Question 1

What's the difference between Observability and monitoring?

Accepted Answer

Monitoring tells you whether a known problem is happening by tracking predefined signals (for example, CPU > 80% or error rate > 1%). Observability goes further: it helps you investigate unknown or new problems by letting you ask questions after the fact using rich telemetry (logs, metrics, and traces) to understand why something happened.

Question 2

When should I use Observability?

Accepted Answer

Use observability when your system is complex enough that failures are hard to diagnose with simple uptime checks—common triggers are microservices, distributed systems, frequent deployments, multi-region architectures, or strict reliability goals (SLOs). If you often ask, "It’s slow, but where is the time going?" or "Which dependency caused this error?", you’ll benefit from observability.

Question 3

How much does Observability cost?

Accepted Answer

Cost depends mainly on telemetry volume and retention: how many metrics you emit, how many logs you ingest (GB/day), how many traces/spans you sample, and how long you store them. Additional factors include query frequency, high-cardinality labels (which can increase metric costs), and whether you use managed services or self-hosted tools. A common cost-control approach is log filtering, metric aggregation, trace sampling, and shorter retention for high-volume data.

Observability

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