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To send OpenTelemetry metrics and traces generated by OpenLIT from your AI Application to Dash0, follow the below steps.

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1. Dash0 Setup

Prerequisites: You’ll need a Dash0 account and authorization token. Sign up at dash0.com for a 14-days free trial, if you don’t have an account yet.

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Get your Dash0 credentials

  1. Log into your Dash0 account
  2. Navigate to Organization Settings → Auth Tokens
  3. Create a new token or copy an existing one
  4. Note your Dash0 OTLP ingestion endpoint (e.g., ingress.eu-west-1.aws.dash0.com:4318 for HTTP or :4317 for gRPC)
  5. Your token will be in the format Bearer auth_xxxxx...
You can send telemetry directly to Dash0’s OTLP endpoint, or route it through an OpenTelemetry Collector for additional processing and filtering.

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2. Instrument your application

For Kubernetes deployments with zero-code instrumentation:

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Option 1: Direct to Dash0 Endpoint

apiVersion: openlit.io/v1alpha1
kind: AutoInstrumentation
metadata:
  name: dash0-instrumentation
  namespace: default
spec:
  selector:
    matchLabels:
      instrument: "true"
  python:
    instrumentation:
      provider: "openlit"
      version: "latest"
  otlp:
    endpoint: "https://ingress.eu-west-1.aws.dash0.com:4318"
    headers:
      Authorization: "Bearer auth_your_token_here"
    timeout: 30
  resource:
    environment: "production"
    serviceName: "my-ai-service"
Replace ingress.eu-west-1.aws.dash0.com:4318 with your Dash0 ingestion endpoint and auth_your_token_here with your actual Dash0 authorization token.

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Option 2: Via OpenTelemetry Collector

For more advanced scenarios with filtering, batching, or multi-destination routing, deploy an OpenTelemetry Collector: 
apiVersion: openlit.io/v1alpha1
kind: AutoInstrumentation
metadata:
  name: dash0-instrumentation
  namespace: default
spec:
  selector:
    matchLabels:
      instrument: "true"
  python:
    instrumentation:
      provider: "openlit"
      version: "latest"
  otlp:
    endpoint: "http://otel-collector.opentelemetry.svc.cluster.local:4318"
    timeout: 30
  resource:
    environment: "production"
    serviceName: "my-ai-service"
Configure your OpenTelemetry Collector to forward to Dash0: 
exporters:
  otlp/dash0:
    auth:
      authenticator: bearertokenauth/dash0
    endpoint: ingress.eu-west-1.aws.dash0.com:4317

extensions:
  bearertokenauth/dash0:
    scheme: Bearer
    token: ${env:DASH0_AUTH_TOKEN}

service:
  extensions:
    - bearertokenauth/dash0
  pipelines:
    traces:
      exporters:
        - otlp/dash0
Refer to the OpenLIT Operator Documentation for more advanced configurations and use cases.

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3. View your telemetry in Dash0

Once your AI application starts sending telemetry data, you can explore it in Dash0:
  1. Traces: Navigate to Traces to view your AI application traces with LLM calls, prompts, completions, and token usage
  2. Services: Check Services to monitor your AI service performance, error rates, and latency
  3. Metrics: Explore metrics for token usage, costs, and AI-specific KPIs
  4. Dashboards: Create custom dashboards to track token consumption, model performance, and business metrics
  5. Query: Use PromQL-based queries to filter and analyze telemetry by model, token usage, or errors
Your OpenLIT-instrumented AI applications will appear automatically in Dash0 with comprehensive observability including LLM costs, token usage, model performance, and GPU metrics.