Serverless Python compute for AI. The best developer experience in GPU clouds. Run anything — LLMs, models, batch jobs — with a Python decorator.
Modal is what happens when you give Python-loving developers the AWS Lambda treatment for GPUs. You write a Python function, decorate it with @app.function(gpu="A100"), and Modal deploys it to a containerized GPU that auto-scales from 0 to thousands. No Docker, no YAML, no Kubernetes. Just Python. Free tier includes $30/month — enough to run a real product or fine-tune a model.
Who it's for: ML engineers who hate DevOps, indie devs shipping AI products fast, teams that want to deploy AI without managing infrastructure, anyone tired of writing Dockerfiles.
Decorators, async functions, local development. Run `modal run script.py` and your code ships to a GPU. No Dockerfile, no yaml, no kubernetes manifests. The fastest path from notebook to production.
Functions scale from 0 to 1000+ containers based on demand. Pay only for actual compute time (per-second). Idle = $0. No more paying for pods sitting at 5% utilization.
T4 ($0.16/hr), L4 ($0.36/hr), A100 40GB ($0.83/hr), A100 80GB ($1.16/hr), H100 ($2.40/hr). Plus custom CPU/RAM configs. Beats AWS, Lambda, RunPod on DX; matches RunPod on price.
Scheduled jobs, web endpoints, queues, volumes (S3-style), Dicts (key-value), Secrets (env vars). Everything you need to ship an AI product without bolting together 5 services.
Modal is the best GPU cloud for Python developers who want to ship AI products in 2026. The developer experience is unmatched, the free tier is genuinely usable, and the auto-scaling means you only pay for what you use. If you can live with Python-only, Modal replaces half your AWS bill and 90% of your DevOps work.
Cheaper raw GPU hours. Less DX polish but more flexibility.
Cog-based model hosting. Best for running community models without managing infra.
Managed inference, no GPU management. Skip Modal if you only need LLM API.
Enterprise GPU cloud. Better SLAs but less DX, more expensive than Modal.