Uptime
Uptime measures the percentage of time a system or service is operational and accessible to users. It reflects how reliably a platform or application is able to meet availability expectations over a defined period.
Calculation
Uptime is typically defined as any time the system is functioning as expected and available to users. This excludes periods of unplanned downtime, including outages and performance degradation. Scheduled time often excludes planned maintenance windows, depending on how availability is defined in SLAs or SLOs.
This metric is calculated by dividing total available time by total scheduled service time, then multiplying by 100:
uptime = (total available time ÷ total scheduled time) × 100
Goals
Uptime helps teams assess service reliability from a user perspective. It answers questions like:
- Are we meeting our availability targets?
- How much unplanned downtime do users experience?
- Are we improving reliability over time?
This metric is a cornerstone of SLA tracking and operational health monitoring. For foundational guidance, see Google’s SRE handbook on availability.
Variations
Uptime is sometimes referred to as Availability, especially in the context of SLAs. It is often reported as a percentage with “nines” (e.g. 99.9% or 99.99%) to denote acceptable downtime.
Common segmentations include:
- By service or endpoint, to isolate availability issues within a broader system
- By time window, such as hourly, daily, or monthly uptime
- By customer region, to detect geographic service disparities
Some teams track Downtime in minutes instead of uptime percentage. Others compare observed uptime against SLO targets to assess gap-to-goal.
Limitations
Uptime measures whether a service is accessible, but not whether it’s usable. A service might be online but degraded in performance, accuracy, or responsiveness, issues that uptime alone will not capture.
It also does not explain what caused downtime or how quickly the team responded. Without supporting incident and observability data, this metric may overstate service quality.
To gain a clearer picture of reliability, combine uptime with:
| Complementary Metric | Why It’s Relevant |
|---|---|
| Mean Time to Restore (MTTR) | Shows how quickly teams recover from downtime once it starts. |
| Change Failure Rate | Highlights whether downtime is being introduced by production releases. |
| Incident Volume | Reveals how frequently disruptions are occurring, even if uptime remains high overall. |
Optimization
Improving uptime focuses on reducing unplanned downtime and building systems that remain available under stress.
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Introduce redundancy and failover. Use multi-region deployments, load balancing, and active-passive clusters to maintain availability when components fail.
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Implement graceful degradation. Design systems to operate in a limited state rather than failing completely. This preserves partial functionality and improves user experience during incidents.
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Harden against risky changes. Use Test-Driven Development, Code Review Best Practices, and Feature Flags to ensure new deployments don’t introduce regressions that reduce uptime.
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Enhance observability and alerting. Monitor not just system health but user experience metrics like latency and error rates. This allows for earlier detection and faster response.
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Document and rehearse response playbooks. Use Incident Response guides and Game Days to prepare teams for high-severity issues and validate that recovery procedures work under pressure.
Uptime is a reflection of both system design and operational readiness. The goal isn’t just staying up, it’s delivering consistent, reliable access users can trust.
