ToolsCompaniesAbout

Lightning AI

by Lightning AINew York, NY, United StatesNo ratings yet

Cloud GPUs, Studios, and training tools for building and deploying your own AI models.

Visit lightning.ai

Linked from lightning.ai · checked 29 Oct

Tool type
Cloud AI model training and deployment platform
Made for
ML engineers, researchers, and AI teams
Runs on
Browser, SSH, and local IDEs on cloud GPUs
Studios run on Lightning cloud GPUs (AWS and GCP currently supported) and can be reached through a browser, SSH, or a local IDE such as VSCode.
Last checked
29 Oct 2026
3 product sources

What it does

Lightning AI is a cloud platform for building, training, and deploying AI models. Teams bring in datasets, code, and model choices, then work in Studios (persistent GPU workspaces reached through the browser, SSH, or a local IDE) and run training, evaluation, or inference on on-demand GPUs and clusters. Outputs include trained or fine-tuned models, experiment results, and deployed inference endpoints.

Typical uses include prototyping on a single GPU, running many experiments in parallel, comparing training runs, and scaling to multi-node training. The platform also offers team features such as shared file systems, published Studios, and role-based access. Its distinct pieces are the Studio workspace model, the CLI and Python SDK for managing resources programmatically, and the open-source PyTorch Lightning framework the platform builds on.

It suits ML engineers, researchers, and teams who need on-demand GPU access and managed training infrastructure. A free tier with up to 30 starting credits is available with no credit card required; paid use is pay-as-you-go by the second or through Pro and Teams plans. Key limits: the free tier allows one active CPU Studio at a time with restarts every 4 hours, GPU prices vary by type, and AWS and GCP are currently supported, with Azure listed as coming soon.

What it can do

  • Launch on-demand GPUs and CPUs in persistent Studios accessible from the browser, SSH, or a local IDE
  • Run multi-GPU and multi-node training, including pretraining and fine-tuning
  • Run many training experiments in parallel and compare runs
  • Deploy autoscaling inference APIs and model endpoints
  • Manage datasets with cloud storage connections (S3, GCS, R2) and distributed data prep
  • Enterprise controls including SSO, role-based access, audit logs, and VPC deployment

Works with

  • AWS
  • GCP
  • S3
  • GCS
  • R2
  • GitHub
  • GitLab
  • VSCode
  • PyCharm
  • Kubernetes

Example workflows

Prototype and tune a model
Input: a dataset and training script. Steps: create a Studio, pick a single GPU, connect via SSH or local IDE, iterate, then run parallel experiments. Output: a tuned model checkpoint and comparable run results.
Deploy an inference endpoint
Input: a trained model. Steps: package it, deploy an autoscaling inference API, and monitor usage. Output: a live model endpoint served on managed GPUs.

Before you choose

  • Free Studios run 24/7 but require a restart every 4 hours; the free tier allows one active CPU Studio at a time.
  • Credits expire after 12 months, and GPU prices vary by type and are billed by the second.
  • Using your own AWS or GCP credits requires the Enterprise tier; Azure is listed as coming soon.

Listed under

Get started

Free tier with up to 30 starting credits; no credit card required.

  1. Create an account at platform.lightning.ai and start a Studio on a free CPU or a GPU.
  2. Connect your code repository (GitHub or GitLab) or upload data, and optionally connect S3, GCS, or R2 buckets.
  3. Install the CLI or Python SDK and connect a local IDE or SSH to the running Studio.
Sign up for a free account

Plans and prices

Prices in USD

Free tier gives up to 30 starting credits (about 80 GPU hours on interruptible machines). Pay-as-you-go GPU and CPU rates are per hour, billed by the second, and vary by GPU type; see the pricing rate table. Pro and Teams plans are billed monthly or annually with credit allotments; Enterprise is custom.

FreeUsage-basedPay-as-you-go GPU and CPU rates by the second; see the pricing rate table.
  • Up to 30 credits to start
  • 1 active Studio (4-hour restarts)
Pro$50/moMonthly billing; annual billing shown at $20 per month.
  • 40 monthly credits or 240 annual credits
  • Multi-GPU and multi-node training (6 GPUs max)
Teams$140/user/moMonthly billing; annual billing shown at $119 per user per month.
  • 50 monthly or 600 annual credits
  • Full-node A100, H100, H200 and multi-node training (12 GPUs max)

Source: lightning.ai/pricing, checked 29 Oct 2026. Prices can change; check before you buy.

Sources for this overview

Product pages, documentation, and pricing checked 29 Oct 2026. Each source supports part of the overview; availability and prices can change.

Ratings and reviews

No ratings for Lightning AI yet.

Be the first to rate it

What’s changed

Free account

See what’s changed at Lightning AI

Launches, price moves and pivots, each with a date and a source. Free with an account.

Signals

Free account

See Lightning AI’s signals

Launch traction, hiring and release activity, tracked over time. Free with an account.

Data and security

Free account

See how Lightning AI handles your data

Training on your files, compliance, hosting and the models behind it. Free with an account.

Compare with similar tools

Free account

Compare Lightning AI side by side

Prices, platforms and features against similar tools, with sources. Free with an account.

Where this comes from

How we research
Company website
lightning.ai: product, pricing and policy pages
YC directory mirror
Batch and company name
Social accounts
Accounts the company links, and its posts on X
Human review
Checked by a person
knowledge_base

Last checked 29 Oct 2026. Every field keeps its source and date; unknown means we couldn’t confirm it.