Modal

Serverless cloud for running Python code on demand GPUs

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What is Modal?

Modal is a serverless compute platform aimed at developers who want to run Python in the cloud without touching containers or Kubernetes. You decorate a function, specify its dependencies and GPU type, and deploy it; Modal handles image builds, scaling and the HTTP endpoint. It is widely used for batch inference, fine-tuning jobs, data pipelines and scheduled tasks, and it bills per second of actual execution with scale to zero. The differentiator is developer experience: infrastructure is expressed in Python rather than YAML.

Last updated: 2026-09-20. This site only provides an index; for exact features, pricing, and licensing, see the official website.

Key features

Python-native functions deployed with a decorator
GPU types selectable per function, from small cards to H100
Automatic container image builds from pip requirements
Scheduled jobs, queues and long-running background tasks
Persistent volumes plus secrets and environment management
Stable web endpoints and scheduled triggers per deployment

Pros & cons

Strengths

Infrastructure defined in Python, not configuration files
Scale to zero means idle workloads cost nothing
Works well for batch jobs and one-off GPU experiments

Watch out for

Framework-specific patterns take time to learn
Not designed for persistent long-lived servers
Cold-start behaviour needs attention in production

Best for & use cases

batch inference, fine-tuning jobs, data pipelines, scheduled gpu tasks

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FAQ

What makes Modal different from a normal cloud?

You define infrastructure in Python. A decorated function specifies its dependencies and GPU, and Modal builds the image, scales it and gives it an endpoint, so there is no container or Kubernetes work.

What is it best used for?

Batch inference, fine-tuning runs, data pipelines, scheduled jobs and internal tools that need a GPU occasionally. It is less suited to long-lived stateful servers.

How does billing work?

You pay per second of CPU and GPU time actually used, and idle workloads scale to zero. A free monthly credit covers small experiments, and costs rise with GPU class and runtime.