Provider Comparison

AWS vs Crusoe

Q: Which provider offers better spot instance pricing?

Both AWS and Crusoe offer spot/preemptible instances, which can reduce costs by 50-80% compared to on-demand pricing. Spot instances are ideal for fault-tolerant workloads like batch inference, hyperparameter tuning, and distributed training with checkpointing. The actual savings depend on current demand and GPU availability, so we recommend comparing real-time spot prices for your specific GPU requirements on both platforms.

Q: What is the minimum billing increment for each provider?

AWS bills per-second, while Crusoe bills per-hour. Per-second billing from AWS offers better cost efficiency for short experiments and iterative development, as you only pay for exactly what you use.

Q: Which provider has better compliance certifications for enterprise use?

AWS holds SOC 2, HIPAA, GDPR, ISO 27001 certifications. Crusoe holds SOC 2, GDPR certifications. For organizations with strict compliance requirements, AWS offers more comprehensive coverage.

Q: Which provider offers better development tools like Jupyter notebooks?

AWS offers built-in Jupyter notebook support for interactive development, while Crusoe requires you to set up your own notebook environment. If quick iteration and experimentation are priorities, AWS's integrated notebooks provide a smoother experience. Additionally, AWS offers web-based terminal access for quick debugging.

Q: Which provider has better Kubernetes support for orchestration?

Both AWS and Crusoe support Kubernetes for container orchestration, enabling you to deploy scalable ML pipelines, manage distributed training jobs, and integrate with MLOps tools like Kubeflow. This is essential for teams running production workloads at scale.

Q: What is each provider best suited for?

AWS is best suited for Large-scale enterprises requiring deep integration with other cloud services; Organizations needing globally redundant availability zones. Crusoe excels at Organizations with strict ESG mandates; Batch training workloads where carbon footprint is a key metric. Understanding these specializations helps you choose the provider that aligns with your primary use case, though both can handle a variety of GPU computing needs.

Q: Which provider offers reserved instances for long-term savings?

Both AWS and Crusoe offer reserved instance pricing for committed usage, typically providing 20-40% discounts compared to on-demand rates. Reserved instances are ideal for predictable, steady-state workloads like always-on inference services. For variable workloads, on-demand or spot instances may offer better flexibility.

Q: Which provider offers better enterprise support?

Both AWS and Crusoe offer enterprise support tiers with dedicated assistance, faster response times, and potentially custom SLAs. Regarding SLAs: AWS offers SLA guarantees (99.99% uptime); Crusoe has no published SLA.

Q: Which provider has better API and automation support?

Both AWS and Crusoe provide APIs for programmatic instance management, enabling automation of provisioning, scaling, and teardown operations. This is essential for integrating GPU resources into CI/CD pipelines and automated ML workflows.

Q: Which provider has better container and Docker support?

Both AWS and Crusoe support containerized workloads, allowing you to deploy Docker images with your ML frameworks, dependencies, and models pre-configured. This ensures reproducibility and simplifies deployment across development, staging, and production environments.

AWS and Crusoe represent contrasting approaches in GPU cloud for ML/AI workloads. AWS, the market leader, offers unparalleled scale, global availability across 30+ regions, and seamless integration with services like SageMaker, EC2 P5 instances (H100 GPUs), and proprietary Trainium/Inferentia chips. It's ideal for enterprises needing managed ML pipelines, compliance (SOC 2, HIPAA, GDPR, ISO 27001), and hybrid workloads. However, its pricing complexity, high on-demand rates, and egress fees can inflate costs. Crusoe differentiates through sustainability, leveraging stranded energy for low-carbon computing, targeting ESG-focused organizations. It provides NVIDIA H100/A100 clusters optimized for batch training, with a vertically integrated energy-to-cloud model reducing operational costs. Geographic footprint is limited (primarily US), lacking AWS's redundancy. Both offer spot instances, but AWS bills per-second while Crusoe uses per-hour. AWS suits complex, production-grade deployments; Crusoe excels in cost-effective, high-intensity training where environmental impact matters. Value hinges on scale needs: AWS for ecosystem lock-in and reliability, Crusoe for green credentials and potential savings on long runs. ML engineers should weigh integration depth against sustainability and regional constraints.

Visit AWS Visit Crusoe

Our Recommendation

Choose AWS for large enterprises (>50 engineers) with global teams, requiring SageMaker for end-to-end ML ops, low-latency inference across regions, or compliance like HIPAA. It's best for budgets tolerating premium pricing ($30-50/hr for H100) in exchange for 99.99% SLAs and Trainium cost savings (up to 50% vs GPUs). Opt for Crusoe if your team (10-50) prioritizes ESG compliance, batch workloads, and US-based ops, especially with budgets under $20/hr for H100 equivalents via efficient energy use. Ideal for startups or research with intermittent large training, leveraging spot instances for 70-90% discounts. Avoid Crusoe for latency-sensitive apps due to limited regions; skip AWS if simplicity and green metrics outweigh ecosystem needs.

Live Pricing

Compare real-time GPU offers from AWS and Crusoe

38 offers available

Provider	GPU Model	VRAM	Host Specs	Region	Price
Crusoe	NVIDIA A40 48GB VRAM	48GB	0 vCPU 0GB RAM	United States	$0.40/GPU/hr
Crusoe	NVIDIA L40S 48GB VRAM	48GB	0 vCPU 0GB RAM	United States	$0.50/GPU/hr
AWS	NVIDIA Tesla T4 16GB VRAM	16GB	4 vCPU 16GB RAM	Virginia	$0.53/GPU/hr
AWS	NVIDIA Tesla T4 16GB VRAM	16GB	8 vCPU 32GB RAM	Virginia	$0.75/GPU/hr
Crusoe	NVIDIA A40 48GB VRAM	48GB	0 vCPU 0GB RAM	United States	$0.90/GPU/hr

Crusoe

United States

NVIDIA A40

48GB VRAM

0 vCPU

0GB RAM

$0.40/GPU/hr

Crusoe

United States

NVIDIA L40S

48GB VRAM

0 vCPU

0GB RAM

$0.50/GPU/hr

AWS

Virginia

NVIDIA Tesla T4

16GB VRAM

4 vCPU

16GB RAM

$0.53/GPU/hr

AWS

Virginia

NVIDIA Tesla T4

16GB VRAM

8 vCPU

32GB RAM

$0.75/GPU/hr

Crusoe

United States

NVIDIA A40

48GB VRAM

0 vCPU

0GB RAM

$0.90/GPU/hr

View all 38 offers

AWS(Est. 2006)

The dominant force in global cloud computing with deep integration of GPUs into its ecosystem for machine learning and other services.

Best For

Large-scale enterprises requiring deep integration with other cloud servicesOrganizations needing globally redundant availability zones

Unique Features

Proprietary silicon like Trainium and Inferentia chips
Fully managed ML development environment with SageMaker

Limitations

High cost relative to specialized clouds
Complexity of pricing including egress fees

Crusoe(Est. 2018)

A climate-aligned computing provider powering high-performance computing using stranded energy sources to mitigate environmental impact.

Best For

Organizations with strict ESG mandatesBatch training workloads where carbon footprint is a key metric

Unique Features

Vertically integrated energy-to-cloud model
Use of stranded energy sources

Limitations

Smaller geographic footprint compared to hyperscalers

Feature Comparison

Access Methods

Feature	AWS	Crusoe
SSH
Jupyter Notebooks
Web Terminal
API
Kubernetes
Containers

Billing Options

Feature	AWS	Crusoe
Billing Increment	per-second	per-hour
Spot Instances
Reserved Instances
Prepaid Credits

Compliance

Certification	AWS	Crusoe
SOC 2
HIPAA
GDPR
ISO 27001

Support

Feature	AWS	Crusoe
SLA
Enterprise Support
Discord Community

Pricing Analysis

Pricing Overview

AWS employs per-second billing for EC2/SageMaker, enabling fine-grained cost control for variable workloads, with spot instances offering 70-90% savings (e.g., P5.48xlarge H100 at ~$32/hr on-demand, <$10 spot). Reserved instances (1-3 years) yield 40-75% discounts, but complex tiers (partial/full upfront) and egress fees ($0.09/GB) add overhead. Crusoe uses per-hour billing, simpler for predictable runs, with spot/preemptible instances at steep discounts (H100 clusters ~$15-25/hr estimated). No reserved options publicly detailed, focusing on on-demand/spot for flexibility. Implications: AWS favors short bursts/experiments (per-sec savings); Crusoe suits steady long jobs (hourly predictability, lower base via energy efficiency). Both lack long-term commitments matching on-prem, but AWS's elasticity suits autoscaling.

Value Assessment

For small experiments/fine-tuning, AWS provides superior value via per-second billing and SageMaker Studio (pay-per-use notebooks), minimizing idle costs vs Crusoe's hourly minimums. Large training runs favor Crusoe: stranded energy lowers effective GPU-hour rates (potentially 20-40% below AWS spot), ideal for 1000+ GPU days where ESG reporting adds ROI. Production inference leans AWS—Trainium/Inferentia cut costs 40-50% for LLMs, with global edge deployment vs Crusoe's batch focus. Hybrid: AWS for dev/test, Crusoe for scale-out training. Overall, Crusoe wins on raw TCO for green batch (if US-centric); AWS for integrated, reliable inference at scale, despite premiums.

Use Case Comparison

LLM Training

Either works

AWS

AWS excels with P5 instances (8x H100 per node, EFA networking for 400Gbps multi-node scaling) and SageMaker for distributed training (up to 1000s GPUs). Trainium2 supports massive pretraining at lower cost/power. Global AZs ensure redundancy; spot fleets handle interruptions via checkpoints. Ideal for production-scale LLMs needing fault-tolerance.

Crusoe

Crusoe's H100 clusters (liquid-cooled, high-density) optimize for large-scale training via stranded energy efficiency, potentially lower $/FLOP. Supports Slurm/Kubernetes for job scheduling. Strong for batch but limited regions risk latency; spot availability good for cost but less mature checkpointing vs AWS.

Batch Inference

Crusoe recommended

AWS

AWS SageMaker Batch Transform and Inferentia/Trainium enable cost-optimized serving (up to 4x throughput vs GPUs). Asynchronous processing scales to petabytes; integrates with S3 for data. Spot instances viable for non-urgent jobs, but egress adds cost.

Crusoe

Crusoe suits high-volume batch with GPU clusters and efficient power usage, lowering costs for offline scoring. Kubernetes orchestration simplifies pipelines. ESG benefits for reporting; hourly billing aligns with job durations, but lacks managed inference services.

Real-time Inference

AWS recommended

AWS

AWS dominates with SageMaker Endpoints, Lambda@Edge, and global Outposts for <100ms latency. Inferentia2 boosts throughput 30%; auto-scaling handles bursts. Multi-AZ HA and API Gateway integration perfect for prod apps.

Crusoe

Crusoe less optimal—focuses on compute over low-latency serving. H100s capable but limited regions/geos increase cold-start risks. No equivalent managed endpoints; requires custom FastAPI/K8s, suiting non-latency-critical apps.

Fine-tuning & Experimentation

AWS recommended

AWS

SageMaker Studio/Jupyter offers per-second GPUs (G5/A10G), hyperparameter tuning, and spot for cheap iterations. Integrates Git/ECR; debugging tools accelerate prototyping for small teams.

Crusoe

Crusoe viable for GPU access via notebooks/clusters, spot for affordability. Simpler setup but lacks SageMaker's ML-specific tools/UI. Good for quick tests if sustainability prioritized, though hourly billing penalizes short runs.

Technical Comparison

Infrastructure

AWS virtualizes via EC2 (Nitro hypervisor), offering GPU instances (P4d/P5 with H100/A100), EBS/GP3 storage, FSx Lustre for HPC, EFA/RDMA networking, and EKS for Kubernetes. Multi-AZ/region HA standard. Crusoe emphasizes dedicated clusters (bare-metal-like H100 pods), Kubernetes-native with Slurm support, object/block storage, but US-focused (Denver, San Antonio DCs). Less virtualization overhead potentially, but narrower storage/network options vs AWS's breadth.

Performance

Both leverage NVIDIA H100/A100; AWS P5 delivers 3.3Tb/s NVLink per node, scales to DGX SuperPOD equivalents with EFA (up to 10k GPUs). Trainium offers custom ML perf. Crusoe matches raw GPU FLOPS with dense racking, claims competitive multi-node scaling via InfiniBand, but limited public benchmarks. AWS edges availability/reliability; Crusoe potentially better power efficiency (stranded energy), suiting sustained training. No major perf gaps reported, but AWS proven at exascale.

Frequently Asked Questions

Which provider offers better spot instance pricing?▾

Both AWS and Crusoe offer spot/preemptible instances, which can reduce costs by 50-80% compared to on-demand pricing. Spot instances are ideal for fault-tolerant workloads like batch inference, hyperparameter tuning, and distributed training with checkpointing. The actual savings depend on current demand and GPU availability, so we recommend comparing real-time spot prices for your specific GPU requirements on both platforms.

What is the minimum billing increment for each provider?▾

AWS bills per-second, while Crusoe bills per-hour. Per-second billing from AWS offers better cost efficiency for short experiments and iterative development, as you only pay for exactly what you use.

Which provider has better compliance certifications for enterprise use?▾

AWS holds SOC 2, HIPAA, GDPR, ISO 27001 certifications. Crusoe holds SOC 2, GDPR certifications. For organizations with strict compliance requirements, AWS offers more comprehensive coverage.

Which provider offers better development tools like Jupyter notebooks?▾

AWS offers built-in Jupyter notebook support for interactive development, while Crusoe requires you to set up your own notebook environment. If quick iteration and experimentation are priorities, AWS's integrated notebooks provide a smoother experience. Additionally, AWS offers web-based terminal access for quick debugging.

Which provider has better Kubernetes support for orchestration?▾

Both AWS and Crusoe support Kubernetes for container orchestration, enabling you to deploy scalable ML pipelines, manage distributed training jobs, and integrate with MLOps tools like Kubeflow. This is essential for teams running production workloads at scale.

What is each provider best suited for?▾

AWS is best suited for Large-scale enterprises requiring deep integration with other cloud services; Organizations needing globally redundant availability zones. Crusoe excels at Organizations with strict ESG mandates; Batch training workloads where carbon footprint is a key metric. Understanding these specializations helps you choose the provider that aligns with your primary use case, though both can handle a variety of GPU computing needs.

Which provider offers reserved instances for long-term savings?▾

Both AWS and Crusoe offer reserved instance pricing for committed usage, typically providing 20-40% discounts compared to on-demand rates. Reserved instances are ideal for predictable, steady-state workloads like always-on inference services. For variable workloads, on-demand or spot instances may offer better flexibility.

Which provider offers better enterprise support?▾

Both AWS and Crusoe offer enterprise support tiers with dedicated assistance, faster response times, and potentially custom SLAs. Regarding SLAs: AWS offers SLA guarantees (99.99% uptime); Crusoe has no published SLA.

Which provider has better API and automation support?▾

Both AWS and Crusoe provide APIs for programmatic instance management, enabling automation of provisioning, scaling, and teardown operations. This is essential for integrating GPU resources into CI/CD pipelines and automated ML workflows.

Which provider has better container and Docker support?▾

Both AWS and Crusoe support containerized workloads, allowing you to deploy Docker images with your ML frameworks, dependencies, and models pre-configured. This ensures reproducibility and simplifies deployment across development, staging, and production environments.

What unique features differentiate these providers?▾

AWS's standout features include: Proprietary silicon like Trainium and Inferentia chips; Fully managed ML development environment with SageMaker. Crusoe's standout features include: Vertically integrated energy-to-cloud model; Use of stranded energy sources. These differentiators may be decisive factors depending on your specific technical requirements and workflow preferences.

How do I get started with each provider?▾

To get started with AWS, visit their website at https://aws.amazon.com?utm_source=gpuperhour&utm_medium=referral to create an account and explore available GPU options. For Crusoe, visit https://crusoe.ai?utm_source=gpuperhour&utm_medium=referral to sign up. Both providers typically offer some form of free credits or trial period for new users. We recommend starting with a small experiment to evaluate the platform's ease of use, instance launch times, and overall fit for your workflow before committing to larger workloads.