Read and manage Lightning AI billing - credit balances for a user, org or teamspace (with transaction history), spend reports broken down by resource type, user, teamspace, day or single resource (jobs, Studios, deployments, sandboxes, storage, LLM/assistant…
Preprocess and stream training datasets with LitData, choose raw or optimized storage, configure media serialization, resume data loaders, and diagnose cloud I/O, caching and multi-worker failures. Use when the user works with litdata, StreamingDataset,…
Build, update, schedule, inspect and troubleshoot Lightning AI pipelines with dependent CPU/GPU jobs, Studio snapshots and shared data. Use when the user wants a multi-step workflow, recurring training, schedule replacement, or logs from a specific pipeline…
Publish a local file (HTML report, PDF, image, dataset sample, build output) to Lightning AI and get a durable, public lightning.ai/artifacts/ link that never expires and renders inline in the browser. Also lists the artifacts drive, unpublishes (revokes)…
Launch and manage batch jobs on Lightning AI - run commands on cloud CPUs/GPUs from a Docker image or a Studio snapshot, monitor status, show live progress with an ETA and setback tracking (status-line bar + Monitor events), fetch logs, SSH into a running job…
Estimate and compare what a workload costs on Lightning AI - fetch live per-hour GPU/CPU machine prices from the accelerator catalog for every cloud (Lightning Cloud, AWS, GCP, Lambda Labs, Nebius, Voltage Park), size hardware for a model, then quote a…
Manage Lightning AI Studios (cloud dev machines with CPUs/GPUs) - create, start, stop, delete studios, switch machine types, run commands in them (including long detached runs with live progress tracking), upload/download files, and SSH in. Use when the user…
Deploy and operate services on Lightning AI - run Docker containers or HuggingFace models (vLLM) behind autoscaled HTTPS endpoints, manage replicas, releases, endpoint auth, and logs. Use when the user wants to deploy an API, model server, or container to…
Call hosted LLMs (OpenAI GPT, Anthropic Claude, Google Gemini, open-weights models) through the Lightning AI models API / LLM gateway - chat, streaming, multi-turn conversations, images, and model metadata; plus the teamspace model checkpoint registry. Use…
Run code in Lightning AI Sandboxes - fast, isolated ephemeral VMs with optional persistence, snapshots, custom images, Docker-in-sandbox (docker / docker compose), network egress policies, file I/O, public port URLs, and interactive PTY sessions. Use when the…