Datadog

Datadog is a leading SaaS observability platform. In 2025, it focuses on AI Observability (monitoring LLMs) and automated remediation with Watchdog.

When to Use

• SaaS Convenience: You want a complete solution (APM, Logs, Infra) without managing storage.
• Full Stack Visibility: Frontend RUM (Real User Monitoring) connected to Backend Traces connected to DB Metrics.
• AI Apps: Monitor token usage, latency, and costs of LLM calls.

Quick Start

Install Agent: DD_API_KEY=... bash -c "$(curl -L https://s3.amazonaws.com/dd-agent/scripts/install_script.sh)"

Enable APM (e.g. Node.js): DD_TRACE_AGENT_URL=http://localhost:8126 node --require dd-trace/init app.js

Core Concepts

Tags

The most important concept. env:prod, service:login, team:core. Filter everything by tags.

Watchdog

AI-driven anomaly detection. "Redis latency is 30% higher than normal".

APM (Application Performance Monitoring)

Automatic instrumentation of code to find slow SQL queries or API calls.

Best Practices (2025)

Do:

• Tag Everything: Use DD_TAGS to standard metadata across all hosts.
• Use Sampling: For high-volume services, sample traces to keep costs down.
• Set Budgets: Datadog is expensive. Use cost alerts.

Don't:

• Don't ignore the bill: Custom Metrics and high-volume logs can spike costs unexpectedly.

References

• Datadog Documentation