Crusoe vs FluidStack Comparison
Detailed comparison of features, pricing, and capabilities
Last updated May 1, 2026
Overview
Compare key metrics and features at a glance
Crusoe
https://www.crusoe.ai
Crusoe is an AI cloud infrastructure company that provides purpose-built cloud computing services optimized for AI workloads, including GPU clusters for training and inference. Originally founded as Crusoe Energy Systems, the company pivoted to focus on sustainable AI cloud computing, leveraging stranded and flared natural gas to power data centers, reducing carbon emissions compared to traditional grid-powered facilities. Crusoe offers high-performance computing resources tailored for machine learning, generative AI, and large-scale model training, positioning itself as an environmentally conscious alternative to hyperscale cloud providers.
FluidStack
https://www.fluidstack.io
FluidStack is a cloud GPU infrastructure provider that aggregates underutilized GPU capacity from data centers worldwide to offer on-demand and reserved GPU compute at competitive prices. The platform enables AI companies, researchers, and developers to access large-scale GPU clusters for training and inference workloads, including support for high-performance interconnects like InfiniBand. FluidStack differentiates itself by sourcing capacity from a distributed network of partner data centers, providing cost-effective alternatives to hyperscale cloud providers for AI/ML workloads.
Quick Comparison
| Detail | Crusoe | FluidStack |
|---|---|---|
| Category | AI Cloud Infrastructure | AI Cloud Infrastructure |
| Starting Price | Contact Sales | Contact Sales |
| Plans Available | 5 | 1 |
| Features Tracked | 17 | 16 |
| Founded | 2018 | 2019 |
| Headquarters | San Francisco, USA | London, United Kingdom |
Features
Detailed feature-by-feature comparison
Feature Comparison
| Feature | | |
|---|---|---|
| core | ||
| 99.98% Uptime | ||
| AMD Compute | ||
| Accelerated Storage | ||
| Crusoe AutoClusters | ||
| Dedicated GPU Clusters | ||
| Elastic Scaling | ||
| Fully Managed Clusters | ||
| H100/H200/B200/GB200 Support | ||
| InfiniBand Interconnects | ||
| Kubernetes Support | ||
| Low-Latency Inference | ||
| Managed Kubernetes | ||
| MemoryAlloy Technology | ||
| NVIDIA GPUs | ||
| Optimized Networking | ||
| Rapid Deployment | ||
| Slurm Support | ||
| Sustainable Energy | ||
| Transparent Pricing | ||
| custom | ||
| Custom Data Centers | ||
| integration | ||
| Distributed Data Access | ||
| Git Integration | ||
| JupyterLab Support | ||
| Multi-Cloud Support | ||
| security | ||
| SSO Support | ||
| Secure Access Controls | ||
| Single-Tenant Isolation | ||
| VPC Installs | ||
| support | ||
| 15-Minute Response SLA | ||
| 24/7 Support | ||
| 99% Uptime SLA | ||
| Cost Tracking | ||
| Proactive Monitoring | ||
Pricing
Compare pricing plans and value for money
Crusoe
Contact Sales
Price Components
- NVIDIA H200 141GB HGX: $4.29/GPU-hour
- NVIDIA H100 80GB HGX: $3.9/GPU-hour
- NVIDIA A100 80GB SXM: $1.95/GPU-hour
- NVIDIA A100 80GB PCIe: $1.65/GPU-hour
- NVIDIA A100 40GB PCIe: $1.45/GPU-hour
Best For
ESG-focused AI teams training massive LLMs or running inference who prioritize sustainable, high-uptime GPU clusters with auto-failover.
FluidStack
Contact Sales
Best For
AI companies and researchers needing rapid, cost-effective, fully managed large-scale dedicated GPU clusters for training without hyperscaler lock-in.
Integrations
See which third-party services are supported
Supported Integrations
Coming Soon
Integration comparison data for Crusoe, FluidStack is being collected and will be available soon.
Strengths & Limitations
Key strengths and limitations of each service
Crusoe
ESG-focused AI teams training massive LLMs or running inference who prioritize sustainable, high-uptime GPU clusters with auto-failover.
- Powers data centers with flare gas and solar for carbon-negative AI computing, slashing emissions versus grid-reliant hyperscalers.
- MemoryAlloy tech delivers 9.9x faster Time-to-First-Token and 5x inference throughput on NVIDIA H100/A100 GPUs.
- AutoClusters auto-remediate GPU failures for 99.98% uptime in elastic, Kubernetes-managed scaling from notebooks to clusters.
- Spot GPU instances and pay-per-1M-token inference offer cost savings over on-demand hyperscale pricing.
- Smaller scale (201-500 employees, Series C) limits global data center footprint versus hyperscalers like AWS or Azure.
- Reliance on stranded energy sources may constrain capacity expansion and geographic availability.
- Enterprise/reserved pricing for GB200/B200 requires custom sales outreach, lacking self-serve transparency.
FluidStack
AI companies and researchers needing rapid, cost-effective, fully managed large-scale dedicated GPU clusters for training without hyperscaler lock-in.
- Rapid deployment of multi-thousand GPU clusters in as little as 48 hours with zero-setup management.
- Single-tenant isolation at hardware, network, and storage levels eliminates noisy neighbors unlike hyperscalers.
- Supports latest NVIDIA H100/H200/B200/GB200 GPUs with InfiniBand and 99% uptime SLA.
- 24/7 engineering support via Slack with 15-minute response times and proactive monitoring.
- Enterprise-only pricing requires contacting sales, lacking transparent pay-as-you-go rates.
- Small team of 11-50 employees and seed funding may limit scalability versus larger competitors.
- Aggregated capacity from partner data centers could introduce variability in global availability.
Company Info
Company details and background
Crusoe
FluidStack
Comparison FAQ
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