The observability vendors charge by the byte. They want you to send everything. The industry tells you to measure everything. So you instrument everything, send it all to your vendor, and wait for clarity.

Instead, you get an Observability bill that’s higher than your AWS or GCP compute costs. And you still can’t answer basic questions: Is my application healthy? Are customers experiencing problems right now? Should I be paging someone?

Even with a top-tier vendor and unlimited budget, more data doesn’t equal more clarity. You’re drowning in metrics, dashboards, and alerts — but you still don’t know what actually matters.

Learn how to reduce alert fatigue and build strategic SLOs by combining multiple KPIs into a single weighted health score. A practical approach to measuring service reliability.

Traditional infrastructure alerts page you when CPU hits 80%, but your users are fine. Meanwhile, degraded API performance goes unnoticed because no arbitrary threshold was crossed. An SLO (Service Level Objective) changes this - it’s a target reliability goal that measures what users actually experience, like “99.9% of requests succeed over 30 days.” Born from Google’s Site Reliability Engineering (SRE) practices, SLO-based alerting only pages when user experience is genuinely at risk, eliminating alert fatigue while catching real issues early.

Traditional threshold alerts fire on every spike, creating alert fatigue. Burn rate alerting is different - it tracks how quickly you’re consuming your error budget and only alerts when errors are sustained enough to threaten your reliability target. This gives you early warnings before user experience degrades, while dramatically reducing noise.

Why does a 5% error rate trigger an alert at 2 AM? Is it catastrophic during peak traffic or meaningless during low usage? Traditional static thresholds can’t tell you. SLO-based alerting asks a better question: “Are we consuming our error budget faster than planned?” This approach ties alerts directly to user-impacting reliability issues, eliminating arbitrary thresholds and reducing alert fatigue while catching real problems early.

Engineering wants to slow down and fix stability issues. Product wants to ship faster and hit deadlines. Who’s right? Both - and neither. The real question isn’t “should we prioritize reliability or velocity?” but “how much unreliability can we tolerate while still meeting our promises?” That’s your error budget: the quantitative answer that turns endless debates into data-driven decisions. With a 99.9% SLO, you get 43.2 minutes of downtime per month to spend on innovation, experiments, or controlled risks.