Observability is the ability to understand system behavior from external outputs. Traditional monitoring answers 'is the system up?' Modern observability answers 'what is the system doing and why?' The distinction matters increasingly as systems grow more complex and failures become harder to predict.

The three pillars of observability are metrics, logs, and traces. Metrics provide quantitative snapshots: CPU usage, request latency, error rates. Logs capture discrete events: application events, errors, state changes. Traces track requests through distributed systems, showing where time is spent and where failures occur. Together, they enable understanding of complex systems.

Metrics are the foundation of observability. Prometheus established conventions: time-series databases storing numeric values timestamped measurements. Counters track total occurrences. Gauges measure instantaneous values. Histograms track distributions. Summary metrics aggregate percentiles. This vocabulary is so effective that Prometheus became industry standard.

Logs provide rich context that metrics cannot. A spike in error rate is concerning; an error log explaining why is actionable. However, naive logging creates volume problems—thousands of log entries per second become overwhelming. Structured logging (outputting JSON with consistent fields) makes processing and searching logs feasible. Tools like Datadog and Splunk enable searching hundreds of terabytes of logs quickly.

Distributed tracing solves the observability problem for distributed systems. When a user request flows through microservices, understanding the path is crucial. Distributed tracing instruments calls between services, creating traces showing the request path. The 99th percentile latency might be caused by one slow service or a cascade of slightly slow services. Traces answer which.

OpenTelemetry established standards for instrumentation. Libraries that follow OpenTelemetry conventions produce compatible metrics, logs, and traces regardless of where they're collected. This standardization means you can swap backends—from Datadog to New Relic to self-hosted Prometheus—without reinstrumenting code.

The economics of observability are changing. Cloud providers offer comprehensive observability platforms (CloudWatch, Stackdriver, Azure Monitor). Open-source alternatives (Prometheus, ELK Stack, Jaeger) require more operational effort but cost less. For many organizations, the decision is between self-hosting infrastructure and paying for managed services.

Future observability involves deeper integration with application code. Profiling instruments CPU and memory to identify bottlenecks. Continuous profiling captures performance data constantly rather than on-demand. AI-powered anomaly detection identifies unusual patterns humans would miss. The field is moving toward automatic instrumentation and AI-assisted analysis, reducing the manual work required to understand system behavior.