LLM Observability with OpenTelemetry Collector and OpenLIT
To send OpenTelemetry metrics and traces generated by OpenLIT from your AI Application to an OpenTelemetry Collector, follow the below steps.The OpenTelemetry Collector is a vendor-agnostic way to receive, process, and export telemetry data. It can act as an intermediary to route your OpenLIT data to multiple backends or apply processing transformations.
YOUR_COLLECTOR_ENDPOINT with your OpenTelemetry Collector endpoint from Step 1.
Kubernetes cluster: http://my-otel-collector:4318
External: http://your-collector-host:4318
When deploying the OpenTelemetry Collector in the same Kubernetes cluster, use the service name and namespace for the endpoint (e.g., http://my-otel-collector.monitoring.svc.cluster.local:4318).
Once your LLM application is sending data to the OpenTelemetry Collector, configure exporters to send data to your preferred observability backends:Popular Exporter Configurations:
# Check collector logsdocker logs <collector-container-id># Or for Kuberneteskubectl logs -l app.kubernetes.io/name=opentelemetry-collector# Health check endpoint (if enabled)curl http://localhost:13133/
Benefits of Using OpenTelemetry Collector:
Vendor Agnostic: Route data to multiple backends simultaneously
Data Processing: Apply transformations, filtering, and sampling
Protocol Translation: Convert between different telemetry formats
Buffering & Reliability: Handle network issues and backend outages
Cost Optimization: Sample and filter data to reduce costs
Security: Add authentication, encryption, and data anonymization
Your OpenLIT-instrumented AI applications will send telemetry data to the Collector, which can then process and route it to any number of observability backends, providing flexibility and powerful data processing capabilities for your LLM monitoring infrastructure.