Lightning AI

Lightning AI is built to reduce the friction of running machine learning code on cloud hardware by offering an integrated workspace and deployment path. The platform centers on Studios, persistent cloud workspaces where you can develop in Jupyter style notebooks or connect with VS Code, using GPUs when needed and pausing work when you are done so the environment and files persist. Studios can be created from templates and support typical development needs such as terminals, package installs and connecting code from GitHub or GitLab using SSH keys. For teams moving beyond experiments, Lightning also documents ways to deploy containers and to host web apps from a Studio and expose them via a public URL, which makes it practical for demos and internal tooling. In its open source ecosystem, Lightning maintains components such as LitServe for building custom inference APIs, which complements the hosted workflow when you need production serving patterns. Pricing is published with a free tier and paid plans such as Pro, and the pricing page notes monthly prices that can be billed annually. Operational costs can also include storage, and Lightning documentation states that the first 10 GB saved on its Drive are free, with additional storage billed at $0.10 per GB per month. Lightning AI fits developers and researchers who want cloud GPUs without assembling separate services for IDE, compute and deployment. It is not a managed training framework by itself, so you still bring your own code and ML stack, but the environment aims to make iteration and sharing faster and more reproducible.