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What each provider is actually selling
These are not four identical GPU-rental products. Crusoe combines an AI-focused infrastructure cloud with managed AI services. AWS, Google Cloud, and Microsoft Azure combine accelerators with much larger general-purpose cloud platforms, enterprise controls, and service catalogs. Your decision therefore has two parts: the economics and performance of the accelerator, and the amount of surrounding platform work your team wants to operate.
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| Provider | Core strength | Best fit | Main trade-off |
|---|---|---|---|
| Crusoe | Published GPU rates, newer accelerator options, managed inference and serverless fine-tuning, Kubernetes or Slurm control | Teams that want direct AI infrastructure pricing and a focused operating model | Less breadth than a hyperscaler’s global service ecosystem; exact capacity and regional fit still require a quote |
| AWS | P5/P5e/P5en instances, EFA and NVSwitch networking, UltraClusters, SageMaker and Bedrock | Large distributed training and organizations already standardized on AWS governance | Pricing is assembled from instance, storage, transfer, support and purchasing choices rather than one simple GPU rate |
| Google Cloud | Gemini platform, Model Garden, tuning, custom training, prediction and MLOps tied to Google data services | Teams building model-centric products around Google data, analytics and managed ML workflows | Accelerator pricing depends on model, region, VM shape and purchase model; a generic GPU number is not a valid quote |
| Azure | Foundry tools, Microsoft identity and contracts, and access to models from Microsoft, OpenAI, Meta and Cohere | Microsoft-centric enterprises that need integrated application, identity and procurement controls | H-series and NC-series lifecycle changes make migration and current-VM selection important |
Crusoe Cloud: transparent rates with two operating modes
Crusoe documents an Infrastructure Cloud and a Managed AI layer. Infrastructure Cloud provides GPU and CPU virtual machines, persistent and object storage, VPC networking, load balancers, managed Kubernetes, managed Slurm and a container registry. Managed AI adds serverless inference, self-serve deployments and serverless fine-tuning. You can therefore expose a managed endpoint, or run the cluster and scheduler yourself.
Published GPU and infrastructure rates
The following are the rates listed on Crusoe’s 2026 rate card. They are vendor prices that can change, so verify the live quote for your region and purchasing model before committing.
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| Resource | Listed price | Unit and qualification |
|---|---|---|
| NVIDIA H100 80GB HGX | $4.50 | Per GPU-hour |
| NVIDIA H200 141GB HGX | $5.40 | Per GPU-hour |
| NVIDIA A100 80GB SXM | $2.30 | Per GPU-hour |
| NVIDIA A100 80GB PCIe | $2.00 | Per GPU-hour |
| NVIDIA L40S 48GB | $1.50 | Per GPU-hour |
| AMD MI300X 192GB | $3.45 | Per GPU-hour |
| General-purpose CPU | $0.04 | Per vCPU-hour |
| Storage-optimized CPU | $0.09 | Per vCPU-hour |
| Persistent disk | $0.08 | Per GiB-month |
| Shared disk | $0.07 | Per GiB-month |
| Object storage | $0.06 | Per GiB-month |
| Managed Kubernetes | $0.10 | Per cluster-hour |
For a GPU-only illustration, eight H100 GPUs running for one hour at the listed on-demand rate would be $36 before CPU hosts, storage, transfer, support or other services. That arithmetic is not a full job quote.
On-demand, spot and reserved purchasing
Crusoe offers on-demand, spot and reserved purchasing models. Its pricing page states that on-demand instances are billed hourly or per second with no minimum commitment. The same page describes spot capacity as intended for fault-tolerant workloads that can be interrupted. It also currently states no upfront setup fees for on-demand GPU or CPU instances and no network ingress or egress charges. Treat those statements as current vendor policy rather than a permanent promise; confirm them in your order and region.
Crusoe describes on-demand as “our most flexible option, billed per-hour (or per-second) with no minimum commitment, and ideal for workloads where uptime, predictability, and stability are critical.” The useful distinction is operational: on-demand is the default for an uninterrupted training run, spot is a cost lever only when checkpoints and automatic recovery are already designed, and reserved capacity is a commitment trade-off that must be compared with expected utilization.
Where Crusoe fits technically
- Training: managed Slurm or Kubernetes lets you operate distributed jobs directly; you remain responsible for images, job configuration, checkpoints and observability unless you choose a managed service.
- Inference: serverless inference and self-serve deployments can remove much of the endpoint plumbing, while infrastructure VMs allow custom runtimes and dedicated capacity.
- Fine-tuning: serverless fine-tuning is available when you prefer a managed workflow; self-managed clusters remain an option for unusual frameworks or strict control requirements.
- Storage and networking: persistent and shared disks, object storage, VPC networking, load balancers and a container registry cover the basic platform around a GPU job.
AWS: the scale and networking option
AWS EC2 P5 instances use NVIDIA H100 GPUs; P5e and P5en use H200 GPUs. AWS documents up to eight GPUs per instance, up to 900 GB/s of NVSwitch GPU interconnect and up to 3,200 Gbps of Elastic Fabric Adapter (EFA) networking. AWS says EC2 UltraClusters can scale to 20,000 H100 or H200 GPUs with a petabit-scale nonblocking network. These specifications matter when communication between nodes, not just raw GPU memory, limits training speed.
P5-family documentation targets deep learning, generative AI and HPC. Local NVMe, FSx for Lustre and S3 provide different storage paths for checkpoints, datasets and high-throughput scratch data. AWS claims up to four times the performance of previous-generation GPU instances and up to 40% lower machine-learning training infrastructure cost. Those are AWS claims, not independent benchmarks; your model, parallelism, data pipeline and utilization determine the actual result.
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Managed AWS workflows
Amazon SageMaker supplies managed infrastructure, tools and workflows for building, training and deploying machine-learning and foundation models. AWS also positions Bedrock for generative-AI application development. The advantage is breadth: identity, networking, logging, storage, deployment and governance can use services your organization already operates. The cost is a more complex bill and more architecture decisions than a single published GPU-hour rate.
AWS states that “Amazon EC2 P5 instances, powered by NVIDIA H100 Tensor Core GPUs, and P5e and P5en instances powered by NVIDIA H200 Tensor Core GPUs deliver high performance in Amazon EC2 for deep learning (DL) and high performance computing (HPC) applications.” Confirm instance availability, region, tenancy and purchase option before assuming a particular cluster size is obtainable.
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Google’s Gemini platform is organized around model choice, tuning, training, prediction and operational tooling. Its Model Garden includes more than 200 Google and third-party models, including Gemini, Claude and Gemma. The platform also describes model registry, pipelines, feature store, monitoring, BigQuery integration and notebooks, giving teams a route from experimentation to a production endpoint.
Google Cloud’s Compute pricing page is the authoritative place to obtain accelerator rates. The amount depends on GPU model, region, VM shape and purchasing model, so copying an H100 figure without those variables produces a misleading comparison. Include attached CPU capacity, local or network storage, inter-zone traffic and any committed-use or spot terms in the quote.
Google describes Gemini platform as “our open and comprehensive platform that empowers businesses to rapidly build, scale, govern and optimize enterprise-grade agents grounded in your enterprise data.” That positioning is most relevant when the model and the surrounding data/ML workflow are inseparable. If you only need a VM to run a container, its broader platform may add capabilities you do not use.
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Azure: Foundry plus Microsoft enterprise integration
Microsoft Foundry Tools provides customizable tools, APIs and models for production AI applications. Microsoft says customers can use foundation models from Microsoft, OpenAI, Meta and Cohere. Foundry pricing is consumption-based across models, agents and tools, so a realistic estimate must include model-token usage and tool calls as well as GPU or VM resources.
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Azure’s Linux VM pricing page currently warns that H-series and NC-series promotional VMs are no longer available and that normal H-series and NC-series instances will be decommissioned, directing customers to migration guidance and current quotes. This is not a minor footnote: an apparently attractive instance can create a near-term migration project. Check the current replacement family, region, quota and software image before designing around a legacy SKU.
Microsoft describes Foundry as a way to “gain a competitive edge by building AI apps powered by foundation models, including those from Microsoft, OpenAI, Meta, and Cohere.” The practical reason to choose Azure is often less about a single GPU rate than about Microsoft identity, networking, procurement, compliance processes and existing operational skills.
Price: why no provider can be declared cheapest in general
Crusoe’s public rate card makes a headline comparison easy, but an end-to-end cloud bill is not a GPU-hour multiplication. AWS, Google Cloud and Azure prices vary by region, VM shape, accelerator generation, purchase model and contract. The same factors affect Crusoe availability and final quotes.
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- Model and precision: identify the exact model, quantization or training precision and framework version.
- Workload shape: record batch size, sequence length, tokens, expected steps, checkpoint frequency and target completion time.
- GPU topology: specify GPU count, memory requirement, single-node versus multi-node placement and interconnect needs.
- Region and capacity: use the same geography and confirm quotas, reservation terms and realistic availability.
- Runtime: estimate wall-clock hours, idle time, retries and utilization rather than assuming 100% GPU duty cycle.
- Data movement: include dataset ingress, checkpoint traffic, cross-zone or cross-region transfer and egress to users or another cloud.
- Platform services: add CPU hosts, disks, object storage, registry, Kubernetes or Slurm, load balancers, monitoring and managed inference or tuning fees.
- Commercial terms: compare on-demand, spot and reserved prices with the same interruption tolerance and commitment period.
- People and migration: assign a value to porting images, schedulers, networking, observability, security reviews and incident response.
Report both the raw infrastructure total and the engineering effort. A lower GPU-hour rate can be outweighed by migration, orchestration, monitoring or data-transfer work; a higher rate can be rational when it removes weeks of platform engineering or materially shortens training time.
Availability, performance and scaling
When single-node performance is enough
Fine-tuning, evaluation, batch inference and smaller training runs may fit within one node. In that case, GPU memory, hourly price, startup time and software compatibility usually matter more than a supercomputer-class fabric. Crusoe’s H100, H200, A100, L40S and MI300X choices let you match memory and cost to the model rather than defaulting to the largest cluster.
When the network becomes the bottleneck
Large distributed training repeatedly exchanges gradients and activations across GPUs. AWS’s documented NVSwitch and EFA figures, plus UltraCluster scale, are designed for this regime. A specialist cloud can still be appropriate, but you must verify its actual node topology, interconnect, storage throughput and scheduler behavior for your job; a nominally cheaper GPU is not cheaper if communication leaves the GPUs idle.
Availability is a quote, not a brand attribute
GPU supply changes by region and model. Ask every provider for the number of GPUs that can be provisioned together, expected start time, maximum run duration, failure-recovery policy and whether the quote is reserved, on-demand or interruptible. Treat a published rate as a price signal, not a guarantee that eight or hundreds of those GPUs are immediately available.
Managed services versus operating the stack yourself
| Need | Managed path | Self-managed path | Decision question |
|---|---|---|---|
| Training | SageMaker, Google training/tuning workflows, or a managed fine-tuning service | Crusoe, AWS, Google Cloud or Azure VMs with your scheduler and containers | Do you need custom kernels, unusual topology or full scheduler control? |
| Inference | Crusoe serverless inference, managed endpoints, Foundry or model-platform deployment | GPU VMs, Kubernetes, load balancers and your own autoscaling | Is traffic predictable enough to justify dedicated capacity? |
| Operations | Provider-managed provisioning, deployment and some monitoring | Your team owns images, upgrades, quotas, logging, checkpoints and recovery | Can your team support the platform at nights and weekends? |
Managed services reduce undifferentiated work but can constrain supported frameworks, regions, networking patterns or billing visibility. Self-managed infrastructure maximizes portability and control but makes your team responsible for every failure between the dataset and the endpoint.
Security, residency and ecosystem fit
For regulated or contract-bound data, compare the exact region, tenancy model, encryption controls, identity integration, audit logs, incident process and compliance scope. Do not infer parity from a provider’s general cloud reputation. Confirm whether training data, checkpoints, logs and support access remain in the required geography.
Portability also has several layers. Container images and open frameworks move relatively easily; managed feature stores, agent tools, proprietary model APIs, scheduler integrations and data pipelines do not. Keep model artifacts and infrastructure definitions exportable, document accelerator-specific optimizations, and test a recovery path on a second provider if downtime or bargaining power matters.
Which provider should you choose?
Choose Crusoe when
- You want a visible GPU-hour rate and simple on-demand accounting.
- You need H100, H200, A100, L40S or MI300X capacity and can validate regional availability.
- You want managed inference or serverless fine-tuning but still need Kubernetes or Slurm for custom jobs.
- Your team values direct infrastructure control more than a huge adjacent service catalog.
Choose AWS when
- Distributed training requires the documented P5/P5e/P5en topology, EFA, NVSwitch or UltraCluster scale.
- Your organization already uses AWS identity, networking, S3, FSx, observability and procurement controls.
- SageMaker and Bedrock workflows justify the platform complexity.
Choose Google Cloud when
- Gemini, Model Garden and access to more than 200 Google and third-party models are central to product decisions.
- BigQuery, notebooks, pipelines, feature store, registry and monitoring should share one ML workflow.
- You need tuning, custom training and prediction integrated with Google data services.
Choose Azure when
- Microsoft identity, enterprise agreements, networking and compliance controls drive the purchase.
- Foundry’s models, agents, APIs and tools are more valuable than a bare GPU VM.
- You need access to Microsoft, OpenAI, Meta and Cohere foundation models through an existing Azure relationship.
A practical pilot that avoids a misleading winner
- Freeze the workload: choose one representative model, dataset slice, precision, batch size and success metric.
- Request capacity: ask each provider for the same GPU count, region, topology, purchase mode and maximum run length.
- Run a warm-up: measure startup time, image pull, data staging and first successful training or inference request.
- Measure useful output: record tokens per second, samples per second, time to target loss, latency, throughput and GPU utilization.
- Test failure recovery: force a worker interruption or restart, then measure checkpoint restore time and lost work; this is essential before using spot capacity.
- Price the complete run: add CPU, storage, transfer, platform services, support and engineering hours to the accelerator total.
- Check exit costs: export the image, model, checkpoints and infrastructure definitions, and document what depends on proprietary APIs.
Use the pilot result as a workload-specific decision, not a universal ranking. A provider that wins on a single-node inference test may lose on a 512-GPU training job, while a hyperscaler that wins on scale may be unnecessarily expensive for a small fine-tuning service.
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- Comparing unlike GPUs: H100, H200, A100, L40S and MI300X differ in memory, software support and performance. Compare completed work, not just hourly dollars.
- Ignoring idle and orchestration time: queueing, provisioning, data staging and failed jobs are paid time even when the accelerator is not computing.
- Assuming spot is safe: use frequent checkpoints, resumable data loaders and automated requeueing before moving a long run to interruptible capacity.
- Forgetting transfer and storage: a low compute rate can be overwhelmed by repeated checkpoint copies, cross-region reads or long-lived disks.
- Building on a retiring Azure SKU: review the current VM page and migration guidance before approving an H-series or NC-series design.
- Treating vendor performance claims as benchmarks: AWS’s four-times-performance and 40%-lower-cost figures are vendor claims; reproduce them on your own model and configuration.
- Locking into managed APIs too early: retain an open-model or containerized fallback when portability, auditability or future pricing leverage matters.
Bottom line
Crusoe is the clearest starting point when you want transparent AI-oriented GPU pricing, current accelerator choices and a choice between managed services and direct cluster control. AWS is the scale-and-networking choice for demanding distributed training and mature enterprise integration. Google Cloud is strongest when Gemini, Model Garden, data and MLOps form one product workflow. Azure is strongest when Foundry and Microsoft enterprise integration determine the buying decision. Obtain region-specific, workload-specific quotes and validate them with a pilot before declaring any platform the price-performance winner.
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.
