Telemetry & Observability Plan for Founders: Minimal Metrics, Logs & Traces to Ship with Your Brief

Keep the brief two parts: (A) what matters to the business and customers, and (B) what keeps the system healthy enough to deliver that experience. Use explicit names and examples so engineers can implement without back‑and‑forth.

Include: explicit event names, the metric type (counter, gauge, histogram), the SLI/SLO target, log level rules and retention requirements, and which user journeys should get full traces. Keep it to 8–12 items total so instrumenting feels like a checklist, not an open‑ended project.

Choose metrics that directly map to customer success and engineering action. For business metrics, instrument explicit events and counts (counters) with consistent naming: e.g., order.create_attempt, order.create_success, checkout.payment_started, checkout.payment_succeeded. These let product and ops compute conversion funnels without guessing.

For health metrics use Golden Signals: traffic (requests/sec), success_rate (or error_rate), latency percentiles (p95 or p99) and saturation (CPU, memory). Use histograms for latency to compute percentiles accurately; expose p50/p95/p99 for top‑level dashboards.

Log format and levels matter more than flood volume. Require structured JSON logs and include trace_id/correlation_id in every log line so engineers can pivot between traces and logs. Enforce semantic log levels: ERROR (action required), WARN (degraded but functional), INFO (business events), DEBUG (development only).

Set retention to control costs and keep investigatory data available: hot storage for 14–30 days for ERROR/WARN and INFO business events, short hot storage (7–14 days) for DEBUG if enabled, and archive critical security/audit logs longer if compliance requires. Review retention quarterly and reduce volume by removing DEBUG in prod and filtering high‑volume noise.

Define one end‑to‑end SLO per user journey plus one platform SLO per service. Examples: Checkout SLO — 99.9% of payment requests succeed within 1.5s (30‑day window). API availability SLO — 99.95% HTTP 2xx for public API. Compute the error budget (e.g., 0.1% downtime ≈ 43 minutes/month for 99.9%) and tie launch/rollback rules to budget burn.

Alert on error budget burn rate, not every metric blip. Create two alert tiers: (1) burn rate alert (e.g., 4x expected burn over 30m) that pages on‑call; (2) degradation alert (e.g., p95 latency above threshold for 10m) that creates a ticket and notifies slack. This keeps focus on user impact and prevents alert fatigue.

Traces are expensive; use them where they buy the most diagnostic value. Always capture 100% of traces for: payment checkout flows, signup/onboarding funnels, and any flow that directly affects revenue or compliance. For the rest, use probabilistic sampling (1–5%) plus adaptive or error‑based sampling that keeps traces with errors and high latency.

Ensure trace context is injected into logs and metrics (exemplars) so engineers can pivot from an alert to a single trace and correlated logs. Use OpenTelemetry for standard instrumentation and set span naming convention: service.operation (e.g., gateway.authenticate, payments.charge). Review sampling and retention quarterly to balance cost vs. debug value.