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Cerebrium vs Lambda Labs: GPU Compute Price Comparison

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

Provider Overview

Provider type
Specialist
Specialist
Founded
2022
2012
Headquarters
Cape Town, South Africa
San Francisco, CA
Billing model
Per-second usage
On-demand, Reserved (1yr/3yr)
Min commitment
None
None (on-demand)
Support tier
Standard
Community → Enterprise
Regions
2 regions
5 regions

Strengths & Best For

Cerebrium

Cerebrium is a serverless ML infrastructure platform that deploys H100, A100, and T4 GPU workloads in seconds using custom containers, enabling real-time LLM inference and fine-tuned model serving without managing any infrastructure. Per-second billing and fast cold starts make it highly cost-efficient for bursty AI inference APIs and model deployment pipelines. A top choice for ML teams that want to ship production inference endpoints quickly with minimal DevOps overhead.

Strengths
  • Serverless deployment
  • Fast cold starts
  • Custom containers
  • Simple pricing
Best For
Real-time inference APIsModel deploymentServerless AI
Visit Cerebrium
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
us-east-1us-west-1us-west-3eu-central-1ap-south-1

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