---
title: Choosing an Instrumentation Method for Containers
description: Datadog, the leading service for cloud-scale monitoring.
breadcrumbs: >-
  Docs > Serverless > Google Cloud Run > Choosing an Instrumentation Method for
  Containers
---

> For the complete documentation index, see [llms.txt](https://docs.datadoghq.com/llms.txt).

# Choosing an Instrumentation Method for Containers

## Set up with agentic onboarding{% #set-up-with-agentic-onboarding %}

Use agentic onboarding to set up monitoring for your Cloud Run containers with AI assistance. Agentic onboarding detects your project's frameworks, applies the required configuration in place, and verifies that data is flowing. Two complementary paths use the same Datadog account:

- **AI Setup CLI**: A standalone terminal tool. Use it when you don't want to install an MCP server.
- **MCP server**: Set up from your IDE through a coding assistant such as Claude Code or Cursor.

{% tab title="AI Setup CLI" %}
Run the CLI in your project directory (requires Node.js 22+). It links your Datadog account, then instruments your Cloud Run service:

```shell
npx @datadog/ai-setup-cli --product serverless --serverless-compute-type=gcp-cloud-run
```

Omit `--product` to run interactively, or add `--site` to target your Datadog site.
{% /tab %}

{% tab title="MCP server" %}
Use the Datadog MCP server's [`serverless_onboarding`](https://docs.datadoghq.com/agentic_onboarding/setup.md?tab=serverlessmonitoring#mcp-server) tool to set up monitoring for your Cloud Run containers with AI assistance. After you connect, try a prompt like:

```
Help me monitor my GCP Cloud Run services with Datadog using Terraform.
```

{% /tab %}

## Manual instrumentation{% #manual-instrumentation %}

To instrument your Google Cloud Run containers with Datadog, choose one of two options:

- [](https://docs.datadoghq.com/serverless/google_cloud_run/containers/in_container)
- [](https://docs.datadoghq.com/serverless/google_cloud_run/containers/sidecar)
 
- [**In-Container**](https://docs.datadoghq.com/serverless/google_cloud_run/containers/in_container.md): Wraps your application container with the Datadog Agent. Choose this option for a simpler setup, lower cost overhead, and direct log piping.
- [**Sidecar**](https://docs.datadoghq.com/serverless/google_cloud_run/containers/sidecar.md): Deploys the Datadog Agent in a separate container alongside your app container. Choose this option if you have multiple containers in a single service, if you prefer strict isolation of the Datadog Agent, or if you have performance-sensitive workloads.

### Comparison: In-Container versus sidecar instrumentation{% #comparison-in-container-versus-sidecar-instrumentation %}

| Aspect            | In-Container                                             | Sidecar                                                                                                                                                      |
| ----------------- | -------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------ |
| Deployment        | One container (your app, wrapped with the Datadog Agent) | Two containers (your app, Datadog Agent)                                                                                                                     |
| Image changes     | Increases app image size.                                | No change to app image.                                                                                                                                      |
| Cost overhead     | Less than sidecar (no extra container).                  | Extra vCPU/memory. Overallocating the sidecar wastes cost; underallocating leads to premature scaling.                                                       |
| Logging           | Direct stdout/stderr access.                             | Shared volume + log library routing to a log file. Uncaught errors require extra handling, since they are not automatically handled by your logging library. |
| Failure isolation | In rare cases, Datadog Agent bugs can affect your app.   | Datadog Agent faults are isolated.                                                                                                                           |

## Further reading{% #further-reading %}

- [Google Cloud Run Integration](https://docs.datadoghq.com/integrations/google-cloud-run.md)
- [Collect traces, logs, and custom metrics from Cloud Run services](https://www.datadoghq.com/blog/collect-traces-logs-from-cloud-run-with-datadog/)
- [Instrument your container with the in-container approach](https://docs.datadoghq.com/serverless/google_cloud_run/containers/in_container.md)
- [Instrument your container with the sidecar approach](https://docs.datadoghq.com/serverless/google_cloud_run/containers/sidecar.md)
- [Instrument Google Cloud Run applications with the new Datadog Agent sidecar](https://www.datadoghq.com/blog/instrument-cloud-run-with-datadog-sidecar/)
- [Datadog MCP Server: serverless_onboarding tool](https://docs.datadoghq.com/mcp_server/tools.md#serverless_onboarding)