AI Inference Engine Builder's Guide

If you've been calling AI APIs and watching your token bills climb, or you've hit latency walls that no amount of prompt engineering can fix, this guide is for you. You're an engineer who knows models exist, but the layer between "trained weights" and "answer in production" feels like a black box. You've heard terms like KV cache, continuous batching, and prefill/decode thrown around, but never seen them explained in a way that actually sticks. This guide walks you through building your own AI inference engine from scratch. You'll provision a GPU, deploy an open-source inference server, connect your code, stress-test it under load, and understand exactly what's happening under the hood when a model serves a response. No hand-waving. No "just use this API." You'll learn why inference architecture is fundamentally different from training, how to make smart trade-offs between cost and latency, and when running your own infrastructure actually makes sense versus renting someone else's. I'm Linda, an AI engineer with over a decade in tech infrastructure including years at AWS. I've spent my career making complex systems understandable, and I've designed this guide the way I wish someone had taught me: concrete steps, real examples, and the why behind every decision. Whether you're trying to cut costs, gain control over your AI stack, or just understand what you're actually paying for when you call an API, this is your blueprint.