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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
Capabilities (differs)
  • Sandboxed code execution
  • GPU compute
  • Model inference / serving
  • Fine-tuning / training
  • App / agent deployment
  • Sandboxed code execution
  • Workflow orchestration
  • Document parsing (structured)
  • Structured extraction

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

When to pick which

Both cover Sandboxed code execution.

Pick Modal if you need GPU compute, Model inference / serving, Fine-tuning / training, and App / agent deployment.

  • GPU compute (primary capability)
  • Model inference / serving (primary capability)
  • Fine-tuning / training (secondary capability)
  • App / agent deployment (secondary capability)

Pick Tensorlake if you need Workflow orchestration, Document parsing (structured), and Structured extraction.

  • Workflow orchestration (primary capability)
  • Document parsing (structured) (secondary capability)
  • Structured extraction (secondary capability)

Tensorlake leans on Sandboxed code execution as a headline capability; Modal treats it as secondary.

  • Sandboxed code execution (primary capability)

They also differ on:

Platforms
API, CLI · API
Modal detailsTensorlake details