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

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1. Get your Credentials

  1. Log into your Dynatrace environment
  2. Generate an API Token:
    • Go to Settings → Integration → API tokens
    • Click Generate token
    • Give it a name (e.g., openlit-token)
    • Enable these scopes:
      • openTelemetryTrace.ingest - for trace ingestion
      • metrics.ingest - for metrics ingestion
      • logs.ingest - for logs ingestion (optional)
    • Click Generate and copy the token
  3. Get your Environment ID:
    • Your Dynatrace URL format: https://{environment-id}.live.dynatrace.com
    • Extract the {environment-id} part from your Dynatrace URL
  4. Construct your OTLP endpoint:
    • Format: https://{environment-id}.live.dynatrace.com/api/v2/otlp
    • Example: https://abc12345.live.dynatrace.com/api/v2/otlp

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2. Instrument your application

For direct integration into your Python applications:
import openlit

openlit.init(
  otlp_endpoint="https://YOUR_ENVIRONMENT_ID.live.dynatrace.com/api/v2/otlp", 
  otlp_headers="Authorization=Api-Token YOUR_DYNATRACE_API_TOKEN"
)
Replace:
  1. YOUR_ENVIRONMENT_ID with your Dynatrace environment ID.
    • Example: abc12345.live.dynatrace.com
  2. YOUR_DYNATRACE_API_TOKEN with the API token you generated in Step 1.
Refer to the OpenLIT Python SDK repository for more advanced configurations and use cases.

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3. View your telemetry in Dynatrace

Once your AI application starts sending telemetry data, you can explore it in Dynatrace:
  1. Navigate to Observability: Go to Observe and explore in your Dynatrace environment
  2. Distributed traces: View Distributed traces to see your AI application traces with LLM calls and vector operations
  3. Services: Check Services to monitor your AI service performance and dependencies
  4. Metrics: Explore custom metrics in Metrics for token usage, costs, and AI-specific KPIs
Your OpenLIT-instrumented AI applications will appear automatically in Dynatrace with comprehensive observability including LLM costs, token usage, model performance, and vector database operations.