> For the complete documentation index, see [llms.txt](https://docs.ionos.com/cloud/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.ionos.com/cloud/observability/tracing-service/overview.md).

# Overview

Tracing Service is a fully managed, OpenTelemetry-native distributed tracing solution that allows you to ingest, store, and analyze traces generated by your applications.

Use Tracing Service to follow requests across multiple services, identify latency bottlenecks, analyze failures, and understand end-to-end application behavior. Together with Monitoring and Logging, it completes the observability triad, correlating traces, logs, and metrics in a single Grafana interface.

{% hint style="success" %}
**Note:** The Tracing Service uses [<mark style="color:blue;">Tempo</mark>](https://grafana.com/oss/tempo/) as its backend for tracing workflows so you can search, inspect, and analyze distributed traces through a managed platform experience.
{% endhint %}

## Components

![Tracing architecture](https://1737632334-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2F-MifAzdGvKLDTtvJP8sm%2Fuploads%2Fgit-blob-d3d80ef82d7be25b6b237cb988e2794c56aa5a20%2Ftracing-architecture.png?alt=media)

The architecture of the Tracing Service includes the following main components that can be used to collect traces from instrumented workloads, ingest them into the platform, and analyze them in Grafana:

* **Trace Sources:** Applications, services, SDKs, agents, and collectors instrumented with OpenTelemetry.
* **Tracing Pipeline:** A regional pipeline instance that provides authenticated ingestion endpoints for your traces.
* **Tracing Service Platform:** A managed backend that receives trace data and stores it for querying and analysis.
* **Analytics and Visualization:** Grafana with a tracing data source for exploring traces, spans, and service dependencies.

## Supported protocols

Tracing Service currently supports the following OTLP ingestion protocols:

* **OTLP/HTTP:** `https://<tracing-ingestion-endpoint>/v1/traces`
* **OTLP/gRPC:** `grpcs://<tracing-ingestion-endpoint>/v1/traces`

The service implements the open OTLP standard without proprietary agents, so it stays compatible with common OpenTelemetry SDKs such as [<mark style="color:blue;">Go</mark>](https://opentelemetry.io/docs/languages/go/), [<mark style="color:blue;">Java</mark>](https://opentelemetry.io/docs/languages/java/), [<mark style="color:blue;">Node.js</mark>](https://opentelemetry.io/docs/languages/js/), and [<mark style="color:blue;">Python</mark>](https://opentelemetry.io/docs/languages/python/). Instrumentation remains portable across environments.

## Cross-signal correlation

Tracing Service links traces to your other observability signals in the same Grafana workspace:

* **Trace-to-log:** Navigate from a latency span directly to the corresponding Loki logs in the same time context.
* **Trace-to-metric:** Move from a span to related Mimir metrics for the same request.
* **Query and visualize:** Use native TraceQL queries and service-graph visualizations to analyze dependencies and isolate slow operations.

## AI and LLM Observability

Tracing Service supports the OpenTelemetry semantic conventions for Generative AI (GenAI). It makes per-request behavior transparent and auditable in Grafana, capturing execution paths, tool calls, Large Language Model (LLM) latencies, and token consumption alongside your infrastructure signals.

## Automated provisioning

The Grafana Tempo data source is provisioned automatically in your <code class="expression">space.vars.ionos\_cloud</code> Grafana setup. After you point your OpenTelemetry exporter at the dedicated pipeline endpoint, traces are captured without additional manual configuration or infrastructure effort.

{% hint style="info" %}
**Note:** Grafana access depends on the availability of at least one tracing pipeline on the contract in the selected region.
{% endhint %}


---

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