Newsbite analysis · Cloud, DevOps, Programming
Dev.to
Your average latency is lying to you
In this insightful article, the author critiques the use of average latency metrics in assessing application performance, arguing that they can mask serious delays that negatively impact user experience. Instead, percentiles such as p90, p95, and p99 provide a clearer picture of how latency varies among users, particularly revealing how many users face slower response times. This focus on tail metrics helps developers understand the true performance challenges their applications face.
The author emphasizes that monitoring only averages may lead teams to overlook significant performance issues that could result in customer dissatisfaction or churn. By analyzing latency through percentiles, teams can prioritize improvements that enhance the user experience and maintain a dependable service. This approach calls for a reevaluation of metrics dashboard practices, suggesting a shift away from average latency displays to more informative percentile-based visualizations.
MonitorConfidence: MediumShort term horizon
Published 5 Sept 2026, 18:50 UTC11 min read
Lead image — Dev.to
Brief
NewsBite reading:Understanding Latency: Why Averages Can Mislead Performance Insights
TL;DR
- Average latency can hide significant performance issues.
- Percentiles (p90, p95, p99) provide more meaningful performance insights.
- User experience is impacted by outlier latency, not just average speed.
- Averages should be removed from latency dashboards.
- Percentile metrics help developers focus on service reliability.
Interesting facts
- The average is often not representative of user experience.
- The concept of coordinated omission can skew benchmarking results.
- p100 is theoretically impossible as it cannot be a value greater than all others.
What changed
The shift in focus from average latency to percentile metrics for understanding application performance.
Unchanged: The relevance of average latency as a general metric but now deemed inadequate for customer experience assessments.
What we still don't know
- Specific examples of companies that successfully adopted percentile metrics.
- Potential challenges developers may face when transitioning from average to percentile metrics.
Decision signal
Decision brief
Actionable takeaways
- Prioritize percentile metrics over averages in performance monitoring.
- Audit existing metrics dashboards for accuracy in reporting.
- Educate teams on the differences between average and percentile metrics.
- Use tools that support histogram-based latency metrics.
Decision signal
Monitor
Technical significance
Meaningful
Competitive impact
Strengthens position
Source reliability
Medium
Impact certainty
Medium
Novelty
Medium
Execution risk
Low
Strategic weight
Medium
Trend: Mature
- Shift towards precise performance monitoring in software engineering
- Growing adoption of observability tools that support advanced metrics
Personalized guidance
Audience lens
Developers
- Adopt percentile metrics for critical performance evaluations.
- Engage in discussions about proper metrics usage within teams.
Sentiment signal
Sentiment
Overall: NeutralMood: Cautious
The tone of the article is cautionary yet informative, emphasizing the need for change in how performance metrics are interpreted and used.
Drivers
Critique of average metrics in favor of more accurate percentilesCall for better user experience measurement
By category
programmingPositive
Highlighting the importance of accurate performance metrics in software development boosts overall service reliability efforts.
devopsPositive
Improved metric strategies enhance operations and maintain service quality, which is crucial for DevOps effectiveness.
Market signal
Impact & reach
Reevaluation of how latency metrics are used in performance monitoring.
The article highlights a critical aspect of performance measurement that directly impacts user retention and satisfaction. By moving to a more precise understanding of latency, developers can ensure a more reliable user experience, ultimately leading to better business outcomes.
developersPositive
Developers will gain valuable insights into user experience and can target performance improvements more effectively.
Geographic focus
globalNeutral
The principles discussed are applicable to any market with online services.
Risk signal
Risk radar
Cybersecurity riskLow
No cybersecurity issues are highlighted.
Data governance riskLow
Data governance is not a topic covered.
Reputational riskMedium
Inaccurate metrics could lead to reputational damage if user satisfaction declines.
Execution riskMedium
Implementing new metrics could face resistance from teams accustomed to averages.
Infrastructure riskLow
The article focuses on performance metrics rather than infrastructure risks.
Geopolitical riskLow
No geopolitical implications are evident in the article.
Regulatory riskLow
No regulatory issues are addressed.
Supply chain riskLow
No supply chain concerns are mentioned.
Talent displacement riskLow
No implications for workforce changes are discussed.
AI liability riskLow
No AI-related risks are mentioned.