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Modal vs Tensorlake

A side-by-side comparison of Modal and Tensorlake, drawn from Ignaite's continuously-verified listings.

Compared from listings verified as of

Modal

Inference

Serverless GPUs. Run training, inference, batch jobs from Python.

View Modal

Tensorlake

Infra

Sandbox-native cloud for AI agents.

View Tensorlake

At a glance

Feature comparison of Modal and Tensorlake
AttributeModalTensorlake
Category (differs)InferenceInfra
PricingFREEMIUMFREEMIUM
LicenseProprietaryProprietary
DeploymentCloudCloud
Platforms (differs)API, CLIAPI
Model supportModel-agnosticModel-agnostic
Vendor (differs)Modal LabsTensorlake

The honest brief

Modal

Define GPU infra in Python decorators with 2-4s cold starts — no YAML, Dockerfiles, or managed-stack lock-in.

  • Python-decorator infra, no YAML/Dockerfiles
  • Scale-to-zero, pay only when running
  • Scales to hundreds of GPUs
  • Free monthly starter credits
  • SDK lock-in; migrating means rewriting
  • No managed vLLM/TensorRT setup
  • Costs climb under heavy usage
  • Billing hard to predict

Tensorlake

Sandboxes are stateful microVMs that pause and resume, so a long agent loop survives restarts instead of losing state on ephemeral runners.

  • Stateful pause/resume sandboxes
  • Isolated microVM code/tool execution
  • Serverless workflows scale to zero
  • SOC 2 Type 2, encrypted storage
  • Free tier to start
  • Newer, smaller than general clouds
  • Usage-based cost can add up
  • Self-host only on the enterprise tier