I used to plan AI infra the way I plan a road trip: pick a route, hope traffic behaves, and find out about the tolls later. @CottoniaAI 's Copilot changed that conversation for me. The flow is almost uncomfortably simple. It asks for the things that actually drive cost and compute—application type, expected daily requests, context length, and the model you're running—then comes back with a projected compute footprint, optimization opportunities, and a cost check before you commit. No dashboard archaeology. No guessing whether a long-context RAG pipeline needs the same shape as a bursty coding assistant. What I appreciate most is the design philosophy underneath it: infrastructure should behave like an active optimization partner, not a passive resource pool. Cottonia's AI-aware scheduling and layered context management are aimed squarely at the waste I see in real workloads—repeated context, redundant token input, inference steps that add latency without adding value. The claim of 20–50% reduction in repeated token input in high-frequency scenarios is the kind of number I actually want to test against my own traffic. My one honest note: a Copilot recommendation is only as good as the inputs you give it. If you under-specify request volume or context density, the plan will look cleaner than reality. But that's also the point—Cottonia turns infrastructure planning into a feedback loop rather than a one-time spreadsheet. If you're building AI products and still sizing infra by vibe, this is worth a look. #Cottonia @CottoniaAI