integrated distributed training with EFA, UltraClusters, SageMaker, EKS or ECS. Choose Google Cloud for accelerator-optimized VM families and potentially large Spot or committed-use discounts. Choose Azure when Microsoft identity, networking, compliance and data services already define your architecture. Choose Lambda for simple self-serve GPU rental and cluster launch. Choose Crusoe for clear per-GPU prices, Spot options, managed inference and currently free network ingress and egress. Keep CoreWeave in the comparison when its GPU-native inventory and regional capacity fit the workload.
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What makes a fair CoreWeave alternative comparison?
A GPU-hour is only one part of the bill. A published rate may describe a single card, an eight-GPU server, a particular region, a discounted commitment or interruptible Spot capacity. Before comparing providers, record the complete configuration:
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- Topology: GPUs per host, NVLink or NVSwitch, and the bandwidth and fabric used between hosts.
- Host resources: vCPUs, system memory, local NVMe, attached disks and snapshot requirements.
- Location: region, zone, quota and data-transfer path to your datasets and users.
- Billing model: on-demand, per-second or per-minute billing, reservation or commitment, and Spot interruption terms.
- Platform services: Kubernetes, managed training or inference, object storage, filesystems, IAM, monitoring and support.
Normalize prices only for an initial screen. A low per-GPU number can become expensive when the instance has insufficient CPU or memory, requires costly storage, or moves data between regions.
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Published GPU prices at a glance
The following figures are list prices captured on 18 October 2026. They can change with region, capacity and billing model. Lambda and Crusoe quote a GPU-hour, while the CoreWeave figures below quote a complete eight-GPU host; AWS, Azure and Google Cloud require a regional calculator or machine-type selection.
| Provider and accelerator | Published rate | Billing or configuration | What the number means |
|---|---|---|---|
| CoreWeave H100 | $49.24/hour on demand; $19.71/hour Spot | Eight-GPU HGX H100 host, North America | About $6.16 per GPU-hour on demand before host-level differences |
| CoreWeave H200 | $50.44/hour on demand; $20.93/hour Spot | Eight-GPU host, North America | About $6.31 per GPU-hour on demand |
| CoreWeave A100 | $21.60/hour on demand; $9.65/hour Spot | Eight-GPU host, North America | About $2.70 per GPU-hour on demand |
| Lambda H100 SXM | $3.99/GPU-hour | Self-serve | Per-GPU list rate |
| Lambda B200 SXM6 | $6.69/GPU-hour | Self-serve | Per-GPU list rate |
| Lambda A100 SXM | $2.79/GPU-hour | Self-serve | Per-GPU list rate |
| Lambda GH200 | $2.29/GPU-hour | Self-serve | Per-GPU list rate |
| Lambda 1-Click H100 cluster | $6.16, $5.85 or $5.54/GPU-hour | 16, 64 or 256 GPUs respectively; two-week to one-year plans | Cluster-plan pricing, not the self-serve single-GPU rate |
| Crusoe B200 | $8.50/GPU-hour | On demand | Per-GPU list rate |
| Crusoe H200 | $5.40/GPU-hour | On demand | Per-GPU list rate |
| Crusoe H100 | $4.50/GPU-hour | On demand | Per-GPU list rate |
| Crusoe A100 | $2.30/GPU-hour | On demand | Per-GPU list rate |
| AWS P5/P5e/P5en | Not stated as a stable hourly price | Regional EC2 calculator | P5 uses H100; P5e and P5en use H200 |
| Google Cloud accelerator-optimized VMs | Not stated as one universal price | Region and machine-type dependent | Compare VM, GPU, disk, network and discount terms together |
| Azure GPU VMs | Not stated as one universal price | Regional Linux VM pricing calculator | Select region, VM family, operating system and reservation term |
For example, eight Lambda H100 GPUs at the listed self-serve rate would be $31.92 per hour before any non-GPU charges. That arithmetic does not prove it is equivalent to CoreWeave’s eight-GPU HGX host: the host configuration, CPU, RAM, storage and networking must also match.
AWS: the choice for distributed training inside AWS
GPU and network topology
AWS P5 instances use NVIDIA H100 GPUs. P5e and P5en use H200 GPUs. AWS documents configurations of up to eight GPUs per instance and up to 3,200 Gbps of Elastic Fabric Adapter networking. EC2 UltraClusters can scale to 20,000 H100 or H200 GPUs, subject to capacity, quota and the specific deployment design.
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AWS says these instances can accelerate time to solution by up to four times versus previous-generation GPU-based EC2 instances and reduce model-training cost by up to 40%. Those are AWS product-page claims, not an independent benchmark, so treat them as an indication of the vendor’s stated positioning rather than a guaranteed result for your model.
Pricing and capacity
The AWS product information does not publish one stable hourly price for P5, P5e or P5en. Use the regional EC2 calculator and select the exact instance, operating system, tenancy, purchasing option and location. Check GPU quota before planning a multi-node run; a listed instance does not guarantee immediate capacity.
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When AWS wins
- Your data already resides in S3, FSx for Lustre or another AWS service.
- You need EFA and UltraCluster-scale networking for synchronous distributed training.
- SageMaker, EKS, ECS, AWS Batch or ParallelCluster already operate your workflows.
- Central IAM, CloudWatch-style observability, procurement and support contracts matter more than the lowest visible GPU rate.
The trade-off is complexity: the final bill combines EC2, storage, filesystem, data-transfer and orchestration charges, so a GPU-only comparison is incomplete.
Azure: best when Microsoft is already your operating environment
How to price Azure GPU virtual machines
Azure’s Linux VM pricing page is the appropriate starting point, but it does not expose a single stable NC- or H100-equivalent price that applies everywhere. In the calculator, select the region, GPU VM family and exact size, Linux distribution, reservation or savings term, and payment option. Record whether the result is on-demand, reserved or another discounted form before comparing it with a competitor’s rate.
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Azure is most compelling when Microsoft Entra identity, virtual networking, compliance controls, storage, data platforms and enterprise agreements already drive the design. Keeping training data and model endpoints in the same region can avoid transfer charges and simplify governance. Availability is regional and quota-based, so request quota early for large GPU fleets and verify that the required SKU is offered in the chosen zone.
Do not publish or rely on a generic Azure GPU price. A number without a region and SKU can differ substantially from the instance your project can actually obtain.
Google Cloud: accelerator families plus flexible discounting
Machine families and billing granularity
Google Cloud classifies A4, A3 and A2 as accelerator-optimized Compute Engine families. Select the exact family, accelerator count, host shape, region, boot disk and attached storage before calculating cost. Compute Engine bills a minimum of one minute and then in one-second increments, which can reduce waste for short experiments compared with coarser billing.
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Spot and committed-use options
Google says Spot pricing can provide discounts of up to 91% for eligible Compute Engine resources. Spot VMs may be preempted at any time, so use them for checkpointed, restartable training, batch inference or other fault-tolerant work. They are a poor fit for a job that cannot resume after interruption.
Google also documents committed-use discounts of up to 55% for many machine families and up to 70% for memory-optimized families. Eligibility and the exact discount depend on the commitment, resource type and region. Compare the complete VM, GPU, disk, network and reservation bundle rather than applying a percentage to the GPU line alone.
When Google Cloud is the better alternative
- Your datasets, pipelines or serving stack already run on Google Cloud.
- You can design around A-series accelerator VMs and checkpoint regularly.
- A long-running, predictable workload can use a commitment, while elastic jobs can tolerate Spot eviction.
- You need Google’s regional data, analytics or managed-ML integrations alongside the GPUs.
Lambda: the clearest self-serve GPU-unit pricing
Published self-serve rates
Lambda lists H100 SXM at $3.99 per GPU-hour, B200 SXM6 at $6.69, A100 SXM at $2.79 and GH200 at $2.29. These are the easiest figures in this comparison to screen because they are expressed per GPU-hour rather than hidden inside a larger host price.
1-Click Clusters
Lambda’s 1-Click Clusters cover 16 to more than 2,000 GPUs. For H100 clusters, the listed rate is $6.16 per GPU-hour at 16 GPUs, $5.85 at 64 GPUs and $5.54 at 256 GPUs on plans ranging from two weeks to one year. The lower rate at larger sizes reflects a cluster plan; it should not be compared directly with the $3.99 self-serve rate without including the commitment period and the networking and host configuration.
Best use cases and checks
Lambda suits developers who want to launch a GPU quickly, see a predictable unit rate and scale from a single machine to a managed cluster without assembling a full hyperscaler architecture. Confirm region, queue or capacity status, attached storage, networking, image support and any minimum term before committing. A per-GPU quote still leaves CPU, RAM, disk, snapshots and data transfer to be priced separately when they are not included.
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Crusoe: transparent on-demand rates and network-cost advantages
GPU price list
Crusoe lists B200 at $8.50 per GPU-hour, H200 at $5.40, H100 at $4.50 and A100 at $2.30 on demand. Its on-demand option is described as billed per hour or per second, with no minimum commitment, for workloads that need predictable and stable capacity.
Spot, ingress and egress
Crusoe positions Spot capacity for workloads that can stop and restart. Checkpointing is essential before choosing it because an interruption can otherwise discard progress. Crusoe also states that it currently does not charge for network ingress or egress, either within a VPC or to and from the public internet. That policy can materially change total cost for data-heavy training or inference, but verify the current terms and any exceptions when you obtain a quote.
Managed workflow services
Crusoe offers managed inference and serverless fine-tuning in addition to raw instances. If those services replace infrastructure you would otherwise operate, compare the complete workflow price rather than selecting a provider solely on the H100 line. Conversely, a simple long-running training job may be cheaper to run on a plain instance than through an added managed layer.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.CoreWeave as the baseline
What its table includes
CoreWeave publishes a regional table with GPU count, VRAM, vCPUs, system RAM, local storage, on-demand price and Spot price. In North America, the table lists an eight-GPU H100 at $49.24 per hour, H200 at $50.44, A100 at $21.60, L40 at $10 and L40S at $18. The configurations include networking, RAM and local storage, which is why dividing the host price by eight is useful for screening but not a complete total-cost calculation.
Spot economics
The corresponding H100, H200 and A100 Spot prices are $19.71, $20.93 and $9.65 per host-hour. Spot reduces the listed compute charge substantially but introduces interruption risk and may have different capacity availability. Use it only when your training or serving design can resume safely.
How to choose by workload
| Workload or constraint | Most suitable starting points | Reason |
|---|---|---|
| Single-GPU experimentation with transparent pricing | Lambda or Crusoe | Both publish per-GPU rates; Lambda emphasizes self-serve simplicity and Crusoe offers on-demand per-hour or per-second billing. |
| Large synchronous training job | AWS P5/P5e/P5en, CoreWeave, Lambda clusters or Crusoe capacity | Compare interconnect, topology, quota and reserved capacity, not just GPU price. |
| Training that can restart from checkpoints | Google Spot, CoreWeave Spot, Crusoe Spot or another interruptible option | Lower compute cost is exchanged for possible eviction. |
| Microsoft-centered enterprise architecture | Azure | Identity, networking, compliance and data services may outweigh a higher headline rate. |
| AWS-native data and operations | AWS | SageMaker, EKS, ECS, FSx for Lustre, Batch and ParallelCluster reduce integration work. |
| Data-heavy inference exposed to the internet | Crusoe or a provider with favorable transfer terms | Crusoe currently states no network ingress or egress charge; confirm policy and service requirements. |
| GPU-native capacity in a particular region | CoreWeave | Its regional inventory table exposes host resources and Spot alternatives, subject to actual capacity. |
A practical comparison procedure
- Define the job: write down model size, number of GPUs, VRAM requirement, expected run time, checkpoint interval, storage volume and whether interruption is acceptable.
- Choose the hardware: compare the same GPU generation and form factor. An H100 SXM listing is not automatically equivalent to a PCIe H100 or a different host topology.
- Match the topology: record GPUs per server, NVLink or NVSwitch, inter-node fabric and bandwidth. Distributed training can lose more time to communication than it saves from a cheaper card.
- Price the complete bundle: include GPU or VM, CPU, RAM, boot and data disks, snapshots, filesystem, orchestration, public IPs, inter-region traffic and internet egress.
- Apply the billing model: calculate on-demand, reservation or commitment, and Spot scenarios separately. Do not compare a guaranteed on-demand rate with a preemptible discount as if they were the same service.
- Check capacity: verify region, quota, waitlist, reservation and expected launch time. A nominally cheaper GPU that cannot be provisioned when needed has no practical advantage.
- Measure a pilot: run a representative short job and record tokens or samples per second, scaling efficiency, checkpoint time, storage throughput and total invoice. Use that result to decide whether a higher GPU rate buys enough throughput or operational simplicity.
Common cost and availability mistakes
Comparing a host price with a card price
CoreWeave’s $49.24 H100 figure covers an eight-GPU host with associated resources, while Lambda’s $3.99 and Crusoe’s $4.50 are per-GPU figures. Divide only as a screening step, then compare CPU, memory, storage and networking on both sides.
Ignoring data movement
Moving large datasets between regions or to an external serving endpoint can exceed the GPU charge. Keep data and compute together where possible, and include both inter-region and internet transfer in the estimate. Crusoe’s stated no-charge ingress and egress policy is an advantage only if it applies to your exact service and traffic pattern.
Using Spot for a non-restartable run
Spot and other interruptible capacity can terminate without a convenient notice period. Save checkpoints to durable storage, test recovery, and estimate the extra wall-clock time caused by restarts before treating the discount as savings.
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GPU supply changes by region and time. AWS quota, Azure SKU availability, Google accelerator capacity, Lambda cluster inventory, Crusoe capacity and CoreWeave regional inventory can all constrain a deployment. Recheck availability immediately before scheduling a job.
Overlooking commitment terms
Lambda cluster prices cover plans from two weeks to one year, while Google committed-use discounts require a defined commitment. A lower rate is not cheaper if the GPUs sit idle or the workload changes before the term ends.
Bottom-line decision
For the lowest transparent self-serve H100 figure in this comparison, start with Lambda at $3.99 per GPU-hour, then evaluate Crusoe at $4.50 when its transfer policy or managed services reduce your total cost. Use CoreWeave when its available host configuration and regional capacity are worth the integrated eight-GPU package. Move to AWS, Azure or Google Cloud when ecosystem integration, compliance, data locality, networking or discount programs are more important than a standalone GPU rate. Recalculate every quote on the day you deploy: prices, capacity and discount terms are volatile.
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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