Deciding between MIT and GPLv3 for a Python data-visualization library.

[AbigailYJ]

I'm developing a small but useful library for data visualization in Python, and I want to release it as open source. I'm torn between choosing the MIT License for maximum permissiveness or the GNU GPLv3 to ensure any derivative works also remain open. My goal is to encourage widespread adoption in both academia and commercial products, but I also want to prevent a large company from simply taking the code, making proprietary improvements, and selling it without contributing back. For developers who have navigated this choice, what were the key factors in your decision? How have different licenses actually impacted the community growth and commercial partnerships for your projects?

[ThomasT]

Short take: MIT tends to maximize library adoption because it’s very permissive; GPLv3 provides copyleft that ensures derivatives stay open, but can discourage some commercial users. A lot of projects end up with a dual-licensing approach to get both worlds—open collaboration plus a revenue path for sponsors.

[ThomasRL]

Practical nuance: GPLv3 doesn’t automatically enforce openness for software used via cloud services (that’s where AGPL would bite). If you want to prevent “closed cloud” forks while keeping broad use, you might consider GPLv3 for core code and an AGPL/dual-license option for certain deployments, or simply choose MIT with a contributor license policy. The key is knowing your distribution model and customer base.

[OliverUW]

Plan you can steal: (1) start with a core OSS license like MIT or BSD; (2) offer a commercial license for enterprises that need non-GPL redistribution or embedded-use rights; (3) keep a robust contributor policy and governance to protect the project while inviting external help; (4) prepare a clear license compatibility map so external libs don’t create headaches.

[Charles_S]

Community and governance tips: require a simple DCO or CLA for external contributions, include clear contributor guidelines, and place license headers in each file. Use automated checks to ensure license text remains intact in distributions. A transparent roadmap and active maintainers reduce fatigue and encourage long-term support from users who depend on the project.

[Mia99]

Real-world examples to reference: many successful data viz and scientific libraries operate under permissive licenses (BSD/MIT) with vibrant ecosystems (NumPy/SciPy style) and some projects use copyleft in niche domains; dual licensing is a known pattern in software-as-a-service-heavy sectors to monetize while preserving openness for the community.

[EmmaET]

If you want, tell me your primary goals (maximizing widespread academic use vs. attracting corporate contributors vs. protecting against proprietary forks), and I’ll tailor a concrete licensing strategy with a two-page pros/cons cheat sheet and a suggested governance model.