Recently, I tried out @CottoniaAI , and I’d like to share my honest thoughts. When building AI applications, I’ve always struggled with choosing the right infrastructure: going with high-spec GPUs runs the risk of waste, but cutting back leaves you worried the system won't hold up. Often, it ends up being a gut-feel decision. Copilot’s approach is to first break down the workload—looking at factors like application type, model used, estimated request volume, and context window length. Once you input these details, it breaks down your requirements into dimensions like compute power, cost, performance, and scalability, and then provides a comparison of several options. What I found most valuable is how it brings easily overlooked variables into focus: how costs scale when request volume doubles, or where GPU memory pressure builds as the context window lengthens. Thinking through these aspects before making a decision can save you a lot of detours. Of course, the accuracy of the output depends on how close your input data is to reality. For early-stage projects with uncertain traffic, I view it more as a framework for thinking rather than a precise budget planner. If you’re also trying to choose the right infrastructure for an AI application, it’s worth mapping out your workload clearly first before running the numbers. #Cottonia#