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Lambda has the lowest transparent self-serve H100 rate in this comparison at $3.99 per GPU-hour. Crusoe lists $4.50 per H100 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. These list prices were captured on October 18, 2026, and can change with region, capacity and billing model.
Choose AWS when distributed training, Elastic Fabric Adapter (EFA), UltraClusters or deep integration with SageMaker, EKS and ECS matter. Choose Google Cloud when accelerator-optimized VM families, Spot discounts or committed-use pricing fit your workload. Azure is the practical choice for organizations already standardized on Microsoft identity, networking and compliance services. Lambda is the simplest route to clearly priced self-serve GPUs and scale-out clusters. Crusoe combines transparent per-GPU pricing with Spot capacity, managed inference and currently free network ingress and egress. Keep CoreWeave as the baseline when its GPU-native inventory and regional capacity match your job.
At a glance: which CoreWeave alternative fits?
There is no single “cheapest cloud” for every GPU workload. A per-GPU figure can hide the cost of the host, CPU and system memory, local storage, network fabric, data transfer and interruptions. The table below is a screening tool, not a final quote.
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|---|---|---|---|
| CoreWeave | H100 eight-GPU host: $49.24/hour on demand; $19.71/hour Spot (about $6.16 and $2.46 per GPU-hour respectively) | GPU-native infrastructure and regional capacity | Host-level prices include networking, RAM and local storage; the figures are from a North American regional table captured October 18, 2026. |
| Lambda | H100 SXM: $3.99/GPU-hour; A100 SXM: $2.79; B200 SXM6: $6.69; GH200: $2.29 | Fast self-serve experiments and clear unit pricing | Rates are published per GPU-hour; availability and any attached host resources still need checking. |
| Crusoe | H100: $4.50/GPU-hour; H200: $5.40; B200: $8.50; A100: $2.30 on demand | Transparent on-demand or Spot rental, managed inference | On-demand is billed hourly or per second with no minimum commitment; capacity and region affect the actual offer. |
| AWS | P5 H100 and P5e/P5en H200 instances | Distributed training, EFA, UltraClusters and AWS-native operations | The product page does not publish a stable hourly price; use the regional EC2 calculator for the exact instance. |
| Google Cloud | A4, A3 and A2 accelerator-optimized Compute Engine families | Spot or committed-use economics and Google data-platform integration | GPU, VM, disk and network prices depend on region and machine type. |
| Azure | GPU virtual machines, including NC-family options where available | Microsoft identity, networking, compliance and data services | No single Azure GPU price is valid without a region, SKU, operating system and reservation term. |
How to compare GPU rental prices without being misled
Normalize the unit first
Convert a host quote to a GPU-hour only when the host’s GPU count is known. For example, CoreWeave’s $49.24-per-hour H100 host has eight GPUs, so $49.24 ÷ 8 = $6.155, rounded to about $6.16 per GPU-hour. That calculation does not make it equivalent to a bare Lambda or Crusoe GPU: the CoreWeave figure covers a complete eight-GPU system with its associated CPU, memory, local storage and networking.
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Match the accelerator and topology
An H100 SXM, H100 PCIe, H200 and B200 differ in memory capacity, memory bandwidth, thermal envelope and interconnect. For multi-GPU training, record the number of GPUs per host, NVLink or NVSwitch availability, and the inter-node fabric. A lower hourly rate can lose its advantage if a job needs more nodes or spends time waiting on communication.
Include non-GPU charges
- CPU and system memory attached to the VM or host.
- Boot and persistent disks, snapshots and image storage.
- Object-storage requests and any filesystem service used for training data.
- Inter-zone, inter-region and internet egress traffic.
- Reserved capacity, support plans, software licenses and orchestration overhead.
- Idle time while data stages, checkpoints upload or a cluster waits for a job.
Compare the complete configuration and expected wall-clock runtime, not just the accelerator line item. A faster cluster can cost less overall if it finishes a training run sooner.
Separate on-demand, Spot and commitments
On-demand pricing buys flexibility but is usually the highest unit rate. Spot or preemptible capacity can be much cheaper, but the provider may reclaim it. Reservations and committed-use plans reduce rates in exchange for a term or spending commitment. Put the billing model beside every price in your spreadsheet so a one-time discount is not mistaken for a recurring rate.
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CoreWeave as the baseline
CoreWeave publishes a detailed regional table with GPU count, VRAM, vCPUs, system RAM, local storage, on-demand price and Spot price. In North America, the October 18, 2026 table lists the following eight-GPU hosts:
| Configuration | On-demand host price | Spot host price | Approximate on-demand GPU-hour |
|---|---|---|---|
| H100, 8 GPUs | $49.24/hour | $19.71/hour | $6.16 |
| H200, 8 GPUs | $50.44/hour | $20.93/hour | $6.31 |
| A100, 8 GPUs | $21.60/hour | $9.65/hour | $2.70 |
| L40, 8 GPUs | $10.00/hour | Not stated | $1.25 |
| L40S, 8 GPUs | $18.00/hour | Not stated | $2.25 |
The normalized figures are useful for an initial screen, while the host prices are the meaningful numbers for budgeting a complete machine. CoreWeave also lists a single-GPU H100 inference price of $6.16 per hour; treat that as an inference offering rather than proof that every H100 workload can be rented one GPU at that rate. Spot prices lower the bill substantially but require checkpointing and restart logic.
When CoreWeave remains the right choice
- You need a tightly coupled multi-GPU host and its available topology matches your framework.
- A required region has capacity on CoreWeave but not on a lower-priced alternative.
- Your software, images and storage workflow are already built around its GPU-focused environment.
- The all-in host configuration is cheaper than assembling equivalent CPU, memory and storage elsewhere.
Lambda: the clearest self-serve GPU-unit pricing
Lambda’s self-serve catalog reports prices per GPU-hour, making an initial comparison straightforward. The published October 2026 figures are:
| GPU | Self-serve rate | Price basis |
|---|---|---|
| H100 SXM | $3.99 | Per GPU-hour |
| B200 SXM6 | $6.69 | Per GPU-hour |
| A100 SXM | $2.79 | Per GPU-hour |
| GH200 | $2.29 | Per GPU-hour |
On the published H100 figure, Lambda is about 35% below CoreWeave’s normalized $6.16 H100 host rate and about 11% below Crusoe’s $4.50 on-demand H100 rate. That is a price comparison, not a performance or total-cost claim: host memory, storage, networking and availability must still match.
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For larger jobs, Lambda’s 1-Click Clusters product covers 16 to more than 2,000 GPUs. H100 cluster pricing on plans lasting from two weeks to one year is listed at $6.16 per GPU-hour for 16 GPUs, $5.85 for 64 GPUs and $5.54 for 256 GPUs. The declining rate reflects the cluster plan and term; it should not be compared directly with a short, on-demand single-GPU rental.
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Where Lambda fits best
- Developers who want a simple launch path without building a full cloud architecture.
- Interactive experimentation where a clearly stated per-GPU rate is more useful than a complex VM calculator.
- Scale-out training when a planned cluster term makes the 1-Click pricing worthwhile.
Before committing, verify the exact GPU variant, host memory, interconnect, region, queue or provisioning time and what storage is included. The public unit price alone does not establish those details.
Crusoe: transparent rates plus workflow services
Crusoe lists these October 2026 on-demand prices per GPU-hour:
| GPU | On-demand rate |
|---|---|
| B200 | $8.50/GPU-hour |
| H200 | $5.40/GPU-hour |
| H100 | $4.50/GPU-hour |
| A100 | $2.30/GPU-hour |
Crusoe describes on-demand as its most flexible option, billed per hour or per second with no minimum commitment, for workloads where uptime and predictability are important. Its Spot option is intended for jobs that can be stopped and restarted. A training pipeline using frequent checkpoints, resumable data loading and automatic retry can take advantage of Spot; a live service that cannot tolerate interruption should use on-demand capacity.
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Crusoe states that it currently does not charge for network ingress or egress, either within a VPC or to and from the public internet. Confirm the policy for your account, region and traffic pattern before modeling a large transfer, because service terms can change. Even with free network transfer, storage, compute host resources and data-processing charges may remain.
Managed inference and fine-tuning
Crusoe also offers managed inference and serverless fine-tuning. If those services remove the need to operate an always-on cluster, compare the complete workflow cost rather than choosing solely by the raw H100 rate. A higher GPU price can be economical when it eliminates idle serving capacity or operational work.
AWS: the choice for distributed training and AWS-native stacks
AWS EC2 P5 instances use NVIDIA H100 GPUs; P5e and P5en use H200 GPUs. AWS documents configurations with up to eight GPUs per instance, up to 3,200 Gbps of Elastic Fabric Adapter networking and EC2 UltraClusters scaling to as many as 20,000 H100 or H200 GPUs. Those capabilities target tightly coupled distributed training rather than occasional single-GPU experiments.
AWS does not publish a stable hourly price for these instances on the referenced product page. Use the regional EC2 calculator and specify the exact instance, operating system, tenancy, region and purchase model. Do not substitute a single global “P5 price” for that calculation.
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- SageMaker: managed training and deployment workflows.
- EKS and ECS: Kubernetes or container orchestration alongside existing services.
- FSx for Lustre: high-throughput file storage for training data.
- AWS Batch and ParallelCluster: queue-based jobs and HPC-style cluster management.
- EFA and UltraClusters: low-latency, high-bandwidth communication for multi-node jobs.
AWS says its current GPU 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 claims from the AWS product page, not an independent benchmark; your result depends on model, software, scaling efficiency and data pipeline.
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When AWS is the economic choice
Calculate the cost of moving data, operating the cluster and supporting the surrounding application. If your datasets, IAM policies, observability, deployment pipelines and production endpoints already run in AWS, avoiding cross-cloud transfer and re-platforming may outweigh a lower advertised GPU-hour elsewhere.
Google Cloud: accelerator families and flexible discount models
Google Cloud classifies A4, A3 and A2 as accelerator-optimized Compute Engine families. GPU SKU prices depend on the region and machine type, so a valid estimate must include the VM, attached accelerators, boot and data disks, network traffic and any reservation.
Spot pricing
Google states that Spot prices can provide discounts of up to 91%. Spot VMs can be preempted at any time, so use them for fault-tolerant jobs such as distributed training with frequent checkpoints, batch inference and rendering queues. Build automatic restart behavior before relying on the discount; an interrupted job that loses hours of work can cost more than on-demand capacity.
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Committed-use discounts
Google documents committed-use discounts of up to 55% for many Compute Engine machine families and up to 70% for memory-optimized families. These are commitment-based ceilings, not universal GPU rates. Confirm the eligible machine family, term, region and whether the commitment covers the complete VM or only a particular resource.
Billing granularity
Compute Engine bills a minimum of one minute and then in one-second increments. That granularity helps short jobs, but boot time, image pulls and data staging still consume billable time. Include startup and teardown in your runtime model.
Azure: best when Microsoft services already shape the architecture
Azure’s Linux VM pricing calculator is the authoritative route for GPU virtual machines. Select the region, GPU VM family and SKU, operating system, reservation term and purchase model; the retrieved pricing page does not expose a stable NC or H100 price that can be quoted independently of those choices.
Azure is most compelling when Microsoft identity, virtual networking, compliance controls, data services and support contracts already drive the design. A single Azure GPU may cost more or less than a specialist provider after those integration costs are included. Check quota and regional availability before designing around a particular SKU, because a listed VM type is not necessarily available in every subscription or location.
Questions to answer in the Azure calculator
- Which GPU family and exact SKU provide the required VRAM and interconnect?
- Is the workload Linux, and are any marketplace images or licenses included?
- Will a reservation or savings commitment apply for the expected runtime?
- What are the disk, snapshot, outbound-transfer and support charges?
- Can the subscription obtain quota in the target region?
Availability, quota and topology can matter more than price
Check capacity before optimizing a spreadsheet
GPU markets are capacity-constrained and volatile. A low public rate is not useful if the provider has no capacity in your required region or if your account cannot obtain quota. Ask each provider about current stock, expected provisioning time, reservation options and any waitlist before promising a project date.
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Match the communication fabric to the job
Single-GPU fine-tuning, development notebooks and small inference services can tolerate a basic VM. Large transformer training often needs NVLink or NVSwitch within a host and a fast inter-node fabric between hosts. Record those requirements explicitly; otherwise a nominally cheaper cluster may scale poorly and spend more time communicating than computing.
Plan for interruptions
Spot and preemptible capacity can disappear without a guaranteed recovery window. Use durable checkpoints, idempotent data preparation, automatic job resubmission and a fallback capacity class. Keep a small on-demand allocation if a long-running distributed job cannot safely restart from the latest checkpoint.
Billing and total-cost worksheet
Use a consistent worksheet for every provider. The following sequence prevents a low GPU rate from hiding a high all-in bill.
- Define the workload: GPU model, number of GPUs, expected wall-clock hours, memory requirement, interconnect requirement and whether interruption is acceptable.
- Choose the purchase model: on-demand, Spot, reservation or committed use. Record the term and any minimum spend.
- Price the complete host: GPU, vCPU, system RAM, local or attached storage and operating-system image.
- Add data movement: object-storage reads and writes, inter-zone or inter-region traffic, public egress and checkpoint transfer.
- Add orchestration and support: managed training, Kubernetes control-plane costs, monitoring, support plans and engineering time.
- Model interruption: estimate checkpoint overhead, restart time and the probability of losing capacity when using Spot.
- Calculate cost per completed run: multiply the all-in hourly burn by expected runtime, then add retries and idle periods.
For a simple comparison, use total run cost = (GPU and host charges + storage + network + platform charges) × billed hours + restart overhead. Keep the input assumptions next to the result so a regional or pricing change does not silently invalidate it.
Which provider should you choose for common workloads?
Short experiments and notebooks
Start with Lambda’s self-serve catalog or Crusoe’s on-demand instances. Their per-GPU rates are easy to read, and neither requires you to begin with a large cluster commitment. Verify idle timeout behavior and storage persistence so an inexpensive GPU does not sit unused.
Large, tightly coupled training
Evaluate AWS P5 or P5e/P5en, CoreWeave’s eight-GPU hosts, Lambda 1-Click Clusters and any Crusoe multi-GPU offering that matches the required topology. Compare measured scaling efficiency, interconnect and capacity reservation rather than dividing the hourly quote by GPU count alone.
Interruption-tolerant batch jobs
Use Spot where your code can checkpoint and resume. Google advertises discounts of up to 91%, and CoreWeave publishes separate Spot host prices. Crusoe also offers Spot capacity. Build the retry path first, then compare expected completed-run cost rather than the lowest instantaneous rate.
Production inference
Choose the platform that can keep the endpoint available, scale it to demand and integrate with your identity, networking and observability stack. Crusoe’s managed inference option may reduce operations; AWS, Google Cloud and Azure may be preferable when the rest of the application already runs there. Include minimum replicas, autoscaling, model storage and egress in the estimate.
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Start with Azure because identity, policy, networking and compliance integration can simplify the deployment. Validate GPU quota and regional capacity before selecting a model or promising a service-level target.
Data and ML systems already on AWS
Start with AWS P5 or related instances when SageMaker, EKS, ECS, FSx for Lustre, Batch or ParallelCluster are central to operations. The value is the complete pipeline and EFA/UltraCluster scaling, not a published GPU sticker price.
A practical selection procedure
- Write the hardware brief: specify GPU generation, VRAM, GPU count, host memory, storage throughput and interconnect.
- Shortlist two pricing models: one flexible on-demand option and one Spot or commitment option if the workload permits.
- Check region and quota: confirm that the exact SKU can be provisioned in the data region required for latency, residency or compliance.
- Run a small representative job: measure startup time, data-loading throughput, tokens or samples per second, checkpoint time and multi-GPU scaling.
- Calculate completed-work cost: include idle, storage, transfer and retry time, not just active GPU time.
- Document the fallback: define a second provider or SKU and the code path for resuming from checkpoints.
- Recheck immediately before purchase: GPU rates, capacity and discount terms can change between planning and launch.
Common mistakes and how to avoid them
Comparing a host price with a bare GPU price
CoreWeave’s $49.24 H100 figure is an eight-GPU host. Lambda’s $3.99 and Crusoe’s $4.50 figures are per GPU-hour. Normalize the GPU count, then compare the included CPU, memory, storage and networking.
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Assuming “up to” discounts are guaranteed
Google’s up-to-91% Spot and up-to-55% committed-use figures describe potential discounts under qualifying conditions. They are not universal prices. Check the exact SKU, region, term and interruption behavior.
Ignoring egress and data locality
A low compute rate can be overwhelmed by moving a large dataset or serving results across regions. Keep data close to the GPUs or include the transfer in the first estimate.
Choosing Spot for a non-restartable job
Spot capacity is appropriate only when interruption is acceptable. Add checkpointing and restart automation, or use on-demand capacity for jobs that cannot recover cleanly.
Planning around a listed SKU without quota
Cloud catalogs show what a provider offers, not what your account can necessarily launch today. Request quota and verify capacity before finalizing architecture.
Forgetting startup and teardown time
Per-second billing does not make initialization free. Image pulls, filesystem mounts, data staging and checkpoint uploads all extend billed time. Measure the full lifecycle in a pilot.
Final recommendation
For a transparent, self-serve H100 starting point, compare Lambda at $3.99 per GPU-hour with Crusoe at $4.50 per GPU-hour, then verify equivalent host resources and availability. Use CoreWeave’s $49.24 eight-GPU H100 host as a complete-system benchmark rather than treating its $6.16 normalized figure as a like-for-like bare-GPU quote. Move to AWS when EFA, UltraClusters and AWS services determine success; Google Cloud when Spot or committed-use economics and accelerator-optimized families fit; and Azure when Microsoft’s platform integration, policy and regional requirements dominate. Recheck every price, quota and capacity assumption at the time you deploy.
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.
