How do we tie technical wins to business outcomes?

Define SLOs per user journey (p95, error rate) and track conversion/retention deltas for cohorts exposed via feature flags. If LCP drops 300ms on product pages and conversion rises 1–2%, that’s the story for the CFO.

We’re mid-migration to microservices. Should we pause for performance?

Don’t pause; pick Playbook 2. Collapse chatty calls with a BFF, add timeouts/circuit breakers, and canary. You’ll stabilize now and keep the migration moving.

How safe is canarying performance changes?

Very, if you enforce SLO-based guardrails and auto-revert with ArgoCD/Istio. We target 10% → 50% → 100% with p95/error rate checks gating each step.

What about AI features that are slow and inconsistent?

Use Playbook 5: cache and stream for perceived speed, tier models by need, and add circuit breakers. If AI-generated code created blocking calls, run a short code rescue to refactor async boundaries.

Performance-optimization · Dec 9, 2025 · 10 minute read

Stop Guessing: Performance Playbooks That Actually Move User Metrics

Seven battle-tested optimization playbooks for the architectures you actually run—mapped to user-facing KPIs, rollout steps, and measurable impact.

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Stop Guessing: Performance Playbooks That Actually Move User Metrics

Key takeaways

Implementation checklist