Learn how Kanba and Plane differ in their key features, development activity, technology stack and community adoption, so you can decide which of these project management suites is best for you.
Last 30 days
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Last 30 days
Last commit
Repository age
Version
License
Self-hosted
Repository

Plane appears to have several advantages over Kanba, particularly in popularity, growth, activity, maturity and features. Consider your specific needs regarding popularity, growth, activity, technology, maturity, licensing and features when making your decision.
Plane significantly outpaces Kanba in community adoption with 59,882 stars compared to 657 stars on GitHub. This 91.1x difference suggests Plane has a much larger and more active community. In terms of developer contributions, Plane has 5,875 forks, indicating strong developer engagement.
Plane is growing faster, adding 1,543 stars in the last 30 days (+2.6%) against adding 7 stars for Kanba (+1.1%). Plane is both larger and pulling further ahead.
Plane shows more recent development activity with its last commit 9 hours ago, while Kanba was last updated 2 months ago. This suggests Plane is being more actively maintained.
Both tools share common technology foundations, being built with JavaScript, CSS, Typescript, JSX, Next.js. However, they differ in their additional technology choices: Plane leverages Bash, Python.
Plane has been in development longer, starting 4 years ago, compared to Kanba which began 1 year ago. This 2.6-year head start suggests Plane may have more mature features and established processes.
Kanba uses the MIT license, which is more permissive than Plane's AGPL-3.0 license, potentially offering greater flexibility for commercial use and integration.
Both tools serve similar use cases in Project Management Suites. However, they also have distinct specializations: Plane extends into Collaborative Workspaces, Agile Project Management.
Plane provides self-hosting options for complete data control and customization, while Kanba may be primarily cloud-based or require different deployment approaches.
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