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Performance Monitoring & Optimization - Coggle Diagram
Performance Monitoring & Optimization
Define KPIs & Metrics to Assess Technology Impact
✅ What’s expected
Identify meaningful technical and product metrics that tie back to business outcomes
Identify meaningful technical and product metrics that tie back to business outcomes
🛠️ Tools
Datadog / New Relic / Prometheus + Grafana – system metrics
Amplitude / Mixpanel / PostHog – product usage analytics
Metabase / Looker / Redash – business intelligence
Notion / Confluence – KPI dashboards and documentation
🏆 Best Practices
Use North Star metrics (e.g., time to deploy, checkout success rate)
Align tech KPIs with product or user outcomes (e.g., signup-to-purchase conversion)
Keep KPIs minimal but actionable (3–5 per domain)
Track Engineering Efficiency & Team Performance
✅ What’s expected
Measure how efficiently the engineering team delivers value
Use metrics to guide improvements (not micromanagement)
🛠️ Tools
Linear / Jira + Reports – cycle time, throughput
GitHub Insights / Velocity (Code Climate) / Haystack – code-level metrics
Sleuth / Dora / DX – DORA metrics (lead time, deploy frequency, MTTR, change fail rate)
Lattice / 15Five – team pulse surveys
🏆 Best Practices
Use cycle time breakdowns (coding → review → deploy) to identify slow spots
Track DORA metrics: the industry standard for elite-performing teams
Avoid vanity metrics (e.g., number of commits) — focus on outcomes and flow
Service-Level Objectives (SLOs) & Reliability Metrics
✅ What’s expected
Define SLOs, SLIs, and error budgets for critical user-facing services
Monitor uptime, latency, and error rates proactively
🛠️ Tools
Prometheus + Grafana / Datadog / New Relic – for SLO dashboards
SLO Tracker (by Nobl9 or open source SLO tools)
Sentry / Rollbar / Bugsnag – app error tracking
StatusPage / Better Uptime – public or internal uptime reporting
🏆 Best Practices
Define SLOs for high-impact endpoints (e.g., 99.9% uptime for login)
Use SLIs like latency, availability, error rate, saturation
Apply error budgeting: if you breach budget, pause feature dev to fix reliability
Product & Infrastructure Performance Optimization
✅ What’s expected
Continuously improve the performance of backend, frontend, and cloud infrastructure
Measure cost efficiency and optimize usage
🛠️ Tools
Lighthouse / WebPageTest / SpeedCurve – frontend performance
Redis / CDN / Cloudflare / Varnish – cache acceleration
AWS Cost Explorer / Finout / CloudZero / Kubecost – cloud cost tracking
k6 / Artillery / Locust / JMeter – load testing
🏆 Best Practices
Implement performance budgets (e.g., <1.5s LCP, <200ms API latency)
Monitor cost-per-transaction and cost-per-user
Periodically run load tests before major launches
Establish Feedback Loops for Continuous Improvement
✅ What’s expected
Create mechanisms for regularly reviewing and acting on performance data
🛠️ Tools
Weekly retro boards (Miro, Parabol, EasyRetro)
Monthly KPIs and postmortem review sessions
Slack alerts / Datadog monitors / PagerDuty – for real-time feedback
🏆 Best Practices
Run blameless postmortems after incidents
Share engineering KPIs in company All-Hands
Keep dashboards visible and discussed