Hello, I’m Lee Do-won (Kenneth).
I’ve opened the free course “Spring AI LLM Cost Monitoring — Build a Token Dashboard with One Line of Actuator”.
Why did I create this course?
The question I was asked most often while consulting on enterprise AI projects was this.
"We built a chatbot, but how much will this cost per month?"
LLM service costs vary with every request, responses can take several seconds, and the causes of failures lie outside our code. Yet many teams monitor them like ordinary web services and only discover the problem after receiving the bill.
Zero errors, Health UP, HTTP 200. An incident where input tokens increase several-fold while everything appears normal cannot be detected by conventional monitoring.
What this course does
With one line of Actuator, expose the token, latency, and error metrics that Spring AI is already recording.
With a single docker compose command, launch a Prometheus + Grafana token dashboard.
We find “occasionally slow requests” using p95 instead of the average.
With a single topK 4 → 20 setting, you will reproduce firsthand a “failure that looks normal” where input tokens increase approximately fourfold, and detect it on the dashboard.
No business code changes are needed. Just one dependency line and a few configuration lines.
Course Information
Duration: Approximately 1 hour (including hands-on practice)
Lab environment: JDK 21 or later, Docker Desktop, and an OpenAI API key or Ollama (free)
Provided materials: hands-on project, docker-compose, Grafana dashboard JSON, load scripts (bash · PowerShell), and step-by-step course materials
Metric names follow the Spring AI standard, so you can use the hands-on dashboard as-is in your project. Even without an API key, you can follow along all the way using Ollama.
Recommended for those who
Backend developers building a PoC or internal chatbot with Spring AI and preparing for launch
Actuator and Prometheus users who don't know what to monitor in LLM services
Team leads and architects who need to report the cost of adopting LLMs in numbers
If you want to go deeper
The custom metric that converts tokens into KRW costs, cost spike alerts, and distributed tracing (Jaeger) for viewing a single request step by step are covered in the 『Practical LLM Service Development for Spring Developers』 Section 16.
👉 Practical LLM Service Development for Spring Developers
If you get stuck while taking the course, please post your questions on the Q&A board. Questions like "I followed the steps, but the graph is empty" are welcome too.
The feedback you leave in your course review is a great help when creating the next course.
Thank you.
Best regards,
Dowon Lee