Use cases
Inventory
Find every tool, owner, seat, and cost.
Savings
Catch waste, overlap, and renewals early.
Access
Onboard and offboard across your stack.
Context
Map how people, tools, spend, and agents connect.
For
Founders
Protect runway by seeing every tool and cost.
Engineering
Control AI usage, access, and provider spend.
Finance
Reconcile software spend and find waste.
Operations
Keep inventory, access, and renewals current.
Resources & company
Resources
Guides and practical software insights.
Manifesto
Why software operations need a new model.
Press
Company news and media resources.
Partners
Build and grow with ELI.
IntegrationsStacksTools
Talk to a humanDiscover my stack
IntegrationsStacksTools
Discover my stackTalk to a human

Company

  • Home
  • Manifesto
  • Talk to us
  • Partners

Help

  • Terms of service
  • Privacy policy
  • Docs

Product

  • Inventory
  • Savings
  • Access
  • Context

For teams

  • Founders
  • Engineering
  • Finance
  • Operations

Explore

  • Integrations
  • Tools
  • Stacks
  • Startup credits
  • Articles

Socials

  • LinkedIn
  • Instagram
  • TikTok
  • YouTube
Enterprise Layer Intelligence
Categories/AI Orchestration and MLOps/Lightning AI
Lightning AI

Lightning AI

Founded by William Falcon in 2019

The all-in-one platform for AI development. Code together. Prototype. Train. Scale. Serve. From your browser - with zero setup. From the creators of PyTorch Lightning.

Pricing model

Free Tier

Rating

★ People love it

Time to value

Quick Setup (< 1 hour)

You can use Lightning AI to build, train, and deploy AI models in a collaborative GPU cloud environment. It provides AI notebooks with persistent GPU access, managed training clusters, inference APIs, and templates for various AI projects. The system includes AI debugging assistance and supports PyTorch development with zero setup required from your browser.

What Lightning AI does

Train neural networks on GPU clustersDeploy models as scalable APIsDebug AI code with AI assistanceManage training experiments and metricsShare AI projects with team membersClone and customize AI templatesRun distributed training jobsMonitor model performance in productionPersistent GPU notebooks with AI coding assistanceOne-click model deployment with LitServePre-built templates for common AI tasksMulti-cloud GPU cluster managementBrowser-based development with zero setupCollaborative workspace for AI teamsPyTorch Lightning integrationPay-per-token inference APIs

Pricing breakdown

Pricing model: Free tier

Tutorials & Demos

Frequently asked

Google CloudGoogle CloudNVIDIA Base Command ManagerNVIDIA Base Command ManagerGitHubGitHubHugging FaceHugging FacePyTorchPyTorchHydraHydra

Want a tailored answer?

See whether Lightning AI fits your stack.

Techbible weighs Lightning AI against what you already pay for, your team shape, and the work that's actually happening. Free to start.

Side by side

Compare Lightning AI

Search the catalog or pick a similar tool to compare pricing, features, and fit.

Or pick from similar tools

More in AI Orchestration and MLOps

All tools →
AI Reliability for Production LLMs

AI Reliability for Production LLMs

Latitude is the reliability layer for AI products: detect failures, measure performance, and automatically improve model quality

AMESA

AMESA

Build and deploy AI agents to automate complex industrial processes.

Braintrust

Braintrust

Monitor AI applications and evaluate model performance in production.

Cascade

Cascade

Provides software infrastructure and security for AI agents, with self-improving models that prevent threats and failures at scale.

Executor

Executor

Connect AI agents to all your tools through one MCP gateway

Fireworks

Fireworks

Run, fine-tune, and deploy open-source AI models at high speed

Flyte

Flyte

Create and run data science and machine learning workflows using Python

Honcho

Honcho

Store and retrieve personalized memory for AI agents across conversations

Lightning AI, GPU cloud, PyTorch Lightning, AI model training, AI deployment, machine learning, AI notebooks, GPU clusters, model inference, AI development, collaborative workspace, AI templates, SLURM, Kubernetes, multi-cloud