Provider Comparison

AWS vs Hyperstack

Q: Which provider offers spot instances for cost savings?

AWS offers spot/preemptible instances, which can significantly reduce costs (typically 50-80% off on-demand prices) for interruptible workloads like batch processing and training with checkpoints. Hyperstack does not currently offer spot instances, so all usage is billed at on-demand rates. If cost optimization through spot instances is important for your workflow, AWS would be the better choice.

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

AWS bills per-second, while Hyperstack bills per-minute. 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. Hyperstack holds GDPR, ISO 27001 certifications. For organizations with strict compliance requirements, AWS offers more comprehensive coverage.

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

Both AWS and Hyperstack offer built-in Jupyter notebook support, making it easy to start experimenting without additional setup. This is particularly valuable for data scientists and researchers who prefer interactive development environments. Additionally, AWS offers web-based terminal access for quick debugging.

Q: Which provider has better Kubernetes support for orchestration?

Both AWS and Hyperstack 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. Hyperstack excels at European enterprises requiring GDPR compliance; Sustainable computing initiatives. 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 Hyperstack 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 Hyperstack offer enterprise support tiers with dedicated assistance, faster response times, and potentially custom SLAs. Regarding SLAs: AWS offers SLA guarantees (99.99% uptime); Hyperstack has no published SLA.

Q: Which provider has better API and automation support?

Both AWS and Hyperstack 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?

AWS offers native container support for running Docker images, while Hyperstack may require additional configuration. Container support is valuable for reproducible ML pipelines and easy deployment of pre-built environments.

AWS and Hyperstack represent contrasting approaches in GPU cloud infrastructure for ML/AI workloads. AWS, the market leader, offers unparalleled ecosystem integration, with GPUs deeply embedded in services like EC2 P5 instances (H100s), SageMaker for end-to-end ML pipelines, and proprietary chips like Trainium2 for cost-efficient training. It's ideal for enterprises needing global scale, multi-AZ redundancy, and compliance across SOC 2, HIPAA, GDPR, and ISO 27001. However, its pricing complexity, including data egress fees, and higher baseline costs can deter cost-sensitive users. Hyperstack positions itself as a sustainable alternative, powering all operations with 100% renewable energy and targeting European enterprises prioritizing GDPR and eco-friendly computing. Its AI Studio streamlines generative AI workflows, with per-minute billing simplifying short jobs. While enterprise-grade, it lacks AWS's breadth in managed services and global footprint, potentially limiting it to regionally focused or sustainability-driven teams. Key differentiators include AWS's maturity in large-scale orchestration (e.g., spot instances for 90% savings) versus Hyperstack's green credentials and simpler pricing. AWS suits complex, production-grade deployments; Hyperstack appeals for ethical, compliant experimentation. Value depends on scale: AWS excels in cost optimization for sustained loads, while Hyperstack offers transparency for variable usage. ML engineers should weigh integration needs against sustainability and regional compliance.

Visit AWS Visit Hyperstack

Our Recommendation

Choose AWS for large-scale enterprises (100+ team members) running production ML pipelines, requiring global redundancy, SageMaker integration, or specialized hardware like Trainium for trillion-parameter models. It's optimal for budgets over $10K/month where spot instances and per-second billing yield 70-90% savings on long training runs, despite higher on-demand rates and egress fees. Opt for Hyperstack if your team (under 50) prioritizes GDPR compliance, sustainability (100% renewable), or European data residency. It's better for mid-sized budgets ($1-5K/month) with bursty workloads, as per-minute billing avoids AWS's idle-time charges. Avoid Hyperstack for latency-sensitive global apps lacking its infrastructure depth. Hybrid approaches—AWS for core training, Hyperstack for compliant inference—may suit regulated industries balancing cost, ethics, and performance.

Live Pricing

Compare real-time GPU offers from AWS and Hyperstack

56 offers available

Provider	GPU Model	VRAM	Host Specs	Region	Price	Status
Hyperstack	8×NVIDIA RTX A4000 16GB VRAM	16GB	32 vCPU 172GB RAM 900GB Storage	Norway	$0.15/GPU/hr $1.20/hr total (8×)	Sold Out
Hyperstack	2×NVIDIA RTX A4000 16GB VRAM	16GB	8 vCPU 43GB RAM 200GB Storage	Norway	$0.15/GPU/hr $0.30/hr total (2×)	Available
Hyperstack	NVIDIA RTX A4000 16GB VRAM	16GB	4 vCPU 21GB RAM 100GB Storage	Norway	$0.15/GPU/hr	Available
Hyperstack	10×NVIDIA RTX A4000 16GB VRAM	16GB	56 vCPU 215GB RAM 1300GB Storage	Norway	$0.15/GPU/hr $1.50/hr total (10×)	Sold Out
Hyperstack	4×NVIDIA RTX A4000 16GB VRAM	16GB	16 vCPU 86GB RAM 500GB Storage	Norway	$0.15/GPU/hr $0.60/hr total (4×)	Available

Hyperstack

Norway

Sold Out

NVIDIA RTX A40008x

16GB VRAM

32 vCPU

172GB RAM

900GB Storage

$0.15/GPU/hr

$1.20/hr total (8×)

Hyperstack

Norway

Available

NVIDIA RTX A40002x

16GB VRAM

8 vCPU

43GB RAM

200GB Storage

$0.15/GPU/hr

$0.30/hr total (2×)

Hyperstack

Norway

Available

NVIDIA RTX A4000

16GB VRAM

4 vCPU

21GB RAM

100GB Storage

$0.15/GPU/hr

Hyperstack

Norway

Sold Out

NVIDIA RTX A400010x

16GB VRAM

56 vCPU

215GB RAM

1300GB Storage

$0.15/GPU/hr

$1.50/hr total (10×)

Hyperstack

Norway

Available

NVIDIA RTX A40004x

16GB VRAM

16 vCPU

86GB RAM

500GB Storage

$0.15/GPU/hr

$0.60/hr total (4×)

View all 56 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

Hyperstack(Est. 2021)

A provider focused on sustainable, enterprise-grade GPU acceleration using 100% renewable energy.

Best For

European enterprises requiring GDPR complianceSustainable computing initiatives

Unique Features

100% renewable energy
AI Studio for generative AI workflows

Feature Comparison

Access Methods

Feature	AWS	Hyperstack
SSH
Jupyter Notebooks
Web Terminal
API
Kubernetes
Containers

Billing Options

Feature	AWS	Hyperstack
Billing Increment	per-second	per-minute
Spot Instances
Reserved Instances
Prepaid Credits

Compliance

Certification	AWS	Hyperstack
SOC 2
HIPAA
GDPR
ISO 27001

Support

Feature	AWS	Hyperstack
SLA
Enterprise Support
Discord Community

Pricing Analysis

Pricing Overview

AWS employs per-second billing for on-demand and reserved instances, with spot instances offering up to 90% discounts for interruptible workloads—ideal for fault-tolerant training. Pricing tiers include savings plans (1-3 year commitments, 40-70% off) and complex add-ons like data transfer out ($0.09/GB beyond free tier). This granularity favors long-running jobs but penalizes frequent starts/stops via minimum charges. Hyperstack uses per-minute billing, simpler for short experiments but less granular than AWS's seconds, potentially inflating costs for sub-minute tasks. No spot equivalents mentioned, so on-demand dominates; lacks reserved options in available data. Implications: AWS optimizes variable, high-volume usage (e.g., CI/CD pipelines), while Hyperstack suits predictable, minute-aligned sessions without egress surprises. Teams must model total cost of ownership, factoring AWS's ecosystem savings against Hyperstack's billing predictability.

Value Assessment

For small experiments (<1 hour), Hyperstack provides better value via per-minute billing and no complex tiers, avoiding AWS's effective minimums—cost savings of 20-30% for sporadic fine-tuning. Large training runs (days-long) favor AWS spot instances, slashing H100 costs from $30+/hr on-demand to $3-5/hr, unmatched by Hyperstack's flat model. Production inference benefits AWS's per-second granularity and global edge locations for low-latency scaling. Hyperstack edges sustainability-focused inference with AI Studio efficiencies, but lacks proven multi-region SLAs. Overall, AWS delivers superior value (>50% savings) for volumes >100 GPU-hours/month; Hyperstack wins for eco-compliant, low-volume (<50 hours) where simplicity trumps optimization depth. Benchmark TCO with tools like AWS Pricing Calculator.

Technical Comparison

Infrastructure

Infrastructure comparison information not available.

Performance

Performance comparison information not available.

Frequently Asked Questions

Which provider offers spot instances for cost savings?▾

AWS offers spot/preemptible instances, which can significantly reduce costs (typically 50-80% off on-demand prices) for interruptible workloads like batch processing and training with checkpoints. Hyperstack does not currently offer spot instances, so all usage is billed at on-demand rates. If cost optimization through spot instances is important for your workflow, AWS would be the better choice.

What is the minimum billing increment for each provider?▾

AWS bills per-second, while Hyperstack bills per-minute. 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. Hyperstack holds GDPR, ISO 27001 certifications. For organizations with strict compliance requirements, AWS offers more comprehensive coverage.

Which provider offers better development tools like Jupyter notebooks?▾

Both AWS and Hyperstack offer built-in Jupyter notebook support, making it easy to start experimenting without additional setup. This is particularly valuable for data scientists and researchers who prefer interactive development environments. Additionally, AWS offers web-based terminal access for quick debugging.

Which provider has better Kubernetes support for orchestration?▾

Both AWS and Hyperstack 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. Hyperstack excels at European enterprises requiring GDPR compliance; Sustainable computing initiatives. 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 Hyperstack 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 Hyperstack offer enterprise support tiers with dedicated assistance, faster response times, and potentially custom SLAs. Regarding SLAs: AWS offers SLA guarantees (99.99% uptime); Hyperstack has no published SLA.

Which provider has better API and automation support?▾

Both AWS and Hyperstack 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?▾

AWS offers native container support for running Docker images, while Hyperstack may require additional configuration. Container support is valuable for reproducible ML pipelines and easy deployment of pre-built 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. Hyperstack's standout features include: 100% renewable energy; AI Studio for generative AI workflows. 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 Hyperstack, visit https://www.hyperstack.cloud?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.