I compared the real cost of running LLMs on AWS - here's when each option makes sense

AWS gives you three ways to run LLM inference in production. I've deployed all three for clients and the decision always comes down to the same variables: volume, team size, and how much you value your weekends.

Here's the short version.

The three paths

Bedrock — Fully managed, pay-per-token. You call an API, you get tokens back. No GPUs, no cold starts, no 3am pages about OOM pods.

SageMaker Endpoints - Semi-managed. You bring your model (or a fine-tuned one), deploy it on dedicated instances, and handle autoscaling. Pay per hour whether you're serving requests or not.