Provider Comparison

FluidStack vs Hyperstack

Q: Which provider offers spot instances for cost savings?

FluidStack 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, FluidStack would be the better choice.

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

FluidStack bills per-minute, while Hyperstack bills per-minute. Both providers use the same billing granularity, so this factor won't differentiate your decision.

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

FluidStack holds SOC 2, ISO 27001 certifications. Hyperstack holds GDPR, ISO 27001 certifications. Both providers have similar compliance postures. Check with each provider directly for the most current certification status and specific compliance documentation.

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

Hyperstack offers built-in Jupyter notebook support for interactive development, while FluidStack requires you to set up your own notebook environment. If quick iteration and experimentation are priorities, Hyperstack's integrated notebooks provide a smoother experience.

Q: Which provider has better Kubernetes support for orchestration?

Both FluidStack 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?

FluidStack is best suited for Large-scale training runs requiring massive, immediate capacity; Global reach for GPU resources. 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 FluidStack 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 FluidStack and Hyperstack offer enterprise support tiers with dedicated assistance, faster response times, and potentially custom SLAs.

Q: Which provider has better API and automation support?

Both FluidStack 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?

FluidStack 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.

FluidStack and Hyperstack represent distinct approaches in the GPU cloud market for AI/ML workloads. FluidStack operates as a supercloud aggregator, unifying access to vast GPU resources across global data centers, including Tier 1-4 facilities. This positions it ideally for ML teams needing immediate, massive-scale capacity for large training runs, leveraging spot instances and spare capacity for cost efficiency. Its global reach minimizes procurement delays, though consistency can vary by underlying provider. Compliance includes SOC 2 and ISO 27001, suiting broad enterprise needs. Hyperstack, conversely, emphasizes sustainable, enterprise-grade infrastructure powered by 100% renewable energy, targeting European enterprises prioritizing GDPR compliance and green computing. Its AI Studio streamlines generative AI workflows, offering reliable performance for regulated environments. Both bill per-minute, but FluidStack's spot options provide flexibility absent in Hyperstack's model. Key differentiators: FluidStack excels in scale and agility via aggregation, ideal for dynamic, high-volume users; Hyperstack prioritizes sustainability, compliance, and workflow tools for steady, ethics-focused operations. Value propositions hinge on priorities—FluidStack for cost-optimized bursts globally, Hyperstack for verifiable green credentials and EU-centric reliability. ML engineers should weigh scalability needs against regulatory and environmental mandates when evaluating these providers.

Visit FluidStack Visit Hyperstack

Our Recommendation

Choose FluidStack for large-scale, global ML projects requiring rapid access to thousands of GPUs, such as multi-week training runs on teams of 10+ engineers with flexible budgets. Its spot instances suit bursty workloads, offering 30-70% savings over on-demand, ideal for startups or research labs tolerating occasional variability. Opt for Hyperstack in European enterprise settings (e.g., finance, healthcare) needing GDPR adherence, sustainability reporting, and AI Studio for streamlined fine-tuning or inference pipelines. It's preferable for mid-sized teams (5-20) with steady usage and premium budgets prioritizing reliability over raw scale. For hybrid needs, FluidStack's aggregation provides broader GPU variety (A100/H100), while Hyperstack suits eco-conscious, compliant deployments without global latency concerns.

Live Pricing

Compare real-time GPU offers from FluidStack and Hyperstack

36 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×)	Sold Out

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

Sold Out

NVIDIA RTX A40004x

16GB VRAM

16 vCPU

86GB RAM

500GB Storage

$0.15/GPU/hr

$0.60/hr total (4×)

View all 36 offers

FluidStack(Est. 2017)

A supercloud aggregator providing a unified interface to vast GPU resources from global data centers.

Best For

Large-scale training runs requiring massive, immediate capacityGlobal reach for GPU resources

Unique Features

Supercloud architecture pooling global resources
Aggregation of spare capacity from Tier 1-4 data centers

Limitations

Consistency may vary depending on underlying facility

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	FluidStack	Hyperstack
SSH
Jupyter Notebooks
Web Terminal
API
Kubernetes
Containers

Billing Options

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

Compliance

Certification	FluidStack	Hyperstack
SOC 2
HIPAA
GDPR
ISO 27001

Support

Feature	FluidStack	Hyperstack
SLA
Enterprise Support
Discord Community

Pricing Analysis

Pricing Overview

Both providers use per-minute billing, enabling fine-grained cost control for variable workloads unlike coarser per-hour models. FluidStack differentiates with spot instances, auctioning spare capacity at discounts up to 70%, alongside on-demand options—no reserved instances noted. Hyperstack focuses on per-minute on-demand, lacking explicit spot or reserved tiers, implying stable but potentially higher baseline pricing. Implications: Spot suits fault-tolerant, interruptible jobs like training with checkpoints, reducing costs for sporadic use; on-demand favors production inference needing 99.9% uptime. Short experiments (<1 hour) benefit equally from per-minute granularity, but long runs amplify FluidStack's spot savings. Budget predictability is higher with Hyperstack, while FluidStack risks interruptions but offers elasticity for scaling experiments.

Value Assessment

FluidStack delivers superior value for large training runs and experimentation, where spot pricing slashes costs for 100+ GPU clusters—e.g., $0.50-$1.50/hr per A100 equivalent vs. $2-3 on-demand peers. It's optimal for small teams prototyping burstily. Hyperstack provides better value for production inference and batch jobs in compliant setups, with renewable energy offsetting ESG costs and AI Studio accelerating workflows, potentially saving dev time. For steady mid-scale use (8-64 GPUs), its reliability avoids spot eviction overheads. Small experiments favor FluidStack's spots; real-time inference leans Hyperstack for uptime. Overall, FluidStack wins on cost/scale, Hyperstack on sustainability and enterprise TCO.

Use Case Comparison

LLM Training

FluidStack recommended

FluidStack

FluidStack shines for massive LLM training, pooling global spare capacity for instant 1000+ GPU clusters. Spot instances cut costs 50-70% for multi-day runs with checkpointing, minimizing procurement time across regions. Drawback: potential variability in interconnects or uptime from aggregated DCs.

Hyperstack

Hyperstack supports reliable training with renewable-powered clusters and AI Studio optimizations, suiting GDPR-regulated teams. Lacks spot pricing, so higher costs for prolonged runs; strong for mid-scale (up to 256 GPUs) with consistent performance.

Batch Inference

Either works

FluidStack

FluidStack's vast inventory enables quick scaling for high-volume batch jobs, with spot options ideal for non-urgent throughput. Global DCs reduce latency for distributed data; consistency risks may require monitoring.

Hyperstack

Hyperstack offers predictable inference via enterprise-grade setups and AI Studio, perfect for scheduled batches in sustainable ops. Per-minute billing fits variable loads, with GDPR aiding data-heavy EU workflows.

Real-time Inference

Hyperstack recommended

FluidStack

FluidStack provides on-demand GPUs for low-latency serving, but spot variability suits less for strict SLAs. Global reach aids multi-region deployments; aggregation may introduce networking inconsistencies.

Hyperstack

Hyperstack excels with reliable, renewable infrastructure for production serving, AI Studio easing deployment. GDPR compliance and stability prioritize it for enterprise real-time apps with uptime demands.

Fine-tuning & Experimentation

FluidStack recommended

FluidStack

Ideal for rapid iterations via spot access to diverse GPUs (A100/H100), enabling small-to-medium clusters cheaply. Aggregation accelerates testing across configs without commitments.

Hyperstack

AI Studio streamlines fine-tuning workflows sustainably; per-minute suits short runs, but lacks spots for deepest discounts. Best for compliant, iterative enterprise experimentation.

Technical Comparison

Infrastructure

FluidStack's supercloud aggregates bare-metal and virtualized GPUs from global Tier 1-4 DCs, offering unified APIs, high-bandwidth networking (up to 400Gbps inferred), NVLink/RoCE support, and storage via underlying providers (e.g., NFS/S3-compatible). Kubernetes integration likely via host configs. Hyperstack delivers dedicated enterprise bare-metal with renewable energy, emphasizing GDPR-secure storage, high-speed InfiniBand, and native Kubernetes/AI Studio for orchestration. FluidStack prioritizes breadth over uniformity; Hyperstack uniformity and green infra.

Performance

FluidStack boasts high GPU availability for scaling to 10k+ units, excelling in multi-node training via global pooling, though performance varies (e.g., interconnect latency). Spot evictions demand resilient apps. Hyperstack ensures consistent enterprise perf with renewable clusters, strong multi-GPU scaling (DGX-like), and AI Studio optimizations; limited global scale but reliable for EU. No public benchmarks differ markedly—FluidStack for raw capacity, Hyperstack for predictable throughput.

Frequently Asked Questions

Which provider offers spot instances for cost savings?▾

FluidStack 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, FluidStack would be the better choice.

What is the minimum billing increment for each provider?▾

FluidStack bills per-minute, while Hyperstack bills per-minute. Both providers use the same billing granularity, so this factor won't differentiate your decision.

Which provider has better compliance certifications for enterprise use?▾

FluidStack holds SOC 2, ISO 27001 certifications. Hyperstack holds GDPR, ISO 27001 certifications. Both providers have similar compliance postures. Check with each provider directly for the most current certification status and specific compliance documentation.

Which provider offers better development tools like Jupyter notebooks?▾

Hyperstack offers built-in Jupyter notebook support for interactive development, while FluidStack requires you to set up your own notebook environment. If quick iteration and experimentation are priorities, Hyperstack's integrated notebooks provide a smoother experience.

Which provider has better Kubernetes support for orchestration?▾

Both FluidStack 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?▾

FluidStack is best suited for Large-scale training runs requiring massive, immediate capacity; Global reach for GPU resources. 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 FluidStack 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 FluidStack and Hyperstack offer enterprise support tiers with dedicated assistance, faster response times, and potentially custom SLAs.

Which provider has better API and automation support?▾

Both FluidStack 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?▾

FluidStack 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?▾

FluidStack's standout features include: Supercloud architecture pooling global resources; Aggregation of spare capacity from Tier 1-4 data centers. 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 FluidStack, visit their website at https://www.fluidstack.io?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.