What is Monitoring & Observability?
Monitoring tracks whether your system is healthy; observability lets you understand why it isn't when something goes wrong. Together they give you visibility into your app's behavior in production.
In plain English
A dashboard of warning lights on a car is monitoring — it tells you something is wrong. Observability is having a full diagnostic readout that shows exactly which sensor is failing and why. You need both: warning lights to catch problems fast, and diagnostics to fix them.
How it works
Monitoring collects metrics — CPU, error rate, latency, uptime — and alerts you when thresholds are crossed. Observability goes deeper with traces (following a request across services), structured logs, and custom metrics. The three pillars of observability are metrics, logs, and traces.
Why it matters for AI-built apps
AI-generated code often skips observability entirely, leaving you blind when something breaks in production. Without it, debugging a slow endpoint or a silent failure means manually sifting through logs. With it, you can pinpoint the exact function, query, or external call causing the problem in minutes.
Best practices
Instrument your app from day one — retrofitting observability is painful. Set up alerting on the metrics that matter to users: error rate, p95 latency, and uptime. Use a single platform (Datadog, Grafana, or similar) to correlate metrics, logs, and traces in one place.
Frequently asked questions
What's the difference between monitoring and observability?
Monitoring answers 'is it broken?' with predefined metrics and alerts. Observability answers 'why is it broken?' by letting you explore arbitrary questions about your system's internal state.
What tool should I start with?
Sentry covers errors and traces for free at low volume. For metrics and uptime, Datadog or Grafana Cloud have generous free tiers to get started.
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