Blog • February 2026
By Cemhan Biricik — Founder of ZSky AI
When I started building ZSky AI, the first major technical decision was whether to build on open source models or license proprietary ones. This was not just a technical choice — it was a business philosophy decision that would shape everything that followed. I chose open source, and I would make the same choice again without hesitation.
I want to be fair to the other side. Proprietary AI models from major labs offer genuine advantages. They are often first to market with new capabilities. They come with polished APIs, documentation, and support. You can integrate them quickly and start serving users within days. For many startups, this speed to market is worth the cost.
The pricing is also straightforward. You pay per API call, your costs scale with usage, and you do not need to worry about GPU procurement, model deployment, or infrastructure management. For a team of three building a consumer app, this simplicity is compelling.
The advantages of proprietary AI are real, but they come with a dependency that I was not willing to accept. When you build on someone else's API, you are building on rented ground. They can change pricing, modify model behavior, add content restrictions, or shut down access entirely — and you have no recourse.
I have watched this happen repeatedly. A startup builds its entire product on a third-party AI API. Six months in, the API provider raises prices by 40%. The startup's unit economics collapse overnight. They have no alternative because switching models means rewriting their entire prompt engineering layer.
With open source models running on my own hardware, I control every variable. I choose which model to run. I decide how to optimize it. I set my own content policies. My costs are fixed regardless of usage volume. And no one can revoke my access to the models I have already deployed.
Two years ago, open source models were noticeably behind proprietary ones. That gap has closed dramatically. For image generation — which is ZSky AI's core capability — open source models are now competitive with and in some cases superior to proprietary alternatives. The community-driven pace of innovation is staggering.
The challenge with open source is that you need the engineering capability to deploy, optimize, and maintain the models yourself. There is no support team to call when something breaks. You need to understand model architectures, inference optimization, GPU memory management, and a dozen other technical domains. This is the real cost of open source — not money, but expertise.
I am not dogmatic about this. There are situations where proprietary APIs make sense — particularly for capabilities that open source has not yet matched, like certain large language model tasks. The key is to use proprietary APIs as supplements, not foundations. Your core product capability should run on infrastructure you control.
The open source AI ecosystem is the most important development in technology since open source software itself. It democratizes access to capabilities that were previously gated behind corporate APIs and enterprise pricing. As a founder, I am grateful to the researchers and engineers who make their work available to builders like me. ZSky AI exists because they chose openness over exclusivity.