You're Flying Blind: Adding LLM Observability to Spring AI with OpenTelemetry and Self-Hosted Langfuse

1. 1Add OpenTelemetry to your Spring Boot service to capture LLM-related metrics
2. 2Configure Self-Hosted Langfuse to collect and store LLM-specific data
3. 3Integrate OpenTelemetry with Langfuse to correlate LLM metrics with application performance
4. 4Use the collected data to identify and fix performance bottlenecks in the LLM call
5. 5Implement custom instrumentation to capture additional LLM-related metrics

Developers and DevOps teams can benefit from this approach to improve the performance and reliability of their LLM-enabled services

💡 Standard APM tools are insufficient for capturing LLM-related performance issues, requiring custom observability solutions

🚀 Fix the observability gap in your LLM-enabled Java services with OpenTelemetry and Self-Hosted Langfuse! 🚀