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Lightning AI Studios vs Paperspace Gradient

What both publish

FactLightning AI StudiosPaperspace Gradient
Company headquarters and sizeLinkedIn lists Lightning AI as privately held, headquartered in New York, with 51–200 employees.Crunchbase lists Paperspace in New York, New York, with 11–50 employees.
Deployment optionsfull_stackfull_stack
Documentation depthDocumentation includes platform guides, CLI references, SDK references, AI Studio tutorials, deployment, cluster and organization material.The documentation includes getting-started guides, how-to articles, concepts, reference material, limits, known issues, support pages, CLI references, and SDK/API examples.
Free planYesYes
Intended usersThe pricing page describes Free for students, researchers, and hobbyists; Pro for developers, researchers, and scientists; and Teams for research labs and small teams.The pricing page describes plans for beginners and individual ML/AI engineers, data scientists, researchers, teams, research groups, and startups.
Private projectsYesYes
PurposeAI Studio is a persistent, collaborative GPU cloud workspace for training models, running inference, building agents and AI apps, and coding together.Gradient is a machine learning platform for developing, tracking, and collaborating on machine learning models.
Rating on G24.5 out of 5 from 4 reviewsPaperspace Core is rated 4.9 out of 5 from 10 reviews.

Lightning AI Studios plans

  • FreeFree
  • Pro$20 a month
  • Pro (annual)$20 a month
  • Pro (monthly)$50 a month
  • Teams (annual)$119 a month
  • Teams$119 a month

Paperspace Gradient plans

  • Free (Individual)Free
  • Pro (Individual)$8 a month
  • Growth (Individual)$39 a month
  • Free (Team)Free
  • Pro (Team)$12 a month
  • Growth (Team)$39 a month

Pick Lightning AI Studios if…

The Free plan is described for students, researchers and hobbyists. Pro is aimed at developers, researchers and scientists, while Teams is described for research labs and small teams.

Pick Paperspace Gradient if…

Gradient is aimed at people and teams developing or serving machine learning models, including engineers, data scientists, researchers, and startups. Its browser notebooks, collaboration features, and deployment endpoints support different stages of model work.

Charts they share