Proprietary vs open AI explained

Key Takeaways

At some point, anyone building with large English models faces a practical decision. Which kind of model should I use? If you are just experimenting, this choice may not be very important. You'll probably begin with a proprietary API or model like Chpt since it's easy to use and available. But as soon as you want to deploy something at scale, reduce costs or customize a system, the type of model you choose becomes critical. There are three main categories, each with significant trade-offs in terms of cost, control, and complexity. First, we have proprietary models. These models, like OpenAI's, GPT5 or Google's Gemini, are owned and operated by a company. You access them via a paid service and cannot see or modify the model's internal workings. Many developers start here because proprietary models offer powerful capabilities and are super easy to integrate using APIs. So within our code on the other side we have open models with two categories. First the open weight models. These models like meta's llama 3.1 mstral models or even Gemma models by Google are released with their weights available to the public. However, they aren't fully open source. The training data and methods are usually kept private and licenses can have restrictions. Open weight models give you transparency and flexibility to run them yourself while still benefiting from cutting edge performance. And lastly, we have open- source models. Truly open models shared, not just the weights. Which means that yes, you can implement them yourself like the open models, but you can also understand everything behind [music] it. Which means they also provide the training code, the data, and the methods. All this is under permissive licenses. They maximize control and reproducibility, but they often fall short of the best proprietary or open weight systems in performance, which might not be an issue in most case.