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Daytona vs Paperspace Gradient

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Daytona

8.4#12 in coding playgroundsFree plan
Free planWebAPILinuxmacOS
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What both publish

FactDaytonaPaperspace Gradient
Company foundingDaytona's own about page says the company was founded in 2023.G2 lists Paperspace as serving customers since 2014.
Company legal nameCrunchbase lists the legal name as Daytona Platforms Inc.Crunchbase lists the legal name as Paperspace, Co.
ComplianceDaytona's Trust Center lists GDPR, HIPAA, SOC 2, ISO/IEC 27001, SOC 2 Type 1, and SOC 2 Type 2 compliance materials.The security page says its datacenters meet SOC 1, SOC 2, PCI-DSS, and ISO 27001 standards.
Documentation depthThe official docs cover platform concepts, sandbox classes, agent tools, human tools, webhooks, security, observability, deployments, SDK references and CLI usage.The documentation includes getting-started guides, how-to articles, concepts, reference material, limits, known issues, support pages, CLI references, and SDK/API examples.
FundingRy Walker Research reports a $24 million Series A in February 2026 led by FirstMark, with Datadog and Figma Ventures participating.Paperspace announced a $13 million Series A that brought total funding to $19 million.
Rating on G24.3 out of 5 from 24 reviews.Paperspace Core is rated 4.9 out of 5 from 10 reviews.

Daytona plans

  • Pay-as-you-go—
  • Start-upsFree
  • Free compute creditFree
  • StorageFree
  • Memory$0.02 a month
  • Compute vCPU$0.05 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 Daytona if…

Daytona suits developers and teams running AI-generated code, coding agents, evaluations or data analysis in controlled workspaces. It may also fit teams that need self-hosting, GPU workspaces or JetBrains support.

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