Hands-On LLM Serving and Optimization

Preface

Large language models (LLMs) have gone from research curiosities to production-critical infrastructure in a shockingly short time—much like the internet revolution. An agentic world is coming, and in many ways it’s already here: a new wave of “tokenization” where more and more applications are built on top of LLM infrastructure rather than traditional APIs and services.

In just a few years, “just call the API” from public LLM providers like OpenAI has evolved into “we need our own models,” and then into “we need to run these models efficiently, safely, and at scale.” Businesses now need far more control over their LLMs—for data governance, troubleshooting, evaluation, compliance, and cost management. Many teams have discovered that the hardest part of GenAI isn’t training a model or wiring up a chat UI—it’s everything in between: setting up model serving and optimization that can meet business goals at an acceptable cost.

We’ve watched that gap up close. We’ve seen brilliant prototypes crumble under real traffic or blow through a GPU budget in a week. We’ve seen organizations that are eager to rebuild key use cases for LLMs held back by concerns about public API costs and data safety. We’ve seen teams that want to embed LLMs deeply into core products but feel intimidated by the complexity: how to reason about latency, throughput, and cost or how to choose between public vendors, model serving libraries, cloud endpoints, or another self-managed service.

At the same time, ...