To send OpenTelemetry metrics and traces generated by OpenLIT from your AI Application to the OpenLIT Platform, follow the below steps.

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1. Get your Credentials

If you haven’t deployed the OpenLIT Platform yet, follow the Installation Guide to set it up. Common OpenLIT Platform endpoints:
  • Kubernetes cluster: http://openlit.openlit.svc.cluster.local:4318
  • Local development: http://localhost:4318 (using port-forward)
  • External/Ingress: Your configured external endpoint

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2. Instrument your application

For Kubernetes deployments with zero-code instrumentation: 
apiVersion: openlit.io/v1alpha1
kind: AutoInstrumentation
metadata:
  name: openlit-platform-instrumentation
  namespace: default
spec:
  selector:
    matchLabels:
      instrument: "true"
  python:
    instrumentation:
      provider: "openlit"
      version: "latest"
  otlp:
    endpoint: "YOUR_OPENLIT_PLATFORM_ENDPOINT"
    timeout: 30
  resource:
    environment: "production"
    serviceName: "my-ai-service"
Replace:
  1. YOUR_OPENLIT_PLATFORM_ENDPOINT with your OpenLIT Platform endpoint from Step 1.
    • Same cluster: http://openlit.openlit.svc.cluster.local:4318
    • External: https://your-openlit-domain.com:4318
When using the OpenLIT Operator with the OpenLIT Platform in the same cluster, the default endpoint http://openlit.openlit.svc.cluster.local:4318 is automatically configured if no explicit endpoint is provided.
Refer to the OpenLIT Operator Documentation for more advanced configurations and use cases.

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3. Access OpenLIT Platform Dashboard

Once your LLM application is instrumented, you can explore the comprehensive observability data in the OpenLIT Platform: Access the Dashboard: 
# Get the external service details
kubectl get svc -n openlit openlit

# For local access via port-forwarding:
kubectl port-forward -n openlit svc/openlit 3000:3000
# Then visit: http://localhost:3000
What You’ll See:
  1. LLM Observability Dashboard: Comprehensive view of your AI applications including:
    • Real-time Metrics: Request rates, latency, and error rates
    • Cost Tracking: Token usage and cost breakdown by model and application
    • Performance Analytics: Response times, throughput, and model performance
    • Trace Visualization: Detailed execution flow with full request/response context
  2. Vector Database Analytics: Monitor your vector database operations and performance
  3. GPU Monitoring: Track GPU utilization and performance metrics (if enabled)
  4. Custom Dashboards: Create tailored views for your specific monitoring needs
Your OpenLIT-instrumented AI applications will appear automatically in the OpenLIT Platform with comprehensive observability including LLM costs, token usage, model performance, distributed tracing, and business intelligence - all in a single, self-hosted platform designed specifically for AI workloads.