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Lambda Labs vs Modal: Pricing, Specs, Strengths & Weaknesses

Side-by-side comparison of GPU compute pricing, regions, billing models, and strengths for Lambda Labs and Modal. Rates sourced from public APIs — verify current availability and billing terms before purchasing.

Overview

Provider type
Specialist
Specialist
Founded
2012
2021
Headquarters
San Francisco, CA
New York, NY
Billing model
On-demand, Reserved (1yr/3yr), Committed clusters
Per-second serverless
Min commitment
None (on-demand Instances)
None
Support tier
Community → Enterprise
Community → Enterprise
Regions
4 regions
3 regions

Strengths & Best For

Lambda Labs

Lambda (formerly Lambda Labs) is a purpose-built AI supercomputer cloud offering NVIDIA Vera Rubin NVL72, GB300 NVL72, HGX B300, HGX B200, and H200 GPU clusters for large-scale training and inference. Superclusters run on single-tenant infrastructure with NVIDIA Quantum-2 InfiniBand; 1-Click Clusters™ provide production-ready B200 and H100 clusters optimized for distributed workloads; on-demand Instances spin up in minutes for prototyping. SOC 2 Type II certified with single-tenant, shared-nothing architecture. Trusted by leading AI labs and enterprises for frontier model training.

Strengths
  • Vera Rubin NVL72 and GB300 NVL72 supercomputers
  • 1-Click Clusters™ for production-ready B200/H100 distributed workloads
  • Single-tenant, shared-nothing architecture
  • SOC 2 Type II, caged clusters with hardware-level isolation
  • Co-engineering support from infrastructure experts
  • No egress fees
Weaknesses
  • Premium pricing vs commodity GPU clouds
  • Sales-led process for large clusters
  • On-demand Instances have limited availability vs spot-heavy providers
Best For
Frontier model training at scaleEnterprise AI labs needing dedicated supercomputersTeams wanting managed clusters with expert co-engineeringGovernment and regulated industry AI workloads
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Lambda Labs Rating

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Modal

Modal is a serverless GPU cloud purpose-built for AI workloads, offering per-second billing with zero idle cost. GPU catalog: B300, B200, H200 SXM, H100 SXM5, RTX PRO 6000, A100 80GB, A100 40GB, L40S, A10, L4, T4. Starter and Team plans include free monthly compute credits. Multi-node training up to 128 B200s with 3,200 Gbps InfiniBand. Python-native SDK. SOC 2 and HIPAA compliant.

Strengths
  • B300, B200, H200 — competitive Blackwell/Hopper pricing
  • Per-second billing with zero idle cost — no minimum hours
  • Multi-node training up to 128 B200s with 3,200 Gbps InfiniBand
  • Python-native SDK — no infra management
  • Starter and Team plans include free monthly compute credits
  • SOC 2, HIPAA, data residency controls
Weaknesses
  • Less control over underlying infrastructure vs raw VMs
  • Pricing can be unpredictable for sustained high-throughput workloads
  • Smaller GPU catalog than dedicated cluster providers
Best For
ML engineers wanting zero-ops GPU infrastructureServerless LLM inference and multi-modal workloadsRL rollouts and AI agent sandboxes at scaleTeams wanting one platform from prototype to production
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Modal Rating

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Live GPU Pricing

GPU Model
Modal /hr
L40S
$1.10
$1.95
A100 40GB
$1.99
$2.10
A100 80GB
$2.79
$2.50
H100 80GB
$3.99
$3.95
B200 192GB
$6.99
$6.25

Green = lower price. On-demand hourly rates. Updated every 15 minutes.

Region Coverage

Lambda Labs — 4 regions
US-EastUS-WestEU-CentralAP-South
Modal — 3 regions
US-EastUS-WestEU

Popular Comparisons

Key differences

Lambda Labs

The Superintelligence Cloud — supercomputers for training and inference

Modal

B300, B200, H200, H100 — serverless GPU, per-second billing

AttributeLambda LabsModal
Provider typespecialistspecialist
Billing modelOn-demand, Reserved (1yr/3yr), Committed clustersPer-second serverless
Min commitmentNone (on-demand Instances)None
Regions4 (US-East, US-West, EU-Central, +1)3 (US-East, US-West, EU)
Support tierCommunity → EnterpriseCommunity → Enterprise
Best forFrontier model training at scale, Enterprise AI labs needing dedicated supercomputersML engineers wanting zero-ops GPU infrastructure, Serverless LLM inference and multi-modal workloads
Founded / HQ2012 · San Francisco, CA2021 · New York, NY