Lightning AI
Lightning AI is an AI development platform from the team behind PyTorch Lightning. It provides cloud AI Studios (persistent GPU-backed development workspaces), ephemeral Sandboxes for running untrusted or agent-generated code, multi-node training and finetuning, batch and real-time inference deployments, and hosted Model APIs that expose frontier LLMs behind an API key. The platform is driven programmatically through the lightning-sdk Python SDK, the @lightningai/sdk JavaScript Sandbox SDK, and the lightning CLI, and is backed by a family of widely adopted open-source libraries including PyTorch Lightning, Lightning Fabric, LitServe, LitData, TorchMetrics and Thunder. Lightning AI runs as a fully managed cloud or inside a customer VPC (bring your own cloud), and is SOC 2 Type II and HIPAA certified.
Limited machine-readable signal and partial portal coverage — documentation a human can read, but little a machine or agent can consume without scraping.
API Evangelist profiles Lightning AI the way a machine reads it — 6 machine-readable artifacts across 2 APIs, pulled from the provider's own public surface and indexed so a developer, an analyst, or an AI agent can evaluate it against every other provider on the network.
Every provider in the network is reduced to the same set of machine-readable artifacts — OpenAPI contracts, event specifications, GraphQL schemas, runnable collections, pricing and rate-limit signals, security posture, OAuth scopes, and the agent surfaces (MCP servers and skills) that let software drive the API on its own. We profile them because the interface is the part of a company you can actually inspect: it is a truer signal of what a provider does than any marketing page. From those artifacts we compute the Kin Score — Lightning AI scores 42.6/100 (thin), with a separate agent-readiness read of 24/100 (agent aware). The full breakdown is below, followed by every artifact we hold — each card links through to its machine-readable definition on apis.io.
Kin Score
This is the API Evangelist rating — a single, repeatable read computed from the artifacts on this page. Green fill is points earned; the red track is points possible, so every bar shows earned-versus-possible at a glance.
Kin Score
How this is scored →scored 2026-07-27 · rubric v0.5
How we profile Lightning AI
Each block below is one kind of artifact we hold for Lightning AI. For each we say what it is and why it earns a place in the profile, then list every one we've indexed — capped at two rows, scroll within the panel for the rest.
APIs 2
Each API is captured as its own OpenAPI definition — every operation, parameter, and response. This is the single most useful machine-readable description of what an API does, and it's what lets us score, lint, mock, and generate against it without asking the provider for anything.
Individual APIs this provider publishes, each with its own machine-readable definition.
Lightning AI Platform API
The Lightning AI control-plane API used by the lightning-sdk Python SDK, the @lightningai/sdk JavaScript SDK and the lightning CLI to programmatically manage platform resources:...
Lightning AI Model APIs
Hosted inference API that serves frontier and open models behind a Lightning API key, billed per token with a free monthly token allowance. Accessible through the litai Python c...
Pricing Plans 1
Pricing is part of the interface. Machine-readable plans tell you what a tier costs and includes before you commit — one of the six things the Kin Score reads for commercial clarity.
Published pricing tiers and plan structures.
Lightning Ai Plans
PLANSRate Limits 1
Rate limits are the difference between a demo that works and a production integration that doesn't fall over. Publishing them is an operational-transparency signal — and a hard requirement for any agent that plans its own throughput.
Documented rate limits and quota policies.
Lightning Ai Rate Limits
RATE LIMITSSecurity Posture 2
Authentication, domain security, vulnerability disclosure, and trust-center signals — the evidence that a provider takes security seriously enough to document it. We profile it because you can't govern what you can't see.
Authentication, domain security, vulnerability disclosure, and trust-center signals.
Resources
Every other property we hold for Lightning AI — documentation, portals, status pages, policies, and corporate surface — grouped by the job it does, following the integrator's arc from getting started to running in production.
Get Started 4
Portal, sign-up, and the first successful call
Documentation 2
Reference material describing how the API behaves
Agent Surfaces 1
MCP servers, agent skills, and machine-readable catalogs
Design & Contract 5
Pagination, idempotency, versioning, errors, and events
Build 4
SDKs, sample code, and the tooling you integrate with
Access & Security 3
Authentication, authorization, and security posture
Operate 5
Status, limits, changes, and where to get help
Commercial 4
Pricing, plans, and the legal terms of use
Company 2
The organization behind the API
Other 1
Properties that don't map to a standard resource type
← All providers · Data indexed from github.com/api-evangelist/lightning-ai · machine-readable index on apis.io