Keywords AI

Lambda vs Modal

Compare Lambda and Modal side by side. Both are tools in the Inference & Compute category.

Quick Comparison

Lambda
Lambda
Modal
Modal
CategoryInference & ComputeInference & Compute
PricingUsage-basedUsage-based
Best ForML engineers and researchers who want simple, reliable GPU cloud infrastructurePython developers who want serverless GPU infrastructure without managing containers or Kubernetes
Websitelambdalabs.commodal.com
Key Features
  • NVIDIA GPU cloud instances
  • Pre-configured ML software stack
  • On-demand and reserved pricing
  • Simple API and CLI
  • Multi-GPU cluster support
  • Serverless cloud for AI
  • Python-native container orchestration
  • Auto-scaling GPU infrastructure
  • Pay-per-second billing
  • Built-in web endpoints
Use Cases
  • ML model training and fine-tuning
  • Inference serving
  • Research and experimentation
  • Academic AI computing
  • Startup AI infrastructure
  • Serverless model inference
  • Data processing pipelines
  • Batch jobs with GPU acceleration
  • Development environments with GPUs
  • Auto-scaling AI APIs

When to Choose Lambda vs Modal

Lambda
Choose Lambda if you need
  • ML model training and fine-tuning
  • Inference serving
  • Research and experimentation
Pricing: Usage-based
Modal
Choose Modal if you need
  • Serverless model inference
  • Data processing pipelines
  • Batch jobs with GPU acceleration
Pricing: Usage-based

About Lambda

Lambda provides GPU cloud infrastructure and workstations purpose-built for deep learning. Their cloud platform offers on-demand access to NVIDIA H100 and A100 GPUs with pre-installed ML frameworks. Lambda also sells GPU workstations and servers for on-premises AI development. Known for competitive pricing and developer-friendly tooling, Lambda serves AI researchers and companies needing dedicated GPU compute.

About Modal

Modal is a serverless cloud platform for running AI workloads with zero infrastructure management. Developers write Python code and Modal handles containerization, GPU provisioning, scaling, and scheduling automatically. The platform supports GPU-accelerated functions, scheduled jobs, web endpoints, and batch processing, making it particularly popular for ML pipelines, model serving, and data processing tasks.

What is Inference & Compute?

Platforms that provide GPU compute, model hosting, and inference APIs. These companies serve open-source and third-party models, offer optimized inference engines, and provide cloud GPU infrastructure for AI workloads.

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