# LLM Observability with OpenObserve: Traces, Latency, Token Cost & Prompt Logs

> This video explains the need for observability in complex AI/LLM applications and demonstrates how to monitor them using tools like OpenObserve and OpenTelemetry. It covers key metrics such as traces, latency, token usage, cost, and logs, along with practical dashboards and visualization techniques for debugging and performance optimization.

Source: https://openobserve.ai/webinars-videos/llm-observability-with-openobserve-traces-latency-token-cost-and-prompt-logs/
Published: 2026-04-10
Authors: Simran Kumari
Duration: 5 minutes

---

This video explains the need for observability in complex AI/LLM applications and demonstrates how to monitor them using tools like OpenObserve and OpenTelemetry. It covers key metrics such as traces, latency, token usage, cost, and logs, along with practical dashboards and visualization techniques for debugging and performance optimization.

## What you'll learn

- Why observability is critical for complex LLM applications
- The four key signals: traces, latency, token usage, and logs
- How to instrument LLM apps using OpenTelemetry
- How to monitor end-to-end LLM traces and workflows
- How to measure latency (TTFT and total response time)
- How to track token usage and cost across models and users
- How to debug using prompt and response logs
- How to use waterfall views and span details for analysis
- How to identify bottlenecks with flame graphs
- How to visualize system architecture using service maps and DAGs
- How to correlate logs and metrics using trace IDs
- How to use dashboards for LLM cost monitoring

This video explores why modern AI applications—especially those using multiple LLMs, large context windows, and tool integrations—require strong observability to ensure reliability and efficiency. It introduces the four key monitoring signals: traces, latency, token usage/cost, and logs.

The tutorial walks through instrumenting an LLM application using OpenTelemetry GenAI specifications and integrating it with OpenObserve via a Python SDK or collector. Using real-world trace data from a production SRE agent, the video demonstrates how to analyze system performance through metrics like request rate, errors, and duration.

It further showcases advanced visualization tools including waterfall trace views, span-level input/output inspection, flame graphs for identifying bottlenecks, service maps, and DAG flow diagrams. The video also highlights how logs and metrics can be correlated using trace IDs, and presents pre-built dashboards for tracking LLM costs across models, users, and features.

## Resources

- [LLM Observability features](https://openobserve.ai/llm-observability/)
- [Setup Guide](https://openobserve.ai/docs/integration/llm-applications/)