Introduction
Jaeger is an open-source, CNCF-graduated distributed tracing system that enables end-to-end visibility into request lifecycles across microservices and infrastructure. Designed for high-scale environments, Jaeger provides deep insight into latency, service dependencies, and error propagation. Jaeger is a key observability component used to diagnose performance issues, trace model inference calls, and investigate root causes of distributed system behavior—especially in real-time and multi-agent workloads.
Key benefits of using Jaeger include:
End-to-End Request Tracing: Captures spans and traces from every service touchpoint, showing how requests propagate across APIs, databases, model servers, and background jobs.
Performance Bottleneck Detection: Visualizes latency breakdowns by operation, helping teams identify slow services, queuing delays, or downstream timeouts.
Contextual Debugging: Enriches traces with metadata (e.g., user ID, model version, experiment group) to support incident triage and contextual root cause analysis.
Native OpenTelemetry Integration: Fully compatible with the OpenTelemetry Collector, making it easy to instrument services in Python, Go, JavaScript, and other supported languages.
Service Dependency Mapping: Generates real-time service graphs to illustrate how components interact, enabling architecture-level insights and impact analysis.
Jaeger is used to trace workflows across LLM pipelines, API gateways, autoscalers, ML inference engines (e.g., Ray Serve, Triton, vLLM), and orchestrators like PipeCat. It integrates tightly with Prometheus and Grafana to correlate traces with metrics and alerts—providing a comprehensive view of both performance and behavior. By adopting Jaeger, you can ensure its distributed systems are observable, debuggable, and latency-aware at every layer—empowering engineers to optimize performance and deliver reliable, scalable services.