| name | shipping-and-launch |
| description | Use when preparing to deploy to production. Use when you need a pre-launch checklist, when setting up monitoring, when planning a staged rollout, or when you need a rollback strategy. |
Shipping and Launch
Overview
Ship with confidence. The goal is not just to deploy — it's to deploy safely, with monitoring in place, a rollback plan ready, and a clear understanding of what success looks like. Every launch should be reversible, observable, and incremental.
When to Use
- Deploying a feature to production for the first time
- Releasing a significant change to users
- Migrating data or infrastructure
- Opening a beta or early access program
- Any deployment that carries risk (all of them)
The Pre-Launch Checklist
Code Quality
Security
Performance
Accessibility
Infrastructure
Documentation
Feature Flag Strategy
Ship behind feature flags to decouple deployment from release:
const flags = await getFeatureFlags(userId);
if (flags.taskSharing) {
return <TaskSharingPanel task={task} />;
}
return null;
Feature flag lifecycle:
1. DEPLOY with flag OFF → Code is in production but inactive
2. ENABLE for team/beta → Internal testing in production environment
3. GRADUAL ROLLOUT → 5% → 25% → 50% → 100% of users
4. MONITOR at each stage → Watch error rates, performance, user feedback
5. CLEAN UP → Remove flag and dead code path after full rollout
Rules:
- Every feature flag has an owner and an expiration date
- Clean up flags within 2 weeks of full rollout
- Don't nest feature flags (creates exponential combinations)
- Test both flag states (on and off) in CI
Staged Rollout
The Rollout Sequence
1. DEPLOY to staging
└── Full test suite in staging environment
└── Manual smoke test of critical flows
2. DEPLOY to production (feature flag OFF)
└── Verify deployment succeeded (health check)
└── Check error monitoring (no new errors)
3. ENABLE for team (flag ON for internal users)
└── Team uses the feature in production
└── 24-hour monitoring window
4. CANARY rollout (flag ON for 5% of users)
└── Monitor error rates, latency, user behavior
└── Compare metrics: canary vs. baseline
└── 24-48 hour monitoring window
5. GRADUAL increase (25% → 50% → 100%)
└── Same monitoring at each step
└── Ability to roll back to previous percentage at any point
6. FULL rollout (flag ON for all users)
└── Monitor for 1 week
└── Clean up feature flag
When to Roll Back
Roll back immediately if:
- Error rate increases by more than 2x baseline
- P95 latency increases by more than 50%
- User-reported issues spike
- Data integrity issues detected
- Security vulnerability discovered
Monitoring and Observability
What to Monitor
Application metrics:
├── Error rate (total and by endpoint)
├── Response time (p50, p95, p99)
├── Request volume
├── Active users
└── Key business metrics (conversion, engagement)
Infrastructure metrics:
├── CPU and memory utilization
├── Database connection pool usage
├── Disk space
├── Network latency
└── Queue depth (if applicable)
Client metrics:
├── Core Web Vitals (LCP, INP, CLS)
├── JavaScript errors
├── API error rates from client perspective
└── Page load time
Error Reporting
class ErrorBoundary extends React.Component {
componentDidCatch(error: Error, info: React.ErrorInfo) {
reportError(error, {
componentStack: info.componentStack,
userId: getCurrentUser()?.id,
page: window.location.pathname,
});
}
render() {
if (this.state.hasError) {
return <ErrorFallback onRetry={() => this.setState({ hasError: false })} />;
}
return this.props.children;
}
}
app.use((err: Error, req: Request, res: Response, next: NextFunction) => {
reportError(err, {
method: req.method,
url: req.url,
userId: req.user?.id,
});
res.status(500).json({
error: { code: 'INTERNAL_ERROR', message: 'Something went wrong' },
});
});
Post-Launch Verification
In the first hour after launch:
1. Check health endpoint returns 200
2. Check error monitoring dashboard (no new error types)
3. Check latency dashboard (no regression)
4. Test the critical user flow manually
5. Verify logs are flowing and readable
6. Confirm rollback mechanism works (dry run if possible)
Rollback Strategy
Every deployment needs a rollback plan before it happens:
## Rollback Plan for [Feature/Release]
### Trigger Conditions
- Error rate > 2x baseline
- P95 latency > [X]ms
- User reports of [specific issue]
### Rollback Steps
1. Disable feature flag (if applicable)
OR
1. Deploy previous version: `git revert <commit> && git push`
2. Verify rollback: health check, error monitoring
3. Communicate: notify team of rollback
### Database Considerations
- Migration [X] has a rollback: `npx prisma migrate rollback`
- Data inserted by new feature: [preserved / cleaned up]
### Time to Rollback
- Feature flag: < 1 minute
- Redeploy previous version: < 5 minutes
- Database rollback: < 15 minutes
Common Rationalizations
| Rationalization | Reality |
|---|
| "It works in staging, it'll work in production" | Production has different data, traffic patterns, and edge cases. Monitor after deploy. |
| "We don't need feature flags for this" | Every feature benefits from a kill switch. Even "simple" changes can break things. |
| "Monitoring is overhead" | Not having monitoring means you discover problems from user complaints instead of dashboards. |
| "We'll add monitoring later" | Add it before launch. You can't debug what you can't see. |
| "Rolling back is admitting failure" | Rolling back is responsible engineering. Shipping a broken feature is the failure. |
Red Flags
- Deploying without a rollback plan
- No monitoring or error reporting in production
- Big-bang releases (everything at once, no staging)
- Feature flags with no expiration or owner
- No one monitoring the deploy for the first hour
- Production environment configuration done by memory, not code
- "It's Friday afternoon, let's ship it"
Verification
Before deploying:
After deploying:
