Independent comparison Updated April 2026 20 GPU providers tested Real hourly pricing

GPU cloud comparison · 2026

C

Crusoe vs Lambda Labs

λ

Crusoe wins on 4 of 5 key metrics — but the right choice depends on your workload.

Overall Winner
C
Crusoe
Climate-aligned GPU cloud — H100, H200, B200 and MI300X on green energy
from $0.40/h
★★★★☆ 4.4 / 5 (412 reviews)
Try Crusoe →
VS
λ
Lambda Labs
On-demand H100 clusters — developer-favourite for serious ML
from $1.10/h
★★★★★ 4.5 / 5 (1,872 reviews)
Try Lambda Labs →

Head-to-Head Comparison

C Crusoe
λ Lambda Labs
Starting Price Lower hourly rate
from $0.40/h
from $1.10/h
Overall Rating User rating
4.4 / 5
4.5 / 5
GPU Types Variety
6 types
4 types
Max VRAM Largest available
192 GB
80 GB
Locations Regions covered
US, Iceland
US, AU
Wins out of 5
4
1

GPU Availability

C Crusoe
H100H200B200A100 80GBL40SMI300X

VRAM: 48–192 GB · Locations: US, Iceland

λ Lambda Labs
A100 40GBA100 80GBH100A10

VRAM: 24–80 GB · Locations: US, AU

Pros & Cons

C Crusoe
Pros
  • Among the cheapest H200 access — from $2.10/h
  • B200 availability while most clouds wait-list
  • InfiniBand 3.2 Tb interconnects for serious multi-node
  • Climate-positive operations (uses flared methane)
Cons
  • Smaller GPU variety than RunPod
  • Region selection limited (mostly US + Iceland)
  • Sales-led for large deployments
λ Lambda Labs
Pros
  • Reliable on-demand H100 availability
  • No complex setup — SSH ready in seconds
  • Lambda Stack saves setup time
  • Competitive pricing vs hyperscalers
Cons
  • Limited GPU types vs RunPod
  • Fewer EU datacenter options
  • No serverless endpoints

Which Should You Choose?

C Choose Crusoe if…
  • You need GPU compute for LLM training at scale
  • You need GPU compute for Multi-node H100/H200 jobs
  • You need GPU compute for Sustainable AI workloads
  • You need GPU compute for AMD MI300X clusters
  • Lower price is your top priority (from $0.40/h vs from $1.10/h)
  • You want more GPU variety (6 vs 4 types)
λ Choose Lambda Labs if…
  • You need GPU compute for LLM training
  • You need GPU compute for Research
  • You need GPU compute for Fine-tuning
  • You need GPU compute for Multi-GPU jobs
  • Higher user satisfaction matters (4.5 vs 4.4)