Provider Comparison

Crusoe vs FluidStack

Q: Which provider offers better spot instance pricing?

Both Crusoe and FluidStack 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?

Crusoe bills per-hour, while FluidStack bills per-minute. Consider your typical workload duration when evaluating which billing model offers better value for your use case.

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

Crusoe holds SOC 2, GDPR certifications. FluidStack holds SOC 2, 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?

Neither provider offers built-in Jupyter notebook support, so you'll need to set up your own development environment. Both providers support SSH access, allowing you to install JupyterLab or other tools on your instances.

Q: Which provider has better Kubernetes support for orchestration?

Both Crusoe and FluidStack 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?

Crusoe is best suited for Organizations with strict ESG mandates; Batch training workloads where carbon footprint is a key metric. FluidStack excels at Large-scale training runs requiring massive, immediate capacity; Global reach for GPU resources. 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 Crusoe and FluidStack 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 Crusoe and FluidStack offer enterprise support tiers with dedicated assistance, faster response times, and potentially custom SLAs.

Q: Which provider has better API and automation support?

Both Crusoe and FluidStack 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 Crusoe and FluidStack 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.

Crusoe and FluidStack represent distinct approaches in the GPU cloud market for ML/AI workloads. Crusoe is a climate-aligned provider that leverages stranded energy sources—such as flared natural gas—for high-performance computing, emphasizing environmental sustainability. This vertically integrated model from energy to cloud appeals to organizations with strict ESG mandates, particularly for batch training where carbon footprint metrics are critical. Its smaller geographic footprint limits options compared to hyperscalers, but it offers per-hour billing with spot instances and complies with SOC 2 and GDPR. In contrast, FluidStack operates as a supercloud aggregator, pooling spare GPU capacity from Tier 1-4 data centers worldwide into a unified interface. This enables massive, immediate scalability for large-scale training, with global reach minimizing latency issues. However, consistency can vary across underlying facilities. Billing is per-minute with spot instances, and it holds SOC 2 and ISO 27001 compliance. Key differentiators include Crusoe's sustainability focus and energy efficiency versus FluidStack's aggregation for rapid, expansive resource access. Crusoe suits eco-conscious teams prioritizing predictable, green batch jobs, while FluidStack targets high-volume users needing burst capacity without vendor lock-in. Both provide spot pricing for cost savings, but FluidStack's finer billing granularity benefits short runs. For ML engineers, Crusoe offers reliable ESG-aligned performance, whereas FluidStack excels in flexibility and scale, though with potential variability. Overall, choice hinges on sustainability needs versus global throughput demands.

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Our Recommendation

Choose Crusoe for teams with ESG compliance requirements, such as enterprises in regulated industries (e.g., finance, healthcare) running batch training or inference on mid-sized clusters (4-64 GPUs). It's ideal for budgets emphasizing long-term per-hour commitments and carbon tracking, with smaller teams (5-20 engineers) valuing predictable availability over massive scale. Opt for FluidStack when scaling large LLM training (100+ GPUs) or needing immediate global capacity for distributed teams. It's suited for startups or research labs with variable workloads, bursty budgets leveraging per-minute spot pricing, and technical setups requiring low-latency multi-region inference. Avoid Crusoe if geographic diversity or sub-hour billing is essential; skip FluidStack for consistency-sensitive production where facility variability matters. For hybrid needs, evaluate spot pricing trials first.

Live Pricing

Compare real-time GPU offers from Crusoe and FluidStack

18 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
Crusoe	NVIDIA A40 48GB VRAM	48GB	0 vCPU 0GB RAM	United States	$0.90/GPU/hr
Crusoe	AMD Instinct MI300X 192GB VRAM	192GB	0 vCPU 0GB RAM	United States	$0.95/GPU/hr
Crusoe	NVIDIA A100 PCIe 40GB 40GB VRAM	40GB	0 vCPU 0GB RAM	United States	$1.00/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

Crusoe

United States

NVIDIA A40

48GB VRAM

0 vCPU

0GB RAM

$0.90/GPU/hr

Crusoe

United States

AMD Instinct MI300X

192GB VRAM

0 vCPU

0GB RAM

$0.95/GPU/hr

Crusoe

United States

NVIDIA A100 PCIe 40GB

40GB VRAM

0 vCPU

0GB RAM

$1.00/GPU/hr

View all 18 offers

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

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

Feature Comparison

Access Methods

Feature	Crusoe	FluidStack
SSH
Jupyter Notebooks
Web Terminal
API
Kubernetes
Containers

Billing Options

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

Compliance

Certification	Crusoe	FluidStack
SOC 2
HIPAA
GDPR
ISO 27001

Support

Feature	Crusoe	FluidStack
SLA
Enterprise Support
Discord Community

Pricing Analysis

Pricing Overview

Crusoe employs per-hour billing for on-demand and spot instances, aligning with longer-running batch workloads common in ML training. This model minimizes overhead for sustained jobs (e.g., multi-day trainings) but incurs costs for idle time in shorter experiments. Spot instances provide discounts during low-demand periods from stranded energy variability. FluidStack's per-minute billing offers greater granularity, ideal for dynamic usage like fine-tuning or inference spikes, reducing waste on partial hours. Both support spot pricing for up to 70-90% savings, but lack reserved instances in standard offerings. Implications: Crusoe favors predictable, hours-long runs with lower admin for billing reconciliation; FluidStack suits interruptible, sub-hour tasks or autoscaling, though spot preemption risks are higher due to aggregated spare capacity. For 24/7 inference, per-minute edges out for precision, while per-hour simplifies budgeting for fixed training schedules.

Value Assessment

For small experiments or fine-tuning (1-8 GPUs, <1 hour), FluidStack delivers superior value via per-minute spot pricing, avoiding full-hour charges and enabling rapid iterations at 50-80% lower effective costs than Crusoe's minimums. Large training runs (64+ GPUs, days-long) favor Crusoe's per-hour stability and energy efficiency, potentially undercutting FluidStack on total cost when spots align with stranded energy availability, especially for ESG-bonus credits. Production inference benefits FluidStack's global pooling for on-demand scaling and latency optimization, offering better ROI for variable traffic. Crusoe edges batch inference with consistent green pricing. Overall, FluidStack wins for flexibility/bursts (e.g., 20-40% savings on shorts), Crusoe for sustained batches (10-25% edge on predictability). Test spot yields via trials for accurate TCO.

Technical Comparison

Infrastructure

Infrastructure comparison information not available.

Performance

Performance comparison information not available.

Frequently Asked Questions

Which provider offers better spot instance pricing?▾

Both Crusoe and FluidStack 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?▾

Crusoe bills per-hour, while FluidStack bills per-minute. Consider your typical workload duration when evaluating which billing model offers better value for your use case.

Which provider has better compliance certifications for enterprise use?▾

Crusoe holds SOC 2, GDPR certifications. FluidStack holds SOC 2, 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?▾

Neither provider offers built-in Jupyter notebook support, so you'll need to set up your own development environment. Both providers support SSH access, allowing you to install JupyterLab or other tools on your instances.

Which provider has better Kubernetes support for orchestration?▾

Both Crusoe and FluidStack 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?▾

Crusoe is best suited for Organizations with strict ESG mandates; Batch training workloads where carbon footprint is a key metric. FluidStack excels at Large-scale training runs requiring massive, immediate capacity; Global reach for GPU resources. 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 Crusoe and FluidStack 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 Crusoe and FluidStack offer enterprise support tiers with dedicated assistance, faster response times, and potentially custom SLAs.

Which provider has better API and automation support?▾

Both Crusoe and FluidStack 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 Crusoe and FluidStack 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?▾

Crusoe's standout features include: Vertically integrated energy-to-cloud model; Use of stranded energy sources. FluidStack's standout features include: Supercloud architecture pooling global resources; Aggregation of spare capacity from Tier 1-4 data centers. 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 Crusoe, visit their website at https://crusoe.ai?utm_source=gpuperhour&utm_medium=referral to create an account and explore available GPU options. For FluidStack, visit https://www.fluidstack.io?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.