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The Art of Gradual Rollouts: How to Deploy Features Safely

Learn how to use percentage-based rollouts, canary releases, and ring deployments to ship features with confidence. Reduce risk and catch issues before they affect all users.

K
Kitbase Team
·

Deploying new features is inherently risky. No matter how thorough your testing, production environments are unpredictable. Users behave in unexpected ways, edge cases emerge, and scale reveals hidden problems. Gradual rollouts are your safety net—a way to validate features with real users while limiting blast radius if something goes wrong.

What Is a Gradual Rollout?

A gradual rollout (also called a progressive rollout or incremental release) is a deployment strategy where new features are released to a subset of users first, then gradually expanded to everyone. Instead of flipping a switch and hoping for the best, you methodically increase exposure while monitoring for issues.

Day 1: 1% of users (internal team)
Day 2: 5% of users (beta users)
Day 3: 25% of users
Day 5: 50% of users
Day 7: 100% of users

At each stage, you monitor metrics, gather feedback, and decide whether to proceed, pause, or roll back.

Why Gradual Rollouts Matter

1. Limited Blast Radius

If a feature has a critical bug, only a small percentage of users are affected. Compare:

  • Big-bang release: Bug affects 100% of users immediately
  • Gradual rollout at 5%: Bug affects 5% of users; you catch it early and roll back

The difference could be between a minor incident and a major outage.

2. Real-World Validation

Staging environments can’t replicate production perfectly. Gradual rollouts let you:

  • Test with real user data
  • Validate at production scale
  • Catch issues your test suite missed

3. Data-Driven Decisions

Monitor key metrics at each stage:

  • Error rates
  • Performance metrics
  • Business KPIs
  • User feedback

If metrics degrade, you have data to support rolling back. If they improve, you have confidence to continue.

Rollout Strategies

Percentage-Based Rollouts

The simplest approach: expose the feature to X% of users.

import { FlagsClient } from '@kitbase/sdk/flags';

const flags = new FlagsClient({ token: 'your-api-key' });

// Kitbase handles the percentage calculation based on targetingKey
const showNewFeature = await flags.getBooleanValue(
  'new-dashboard',
  false,
  { targetingKey: user.id } // Used for consistent bucketing
);

Best for: General features without specific targeting requirements

Ring Deployments

Deploy in concentric rings, starting with the most tolerant users:

  1. Ring 0: Internal team and dogfooders
  2. Ring 1: Beta users and early adopters
  3. Ring 2: General availability (GA)
// Pass user attributes in context for segment-based targeting
// Targeting rules are configured in the Kitbase dashboard
const showNewFeature = await flags.getBooleanValue(
  'new-dashboard',
  false,
  {
    targetingKey: user.id,
    ring: getUserRing(user),  // 'internal', 'beta', or 'ga'
    plan: user.plan,
  }
);

Best for: Organizations with defined user tiers

Canary Releases

Route a small percentage of traffic to the new version, compare metrics, then expand:

                    ┌─────────────────┐
                    │   Load Balancer │
                    └────────┬────────┘
                             │
              ┌──────────────┴──────────────┐
              │                             │
              ▼                             ▼
    ┌─────────────────┐          ┌─────────────────┐
    │   Stable (95%)  │          │   Canary (5%)   │
    └─────────────────┘          └─────────────────┘

Best for: Infrastructure changes, backend deployments

Geographic Rollouts

Release to specific regions first:

// Pass country in context for geographic targeting
// Rules are configured in the Kitbase dashboard
const showNewFeature = await flags.getBooleanValue(
  'new-payment-flow',
  false,
  {
    targetingKey: user.id,
    country: user.country,
  }
);

Best for: Localized features, regulatory compliance

Implementing Safe Rollouts

Step 1: Define Success Metrics

Before rolling out, define what success looks like:

  • Error rate: Should stay below 0.1%
  • P95 latency: Should stay under 200ms
  • Conversion rate: Should not decrease by more than 5%
  • User complaints: Should not spike

Step 2: Set Up Monitoring

Ensure you can track metrics in real-time:

import { Kitbase } from '@kitbase/analytics';

const events = new Kitbase({ token: 'your-api-key' });

// Track feature usage
await events.track({
  channel: 'features',
  event: 'new_dashboard_loaded',
  user_id: user.id,
  tags: {
    variant: 'new',
    loadTime: performance.now() - startTime,
  },
});

// Track errors
try {
  await loadNewDashboard();
} catch (error) {
  await events.track({
    channel: 'errors',
    event: 'new_dashboard_error',
    user_id: user.id,
    tags: { error: error.message },
  });
  throw error;
}

Step 3: Create a Rollout Plan

Document your rollout stages:

StagePercentageDurationSuccess Criteria
11%24 hoursNo critical errors
210%48 hoursError rate < 0.1%
350%72 hoursMetrics stable
4100%-Full release

Step 4: Execute and Monitor

At each stage:

  1. Increase the rollout percentage
  2. Monitor metrics for the defined duration
  3. Review user feedback
  4. Decide: proceed, hold, or roll back

Step 5: Have a Rollback Plan

Know how to roll back quickly:

In Kitbase dashboard:
1. Navigate to Feature Flags → new-dashboard
2. Set rollout percentage to 0%
3. Save changes (takes effect immediately)

Flag changes propagate in real-time - no deployment needed.

Real-World Example: Payment Flow Redesign

Let’s walk through a gradual rollout of a new payment flow:

Week 1: Internal Testing (1%)

Dashboard configuration:

  • Flag: new-payment-flow
  • Percentage: 1%
  • Targeting rule: email ends with @kitbase.dev

Monitoring focus: Functional correctness, edge cases

Result: Found a bug with international addresses. Fixed before expanding.

Week 2: Beta Users (10%)

Dashboard configuration:

  • Percentage: 10%
  • Targeting rule: plan equals beta

Monitoring focus: Performance, error rates, user feedback

Result: Conversion rate slightly improved. Proceed.

Week 3: Half of Users (50%)

Dashboard configuration:

  • Percentage: 50%
  • No targeting rules (general rollout)

Monitoring focus: Scale issues, business metrics

Result: Metrics stable. Ready for full release.

Week 4: Full Release (100%)

Dashboard configuration:

  • Percentage: 100%

Next step: Clean up the flag and remove old code.

Common Mistakes to Avoid

1. Moving Too Fast

Resist the pressure to rush. Each stage needs enough time and traffic to surface issues.

Bad: 1% → 50% → 100% in one day Good: 1% → 10% → 25% → 50% → 100% over a week

2. Ignoring Metrics

Don’t just wait for users to complain. Proactively monitor:

  • Error rates and logs
  • Performance metrics
  • Business KPIs

3. No Rollback Plan

If you can’t roll back in minutes, you’re not ready to roll out.

4. Inconsistent User Experience

Ensure users see consistent behavior:

  • Same user should see the same variant
  • Don’t flip-flop between old and new

Kitbase handles this automatically with sticky bucketing.

Conclusion

Gradual rollouts are a fundamental practice for shipping features safely. By releasing to a small percentage of users first and expanding based on data, you reduce risk, catch issues early, and ship with confidence.

The key principles:

  1. Start small (1-5%)
  2. Monitor metrics closely
  3. Expand gradually
  4. Be ready to roll back
  5. Clean up when done

With tools like Kitbase, implementing gradual rollouts is straightforward. You get percentage-based targeting, real-time monitoring, and instant rollback capabilities—everything you need to deploy features safely.

Ready to ship with confidence? Start your free trial and implement gradual rollouts today.

Learn more about deployment strategies in our documentation or contact us for enterprise deployment guidance.