Production Readiness: Complete Pre-Launch Checklist

Shipping code to production is not a flip of a switch. Every deployment carries risk: missed edge cases, missing observability, unaligned team response plans, and cascading failures nobody anticipated. A production readiness checklist is your defense—a structured framework ensuring your team has visibility, alerting, and runbooks in place before the first error lands in production. Without it, you're flying blind. With it, you catch problems before customers do.

Whether you're deploying a new service, a major feature, or updating a critical path, this checklist covers the essentials: error tracking instrumentation, alert thresholds, runbook links, and validation steps. Your goal is simple: arrive at launch day confident that your team can detect and respond to failures fast.

Pre-Launch Infrastructure & Observability

Before deploying, you need the plumbing in place. Error tracking, logging, and monitoring infrastructure should be live and tested on staging—not scrambled together when alerts start firing.

Error tracking setup: Integrate your error tracker (LightTrace accepts unmodified Sentry SDKs) into your application, staging, and pre-production environments. Confirm it's actually ingesting errors by triggering a test exception in each environment. Many teams skip staging validation and discover on live launch that SDK configuration is broken, DSN is wrong, or network policies block inbound telemetry.

Structured logging: Implement structured logging best practices so your logs are searchable and correlated with error traces. Plain log lines are noise under pressure; structured fields (request ID, user ID, service name, environment) let you diagnose patterns fast.

Distributed tracing: If you're deploying a new service or cross-service flow, configure tracing headers (baggage, trace ID propagation) across all services that touch the user request. Test a single flow end-to-end on staging and verify the span waterfall is complete and correlation IDs flow through.

Instrumentation & Data Quality

Correct data is the foundation of fast incident response. Weak instrumentation leads to guessing.

Breadcrumbs and context: Configure your SDK to capture breadcrumbs—user actions, database calls, HTTP requests—leading up to an error. Set custom tags (environment, feature flag state, user cohort) and user context (ID, email) so you can segment issues and understand impact.

Error grouping: Understand how your error tracker fingerprints issues. By default, it groups by stack trace, but you may want fingerprint customization for a noisy error that occurs across many code paths. Configure grouping before launch to avoid wasting time triaging duplicate issues.

Alert thresholds: Decide on alert rules: New high-severity issue? Spike in error rate (e.g., >10 errors/minute for a 5-minute window)? Specific error threshold (e.g., more than 100 NullPointerException in an hour)? Set these thresholds conservatively on launch day—you'd rather be alerted and it be a blip than miss a real outage. Tune thresholds after one week of live data.

Event quotas: If your plan has event limits, understand your expected traffic volume and reserve headroom. A spike above quota silently drops events; you'll miss the very errors you're trying to catch.

Response & Remediation

An error tracker is only useful if your team knows what to do when alerts fire. Define the response protocol before launch.

Alert routing: Decide who gets notified, on what channels, and for which alerts. LightTrace sends alerts via email; ensure the right people are subscribed to the email list (often @oncall or a rotation). Test that alerts actually arrive and no one's email rule is silently filtering them into a folder.

Runbooks & escalation: Link runbooks in your error tracker—attach documentation to alert rules or high-severity issues. Include: what this error means, common causes, triage steps, and escalation paths. A runbook like "Check recent deployments, review memory metrics, check downstream service health" saves 15 minutes during an incident.

Triage process: Define your error triage process. Who triages? When? Daily? On-demand? What information do they need to decide if an error is critical (needs immediate fix), high (next sprint), or low (backlog)? Align on severity definitions with your team before incidents.

Reduce MTTR: Every minute your team spends navigating to logs, cross-referencing services, or reconstructing context is lost time. Set up quick links from your error tracker to your deployment history, dashboards, and logs. The goal is reduce MTTR by automating triage retrieval.

Pre-Launch Testing & Validation

Never trust a system you haven't tested. Validation on staging catches configuration errors, misaligned expectations, and missing integrations.

End-to-end error flow: Deploy your code to staging and trigger an error deliberately (bad request, database connection failure, authorization error). Verify the error arrives in your tracker, is grouped correctly, and has the full stack trace plus breadcrumbs and tags. Check that source maps or symbol files resolved correctly (frame line numbers match your source code).

Alert validation: Trigger your alert thresholds on staging. Send 10 errors in a minute and confirm the spike alert fires and the right people receive the notification. Check that alert rules are not firing on normal staging traffic (false positives waste credibility).

Error budget alignment: Understand your error budgets and SLOs. If your SLO is 99.9% uptime, you have roughly 43 minutes of downtime per month. Does this error threshold align with your budget? Will you burn your entire budget in one incident, or does the threshold leave headroom for rare blips?

Load & stress: If you're expecting traffic to spike on launch, load-test on staging with expected throughput. Does your error tracker's ingest keep up? Do alert thresholds trigger appropriately under load? Some teams see cascading failures because the monitoring system itself is overwhelmed.

Post-Launch Handoff

Launch is not the end—it's the beginning of production vigilance. Establish a smooth handoff to operations and on-call.

Live monitoring: Keep dashboards open during the launch window. Watch error rate, transaction latency, and alert firing rate. If you see anomalies (even small ones), investigate immediately; production is not the time to optimize.

Escalation hotline: Have a quick way for your on-call to escalate to developers if an error is not in the runbook. A Slack channel, an email, a phone line—whatever your team uses.

Feedback loop: After launch, gather feedback. Did the alert thresholds work? Did the runbook help or was it outdated? Did the error grouping cause confusion? Refine the checklist for the next deployment.

The Checklist at a Glance

Before deploying to production:

• Error tracker (LightTrace) configured and tested on staging
• Source maps / symbols uploaded and verified
• Structured logging and breadcrumbs enabled
• Custom tags and user context configured
• Fingerprinting rules set for known noisy errors
• Alert rules (new issue, spike, threshold) defined and tested
• Alert routing verified (emails delivered, on-call subscribed)
• Runbooks written and linked in your tracker
• Team response process (triage, escalation, MTTR targets) documented
• Staging validation completed: errors ingested, alerts fired, stack traces resolved
• Load test on staging with expected launch traffic
• On-call briefing completed; team knows how to use the tracker
• Monitoring dashboards set up and visible during launch window

A comprehensive checklist like this feels heavy, but it's the difference between a calm launch and a chaotic one. Incidents will happen—they always do. The checklist ensures that when they do, your team is equipped to respond fast and learn from what broke.

Production readiness is not a burden; it's insurance. Every hour you invest in this checklist saves you days of firefighting later. Ship with confidence.