Dash0

To send OpenTelemetry metrics and traces generated by Shield360 from your AI Application to Dash0, follow the below steps.

1. Dash0 Setup

graph LR
    A[AI Application<br/>Instrumented by Shield360] --> B[OpenTelemetry Collector<br/>or Dash0 OTLP Endpoint]
    B --> C[Dash0]

Note

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.

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...

Note

You can send telemetry directly to Dash0’s OTLP endpoint, or route it through an OpenTelemetry Collector for additional processing and filtering.

2. Instrument your application

SDK

For direct integration into your Python applications:

Function Arguments

import shield360

shield360.init(
  otlp_endpoint="https://ingress.eu-west-1.aws.dash0.com:4318",
  otlp_headers={"Authorization": "Bearer auth_your_token_here"}
)

Replace:

1. ingress.eu-west-1.aws.dash0.com:4318 with your Dash0 ingestion endpoint
2. auth_your_token_here with your Dash0 authorization token

Environment Variables

import shield360

shield360.init()

Set these environment variables:

export OTEL_EXPORTER_OTLP_ENDPOINT="https://ingress.eu-west-1.aws.dash0.com:4318"
export OTEL_EXPORTER_OTLP_HEADERS="Authorization=Bearer auth_your_token_here"

Replace:

1. ingress.eu-west-1.aws.dash0.com:4318 with your Dash0 ingestion endpoint
2. auth_your_token_here with your Dash0 authorization token

Refer to the Shield360 SDK configuration reference for more advanced configurations and use cases.

CLI

For zero-code auto-instrumentation via command line:

CLI Arguments

# Using CLI arguments
shield360-instrument \
  --otlp-endpoint "https://ingress.eu-west-1.aws.dash0.com:4318" \
  --otlp-headers "Authorization=Bearer auth_your_token_here" \
  --service-name "my-ai-service" \
  --deployment-environment "production" \
  python app.py

Replace:

1. ingress.eu-west-1.aws.dash0.com:4318 with your Dash0 ingestion endpoint
2. auth_your_token_here with your Dash0 authorization token

Environment Variables

# Set environment variables (takes precedence over CLI args)
export OTEL_EXPORTER_OTLP_ENDPOINT="https://ingress.eu-west-1.aws.dash0.com:4318"
export OTEL_EXPORTER_OTLP_HEADERS="Authorization=Bearer auth_your_token_here"
export OTEL_SERVICE_NAME="my-ai-service"
export OTEL_DEPLOYMENT_ENVIRONMENT="production"

# Run your application
shield360-instrument python app.py

Replace:

1. ingress.eu-west-1.aws.dash0.com:4318 with your Dash0 ingestion endpoint
2. auth_your_token_here with your Dash0 authorization token

Refer to the Shield360 SDK configuration reference for more advanced configurations and use cases.

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 Shield360-instrumented AI applications will appear automatically in Dash0 with comprehensive observability including LLM costs, token usage, model performance, and GPU metrics.