DevOps & Infrastructure

Datadog APM Monitoring

Instrument applications with Datadog APM for distributed tracing, error tracking, and performance monitoring with AI-assisted configuration.

Claude Code Codex Cursor

Overview

Datadog APM provides distributed tracing and application performance monitoring for modern applications. It automatically instruments popular frameworks and libraries, traces requests across microservices, and provides detailed performance analytics. AI agents can set up the Datadog agent, configure tracing libraries, create custom metrics, and build monitors and dashboards.

AI coding agents help with Datadog because the setup involves multiple components: the Datadog Agent (which collects and forwards data), language-specific tracing libraries (dd-trace), and configuration for custom tags, resource names, and sampling rules. Your agent can generate the correct installation commands, configure the tracing library for your framework (Express, Next.js, Django, Flask), and set up environment variables for proper service naming and environment tagging.

For production monitoring, your AI agent can create Datadog monitors based on APM metrics, set up service-level objectives (SLOs) from trace data, configure error tracking rules, and build dashboards that visualize request latency distributions, error rates, and throughput across your services. The agent can also help optimize tracing costs by configuring sampling rules and filtering out noisy spans.

Who Is This For?

Backend developers instrumenting Node.js or Python apps with Datadog APM
SRE teams setting up distributed tracing across microservices
DevOps engineers configuring Datadog monitors and alerting
Teams implementing service-level objectives based on APM data

Installation

Setup for Claude Code

Install Datadog Agent: see https://docs.datadoghq.com/agent/
For Node.js: npm install dd-trace
Claude Code generates tracing configs and monitor definitions

Configuration

// Instrument Node.js app (must be first import)
import "dd-trace/init";

// Or with custom config:
import tracer from "dd-trace";
tracer.init({
  service: "my-app",
  env: "production",
  version: "1.0.0",
  logInjection: true,
  runtimeMetrics: true,
});

# Environment variables
# DD_AGENT_HOST=localhost
# DD_TRACE_AGENT_PORT=8126
# DD_SERVICE=my-app
# DD_ENV=production