Infra AI apps

Persistent GPU cloud workspaces to build, train, and ship AI.

A cloud platform built around AI Studios — collaborative, persistent GPU workspaces for coding, training models, running inference, and building agents and AI apps. Pay-as-you-go GPUs with a monthly free credit allowance, plus a Pro tier and bring-your-own-cloud for enterprise. Made by the team behind the open-source PyTorch Lightning framework.

On-demand serverless GPU compute for AI, from Python.

A serverless cloud for deploying AI inference endpoints, agent sandboxes, task queues, and containerized GPU workloads with a few lines of Python. It handles fast cold starts, autoscaling, and Docker-in-Docker execution across multiple cloud backends, and supports bring-your-own-compute. The Developer tier is free with recurring monthly credit; paid tiers add team features and scale, billed pay-as-you-go by GPU usage.

Run AI and batch jobs on any cloud or Kubernetes, from one interface.

An open-source framework for running, managing, and scaling AI and batch workloads across Kubernetes, Slurm, and 20+ cloud providers through a single unified interface. It abstracts away per-provider setup, optimizes for cost and GPU availability, and automatically fails over between regions and clouds when capacity is scarce. You run it yourself against your own infrastructure — the software is free and Apache-2.0 licensed; you pay only your own cloud bills.

Open-source browser API for AI agents and apps.

A batteries-included headless-browser sandbox that lets AI agents automate the web without managing browser infrastructure. Steel handles sessions, cookies, proxy rotation, CAPTCHA solving, and page-to-markdown/screenshot/PDF conversion behind a single API, built on Puppeteer and the Chrome DevTools Protocol. Run the open-source image locally via Docker or use Steel Cloud's managed browser fleet.

Secure, elastic sandboxes for running AI-generated code.

Infrastructure for executing AI-generated code in isolated sandboxes — each a full composable computer with a dedicated kernel, filesystem, network stack, and allocated CPU, RAM, and disk. Sandboxes start in under 90ms, snapshot for persistence, and are driven programmatically through SDKs (Python, TypeScript, Ruby, Go, Java), an API, and a CLI. AGPL-3.0 and available as a managed service, self-hosted stack, or hybrid where you bring your own compute.

Secure cloud sandboxes for running AI-generated code.

Open-source infrastructure that spins up isolated cloud sandboxes so AI agents can execute generated code safely. Python and JS/TS SDKs cover code interpretation, data analysis, and computer-use desktops, with per-second billing. Self-host or BYOC/on-prem is supported for enterprise.

Headless browser infrastructure for AI agents.

Managed cloud fleet of headless browsers that let AI agents browse, authenticate, and act on the web at scale. Sessions ship with stealth proxies, automated CAPTCHA solving, and observability, driven via API or the open-source Stagehand framework. Usage-based billing on top of a monthly base plan.

Postgres + auth + storage + edge functions, open source.

A common default backend when an app needs persistence + auth + realtime. Open-source, self-hostable, very low friction to local dev with the CLI.

Workers + R2 + D1 + Durable Objects. Global edge runtime.

When the app needs to run close to users globally, or when an isolate-per-request model fits the workload better than Node functions. R2 for storage, D1 for SQLite-at-the-edge.

Frontend cloud for React/Next. Edge functions + image opt + analytics.

Next.js-native hosting with fast deploys, edge functions, image optimization, and a free Speed Insights tier. Strong default for the React/Next ecosystem.