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High-Level Design
SLO Design: Error Budgets, Burn Rate Alerts, and the Reliability Tradeoff
A concrete methodology for designing Service Level Objectives that balance reliability and velocity. Covers the SLI → SLO → SLA hierarchy, error budget arithmetic, burn rate alerting (the system Google uses in production), multi-window alert design, the reliability vs. feature velocity tradeoff, and the common SLO design mistakes that cause alert fatigue or miss real incidents. A staff-level differentiator in SRE and senior infrastructure interviews.
Why SLOs Are the Most Misunderstood Tool in Reliability Engineering
Most teams think about reliability in terms of uptime: "we target ~99.9% availability." This framing has two problems. First, it gives on-call engineers nothing actionable — ~99.9% availability means 8.7 hours of downtime per year, but knowing that number does not tell you whether you should be paging someone right now. Second, it treats reliability as binary — either you are up or you are down — when the real problem is almost always degradation: some requests succeed, some time out, P99 latency is up while P50 is fine.
Service Level Objectives (SLOs) solve this by making reliability measurable and actionable:
- Measurable: Instead of "we're up," you know "around 3.2% of checkout requests failed in the past hour, which is 2.4x our 30-day error rate."
- Actionable: Instead of "we're burning error budget," you know "at current burn rate we will exhaust the month's error budget in 4 hours, which means we need to mitigate now, not after morning standup."
The Google SRE team credits SLOs with making their reliability/velocity tradeoff explicit and data-driven (see https://sre.google/workbook/implementing-slos/). Before SLOs, the reliability team would veto any deploy that risked an outage. With SLOs, the question becomes: does the remaining error budget allow this deploy? If yes, ship it. If no, wait until the budget recovers.
This is the frame interviewers want to see at staff level: SLOs as a mechanism for making reliability decisions, not just a metric on a dashboard.