Open Source vs. SaaS: When to Give Away and When to Charge

I build a lot of things. Some I charge money for. Some I give away. The decision between open source and SaaS is one I make multiple times a year, and I've gotten it wrong enough times to develop a framework for getting it right.

This isn't an ideological piece about the virtues of open source or the evils of venture-funded SaaS. It is a practical framework from someone who does both and has opinions about when each model works.

The Framework

The open source vs. SaaS decision comes down to three questions:

1. Where Is the Value?

If the value is in the code itself -- an algorithm, a library, a framework, a tool -- open source often makes more sense. You can't realistically protect code-level value anyway. Someone will clone it, reverse-engineer it, or build an alternative. Open sourcing it lets you capture the community, the brand, and the ecosystem around it.

If the value is in the data, the network, or the operational layer on top of the code -- SaaS makes more sense. Aviation Infinity's value isn't in the Next.js code that renders the quiz interface. It is in the curated question bank, the student knowledge models, and the adaptive learning algorithms trained on real student behavior. Open sourcing the code wouldn't threaten the business because the code without the data is an empty shell.

ClickAi's value is partially in the code (the AI generation pipeline) but mostly in the operational layer -- the hosting, the domain management, the template library, the customer support. SaaS is the right model because the value proposition includes ongoing operation, not just software.

2. Who Is the User?

Developers are comfortable with open source. They can install, configure, and maintain software. They appreciate the transparency of open code and the ability to customize. If your target user is a developer, open source is a viable distribution strategy. The community becomes your marketing channel.

Non-technical users need a managed experience. They don't want to install anything. They don't want to configure servers. They want to sign up, pay, and have things work. For non-technical users, SaaS isn't just a business model -- it's a product requirement.

Most of my products serve non-technical users. Babonbo serves parents. Aviation Infinity serves student pilots. ClickAi serves small business owners. For these users, SaaS is the only realistic delivery model.

3. What Is the Competitive Dynamic?

Open source can be a powerful competitive weapon. If your competitor charges for something, open sourcing an alternative creates immediate pressure. It also builds goodwill and community that's hard for competitors to replicate.

But open source can also commoditize your own product. If you open source your core technology, you're inviting others to build competing products on top of your work. This is fine if your moat is elsewhere (data, network effects, brand) but dangerous if the code is your primary differentiator.

I've seen this play out in the AI space. Open-source models are commoditizing the model layer, which means the value is shifting to the application layer. Building AI applications (SaaS) is more defensible than building AI models (which are being open-sourced at an accelerating rate).

My Personal Decisions

Let me walk through some specific decisions I've made and why.

Aviation Infinity: SaaS (Closed Source)

The value is in the content, the adaptive learning system, and the operational reliability. Student pilots need a platform that works flawlessly, is always available, and has verified content. Open sourcing the exam prep code would be pointless because the code without the verified question bank and student data is useless.

Developer Tools and Libraries: Open Source

When I build utilities and tools for my own development workflow that might be useful to others, I open source them. The cost of maintaining open-source developer tools is low, the community contributions are valuable, and the reputation benefits are real.

AI Pipelines: Hybrid

This is where it gets interesting. My AI content generation pipelines are valuable, but they're also highly specific to my products. Open sourcing the general patterns (how to structure prompts, how to cache LLM responses, how to build evaluation pipelines) is valuable for the community and costs me nothing competitively. The specific prompts, the training data, and the product-specific logic stay closed.

The Hybrid Model

The most interesting business models, in my experience, are hybrids. Open-source the core technology, charge for the managed service, the premium features, or the data layer.

This model works because it aligns incentives. The open-source community gets a useful tool. The company gets distribution and community goodwill. Users who want a managed experience pay for it. Everyone wins.

The risk of the hybrid model is execution complexity. You are maintaining an open-source project and a commercial product simultaneously. The open-source version needs to be genuinely useful, not a crippled demo of the paid version. If the community feels exploited, the goodwill evaporates.

Lessons From Getting It Wrong

Mistake 1: Open sourcing too early. I once open-sourced a project before it was mature enough for external use. The community filed bugs, requested features, and expected support. I wasn't ready for any of this. The project became a burden instead of an asset. Lesson: only open source things that are stable enough to support.

Mistake 2: SaaS-ing a commodity. I built a SaaS product that was essentially a prettier UI on top of a widely available technology. Users initially paid for the convenience, but free alternatives kept emerging and undercutting on price. The product was never defensible. Lesson: before building SaaS, ensure your value proposition goes beyond convenience.

Mistake 3: Ignoring the middle ground. For a long time, I thought in binary: either fully open source or fully closed SaaS. Missing the hybrid model meant missing opportunities to build community around products that would have benefited from it.

The 2023 Landscape

The open source vs. SaaS dynamic is shifting in 2023 for a specific reason: AI.

LLMs have made it cheaper and faster to build software. This means the "clone risk" of open source is higher than ever. Someone can take your open-source project, use an LLM to customize it, and launch a competing product in days.

Simultaneously, the "wrapper" risk for SaaS is increasing. Many SaaS products are thin wrappers around LLM APIs. If OpenAI adds a feature that makes your wrapper redundant, your business evaporates overnight.

The defensible position in 2023, whether open source or SaaS, requires value that can't be easily replicated by an LLM. Curated data. Community. Network effects. Domain expertise. Operational excellence. These are the same moats as before -- LLMs just made them more important.

My Decision Framework (Summary)

When I face the open source vs. SaaS decision, I run through this checklist:

1. Is the primary value in the code or in what surrounds the code?
2. Are my users developers or non-technical people?
3. Does open sourcing create competitive advantage or competitive vulnerability?
4. Can I realistically maintain an open-source community?
5. Is there a hybrid model that captures the best of both?

The answers are usually clear once you ask the right questions. The mistake isn't asking the questions at all and defaulting to whatever feels right in the moment.

Build thoughtfully. Give away strategically. Charge confidently.