Spring AI Observability(Metrics/Traces/Logs) with Prometheus Tempo Loki Grafana | Advanced Tutorial

In today’s video, we’ll going to take a look at Spring AI Observability, basically, it’s all about Spring AI metrics, traces/spans, and logs. We'll build a complete monitoring solution for our application, featuring:
✅ Prometheus for collecting detailed metrics 📊
✅ Tempo for tracking requests with traces and spans ⏳
✅ Loki for aggregating all our logs 📜
✅ Grafana to bring it all together in a single, powerful dashboard 📈
We'll start with a Simple Spring AI App first to understand the basics, then apply this setup to the SSE MCP client and server from our previous videos, giving us a crystal-clear view of the entire flow from user interaction to tool calling.

Github: https://github.com/nlinhvu/llm-sse-mcp-demo-2025/tree/ai-observability

MCP Experiments: https://www.youtube.com/playlist?list=PLLMxXO6kMiNj3rAu72ygiMjqEdKwE94jZ

References:
Spring AI Observability: https://docs.spring.io/spring-ai/reference/observability/index.html

(00:00): Introduction
(01:24): Initiate a Simple Spring AI Application
(07:15): Prometheus for Metrics
(13:41): Tempo for Traces/Spans
(17:20): Loki for Logs
(21:38): OAuth2 SSE MCP Services are applied

#observability #springai #java #modelcontextprotocol #oauth2 #security #springboot #sse #mcp #metrics #traces #spans #logs #prometheus #tempo #loki #grafana #micrometer #opentelemetry #actuator

Видео Spring AI Observability(Metrics/Traces/Logs) with Prometheus Tempo Loki Grafana | Advanced Tutorial канала Linh Vu