Crusoe vs Lambda Labs 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.
Lambda Labs
https://lambdalabs.com
Lambda Labs (also known as Lambda) is a cloud computing and hardware company specializing in GPU-based infrastructure for AI and machine learning workloads. The company offers on-demand and reserved GPU cloud instances, as well as on-premise GPU servers and workstations, designed for training and deploying deep learning models. Lambda serves researchers, startups, and enterprises seeking high-performance compute at competitive pricing compared to hyperscale cloud providers.
Quick Comparison
| Detail | Crusoe | Lambda Labs |
|---|---|---|
| Category | AI Cloud Infrastructure | AI Cloud Infrastructure |
| Starting Price | Contact Sales | $496.8/mo |
| Plans Available | 5 | 9 |
| Features Tracked | 17 | 15 |
| Founded | 2018 | 2012 |
| Headquarters | San Francisco, USA | San Francisco, USA |
Features
Detailed feature-by-feature comparison
Feature Comparison
| Feature | | |
|---|---|---|
| api | ||
| API Monitoring | ||
| core | ||
| 1-Click Clusters | ||
| 99.98% Uptime | ||
| AMD Compute | ||
| Accelerated Storage | ||
| Bare Metal Instances | ||
| Block Storage | ||
| Crusoe AutoClusters | ||
| Elastic Scaling | ||
| GPU Instances | ||
| Lambda Stack | ||
| Managed Kubernetes | ||
| MemoryAlloy Technology | ||
| NVIDIA GPUs | ||
| NVIDIA InfiniBand | ||
| No Egress Fees | ||
| Optimized Networking | ||
| Pay by the Minute | ||
| Private Cloud | ||
| Superclusters | ||
| Sustainable Energy | ||
| Zero Throttling | ||
| integration | ||
| Git Integration | ||
| JupyterLab Support | ||
| Multi-Cloud Support | ||
| security | ||
| Biometric Access | ||
| SSO Support | ||
| Single-Tenant Clusters | ||
| VPC Installs | ||
| support | ||
| 24/7 Support | ||
| Cost Tracking | ||
| Dashboard 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.
Lambda Labs
From $496.8/mo
Price Components
- GPU Hour: $9.86/hour
- Reserved Capacity: $0/cluster
- GPU Hour: $6.16/hour
- GPU Hour: $6.69/hour
- GPU Hour: $3.99/hour
Best For
ML researchers and startups running large-scale distributed training jobs who prioritize cost efficiency and hardware control over managed service breadth.
Integrations
See which third-party services are supported
Supported Integrations
Coming Soon
Integration comparison data for Crusoe, Lambda Labs 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.
Lambda Labs
ML researchers and startups running large-scale distributed training jobs who prioritize cost efficiency and hardware control over managed service breadth.
- Per-second billing with no egress fees undercuts hyperscale providers on total cost of ownership for GPU workloads
- Bare metal access and Quantum-2 InfiniBand networking enable efficient distributed training across hundreds of GPUs
- Lambda Stack pre-installation eliminates environment setup friction, reducing time-to-training from days to minutes
- Smaller scale and regional availability compared to AWS, Google Cloud, and Azure limits enterprise multi-region deployments
- Limited managed services ecosystem; users handle more infrastructure complexity than with hyperscale competitors
Company Info
Company details and background
Crusoe
Lambda Labs
Comparison FAQ
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