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Shadeform vs Lambda Labs: GPU Compute Price Comparison

Side-by-side comparison of GPU compute pricing, regions, billing models, and strengths for Shadeform and Lambda Labs. Updated July 2026.

Provider Overview

Provider type
Marketplace
Specialist
Founded
2023
2012
Headquarters
San Francisco, CA
San Francisco, CA
Billing model
On-demand, Spot
On-demand, Reserved (1yr/3yr)
Min commitment
None
None (on-demand)
Support tier
Community → Enterprise
Community → Enterprise
Regions
4 regions
5 regions

Strengths & Best For

Shadeform

Shadeform is a GPU cloud aggregator that provisions H100, A100, RTX 4090, and many other GPU types across 30+ underlying cloud providers through a single unified API, automatically routing to the cheapest available instance matching your requirements. On-demand and spot GPU rental options are surfaced from the entire provider network, giving teams multi-cloud flexibility without managing multiple accounts. The fastest way to find and launch the lowest-cost GPU for any AI training or inference workload.

Strengths
  • 30+ provider network
  • Single API
  • Automatic cheapest-price routing
  • Wide GPU selection
Best For
Teams wanting multi-cloud flexibilityCost-optimized provisioningSpot-tolerant workloads
Visit Shadeform
Lambda Labs

Lambda Labs offers on-demand and reserved H100, A100, and RTX A6000 GPU instances with simple flat pricing and no egress fees — a refreshing contrast to hyperscaler complexity. Pre-configured PyTorch and TensorFlow environments mean researchers can start LLM training or fine-tuning in minutes without any setup overhead. A go-to on-demand GPU cloud for ML teams that want predictable hourly GPU rental costs without long-term commitments.

Strengths
  • Simple pricing
  • Pre-configured ML stack
  • No egress fees
  • Jupyter notebooks included
Best For
ML researchersDeep learning trainingTeams wanting simplicity
Visit Lambda Labs

Live GPU Pricing

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Region Coverage

Lambda Labs5 regions
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