The Ultimate Tech Stack for Building AI Products

This is the era of solopreneurs. It has never been easier to build end-to-end AI-powered applications, thanks to the most recent developments in AI and developer-friendly frameworks in particular. What used to be a complex domain, mainly led by experienced data scientists, is now democratized and is as straightforward as calling an API (and will only get better and easier over time). However, with all the noise, hype, and continuous advancements in the field, it is difficult to know where to focus and with what stack. In July 2022, I released <a href="http://cowriter.org/" rel="noopener ugc nofollow" target="_blank">Cowriter</a>, an AI-powered text editor aimed at writers. The product quickly went viral and grew to over 500K users worldwide within a few months. In the process, I learned the challenges and benefits of using various tech stacks at scale. Building impressive AI demos is one thing, but scaling AI applications to millions is another, and where most current frameworks fail. In this post, I will share with you the entire tech stack used for building my viral AI web application. You may already be familiar with some of this stack, so my goal is not only to introduce them to you, but also to help you gain intuition on why they were optimal for my needs. Let’s dive right in! <h1>OpenAI API</h1> This one is less obvious than you might think. From all the AI services out there, which one is the best? What defines the best? For me, the best is about quality, reliability, security, performance and pricing. <a href="https://medium.com/@assafelovic/the-ultimate-tech-stack-for-building-ai-products-497cfa9139cb">Website</a>