강의

챌린지

멘토링

로드맵

커뮤니티

Programming

/

Back-end

Practical LLM Service Development for Spring Developers

This is a practical course on building a RAG-based document search system using the OpenAI API and Spring AI in a Spring Boot environment. You will learn the entire process of developing an AI assistant step by step, from the concepts of Prompt Engineering, Vector Stores, and Embeddings to rapid prototyping with Dify, MCP tool integration, multimodality, and guardrails. You can learn practical application strategies by comparing direct implementation with the use of no-code tools. 🆕 [Update] Section 4 now includes a supplementary lecture (1 hour) on TypeSafe’s Jev decision-making model, covering how to design an agent’s decision-making logic in greater depth.

(5.0) 6 reviews

795 learners

Level Basic

Course period Unlimited

  • Dowon Lee
openai
openai
vector-database
vector-database
prompt engineering
prompt engineering
RAG
RAG
Spring AI
Spring AI
openai
openai
vector-database
vector-database
prompt engineering
prompt engineering
RAG
RAG
Spring AI
Spring AI
News list
kenneth님의 프로필 이미지
kenneth

[Open] Spring AI LLM Cost Monitoring — Get a Token Dashboard for Your Service Up and Running in 1 Hour

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

Comment