> ## Documentation Index
> Fetch the complete documentation index at: https://docs.openlit.io/llms.txt
> Use this file to discover all available pages before exploring further.

# Dash0

> LLM Observability with Dash0 and OpenLIT

<Frame>
  <img src="https://mintcdn.com/openlit/5gjt55mJg-FqwcfK/images/dash0-traces.png?fit=max&auto=format&n=5gjt55mJg-FqwcfK&q=85&s=284b9c456b8ff421259dbee62bcd6900" width="5332" height="2604" data-path="images/dash0-traces.png" />
</Frame>

<Frame>
  <img src="https://mintcdn.com/openlit/5gjt55mJg-FqwcfK/images/dash0-metrics.png?fit=max&auto=format&n=5gjt55mJg-FqwcfK&q=85&s=40e88e8b3d020909b3f23595ca322238" width="5325" height="2583" data-path="images/dash0-metrics.png" />
</Frame>

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

### 1. Dash0 Setup

```mermaid theme={null}
graph LR
    A[AI Application<br/>Instrumented by OpenLIT] --> 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](https://www.dash0.com) for a 14-days free trial, if you don't have an account yet.
</Note>

#### 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.
</Note>

### 2. Instrument your application

<Tabs>
  <Tab title="SDK">
    **For direct integration into your Python applications:**

    <Tabs>
      <Tab title="Function Arguments">
        ```python theme={null}
        import openlit

        openlit.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
      </Tab>

      <Tab title="Environment Variables">
        ```python theme={null}
        import openlit

        openlit.init()
        ```

        Set these environment variables:

        ```shell theme={null}
        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
      </Tab>
    </Tabs>

    Refer to the OpenLIT [Python SDK repository](https://github.com/openlit/openlit/tree/main/sdk/python) for more advanced configurations and use cases.
  </Tab>

  <Tab title="CLI">
    **For zero-code auto-instrumentation via command line:**

    <Tabs>
      <Tab title="CLI Arguments">
        ```shell theme={null}
        # Using CLI arguments
        openlit-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
      </Tab>

      <Tab title="Environment Variables">
        ```shell theme={null}
        # 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
        openlit-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
      </Tab>
    </Tabs>

    Refer to the OpenLIT [Python SDK repository](https://github.com/openlit/openlit/tree/main/sdk/python) for more advanced configurations and use cases.
  </Tab>
</Tabs>

### 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.