Gradual rollouts are a critical component of modern software delivery, allowing teams to release new features to a small percentage of users first, monitor for issues, and incrementally expand coverage. LaunchDarkly is a popular feature management platform that provides sophisticated targeting and rollout capabilities, while Claude Code brings intelligent automation to the entire process. This tutorial shows you how to combine these tools for efficient gradual feature rollouts.
Understanding the Gradual Rollout Pattern
Gradual rollouts (also called phased rollouts or percentage-based releases) involve releasing a feature to a subset of users before making it available to everyone. This approach reduces risk by limiting the blast radius of potential issues while still providing real-world feedback quickly.
The typical rollout progression looks like this:
- Start with 1-5% of users (often internal users or early adopters)
- Monitor metrics and error rates closely
- Increase to 10-25% if everything looks good
- Expand to 50% and then 100% over time
- Remove the feature flag once the rollout is complete
LaunchDarkly provides the infrastructure for this pattern with its percentage-based targeting, while Claude Code can automate flag creation, configuration, and cleanup operations.
Setting Up LaunchDarkly with Claude Code
Before implementing gradual rollouts, you need to configure LaunchDarkly and connect it with Claude Code. The integration typically involves installing the LaunchDarkly SDK and setting up authentication.
First, install the LaunchDarkly SDK for your language:
npm install launchdarkly-node-server-sdk
Then configure the client with your SDK key:
// lib/launchdarkly.ts
import * as LaunchDarkly from 'launchdarkly-node-server-sdk';
const ldClient = LaunchDarkly.init(process.env.LAUNCHDARKLY_SDK_KEY!, {
logger: LaunchDarkly.basicLogger,
flushInterval: 5,
allAttributesPrivate: true,
privateAttributeNames: ['email', 'ip']
});
export default ldClient;
Claude Code can help you generate this setup automatically. Simply describe your requirements and it will create the appropriate configuration files with proper TypeScript types and error handling.
Creating Feature Flags Programmatically
One of Claude Code’s strengths is generating consistent code patterns. For LaunchDarkly, you can have Claude create flag definitions, evaluation logic, and cleanup scripts:
// flags/feature-flags.ts
import ldClient from '../lib/launchdarkly';
export interface FlagConfig {
key: string;
name: string;
description: string;
defaultValue: boolean;
rolloutStages: number[];
}
export const featureFlags: FlagConfig[] = [
{
key: 'new-checkout-flow',
name: 'New Checkout Flow',
description: 'Redesigned checkout experience',
defaultValue: false,
rolloutStages: [5, 10, 25, 50, 100]
},
{
key: 'ai-recommendations',
name: 'AI Product Recommendations',
description: 'Machine learning based product suggestions',
defaultValue: false,
rolloutStages: [1, 5, 15, 30, 100]
}
];
export async function evaluateFlag(userKey: string, flagKey: string): Promise<boolean> {
const user = { key: userKey };
return ldClient.boolVariation(flagKey, user, false);
}
This pattern allows you to maintain a centralized configuration of all your feature flags with their intended rollout stages. Claude Code can generate these configurations and keep them synchronized as your rollout strategy evolves.
Implementing User Targeting
LaunchDarkly’s power comes from its sophisticated targeting rules. You can target users based on attributes like email domain, country, account age, or custom attributes. Claude Code can help you construct complex targeting rules:
// targeting/build-targeting-rules.ts
export interface UserContext {
key: string;
email?: string;
country?: string;
accountAge?: number;
plan?: 'free' | 'pro' | 'enterprise';
}
export function buildTargetingRules(flagKey: string, rolloutPercentage: number) {
return {
flags: {
[flagKey]: {
state: 'on',
rules: [
{
variation: true,
rollout: {
variations: [
{ variation: true, weight: rolloutPercentage * 1000 },
{ variation: false, weight: (100 - rolloutPercentage) * 1000 }
]
}
}
]
}
}
};
}
The key insight is that LaunchDarkly uses deterministic hashing to ensure consistent user experience—if a user sees the new feature, they’ll continue seeing it on subsequent visits, unless you explicitly change their targeting.
Automating Rollout Progression
As your rollout progresses through stages, you need a reliable way to update flag configurations. Claude Code can help you create automation scripts that handle the progression:
// scripts/progress-rollout.ts
import ldClient from '../lib/launchdarkly';
import { featureFlags } from '../flags/feature-flags';
interface RolloutProgress {
flagKey: string;
currentStage: number;
metrics: {
errorRate: number;
p95Latency: number;
conversionDelta: number;
};
}
async function canProgressToNextStage(progress: RolloutProgress): Promise<boolean> {
const { errorRate, p95Latency, conversionDelta } = progress.metrics;
// Define your safety thresholds
const thresholds = {
maxErrorRate: 0.01, // 1% error rate
maxLatency: 500, // 500ms p95
minConversionDelta: -0.05 // Allow up to 5% conversion drop
};
return errorRate <= thresholds.maxErrorRate &&
p95Latency <= thresholds.maxLatency &&
conversionDelta >= thresholds.minConversionDelta;
}
async function updateRolloutPercentage(flagKey: string, newPercentage: number) {
await ldClient.upsertFlag(flagKey, {
on: true,
targets: [],
rules: [{
variation: true,
rollout: {
variations: [
{ variation: true, weight: newPercentage * 10000 },
{ variation: false, weight: (100 - newPercentage) * 10000 }
]
}
}]
});
console.log(`Updated ${flagKey} rollout to ${newPercentage}%`);
}
This script demonstrates a data-driven approach to rollout progression. You can integrate this with your monitoring systems to automatically advance rollouts when metrics stay within acceptable ranges.
Monitoring Rollout Health
Successful gradual rollouts require careful monitoring. Claude Code can help you set up comprehensive monitoring dashboards and alerting. Here’s a pattern for tracking rollout health:
// monitoring/rollout-metrics.ts
export interface RolloutMetrics {
flagKey: string;
timestamp: Date;
totalUsers: number;
exposedUsers: number;
errors: number;
p50Latency: number;
p95Latency: number;
p99Latency: number;
}
export function calculateHealthScore(metrics: RolloutMetrics): 'healthy' | 'warning' | 'critical' {
const errorRate = metrics.errors / metrics.exposedUsers;
const latencyDegradation = metrics.p95Latency / 200; // Baseline 200ms
if (errorRate > 0.05 || latencyDegradation > 3) {
return 'critical';
} else if (errorRate > 0.01 || latencyDegradation > 1.5) {
return 'warning';
}
return 'healthy';
}
Best Practices for Gradual Rollouts with Claude Code
When implementing gradual rollouts with LaunchDarkly and Claude Code, follow these best practices:
Start small and slow. Begin with internal users or a small percentage of early adopters. This gives you real feedback before broad exposure.
Define clear success criteria. Before each rollout stage, establish specific metrics that determine whether to proceed. Include error rates, latency, and business metrics like conversion or engagement.
Use automated progressions thoughtfully. While automation speeds up releases, ensure you have manual override capabilities. Sometimes you need to rollback quickly based on qualitative feedback.
Maintain flag hygiene. Remove old feature flags promptly after full rollout. Accumulated flags create technical debt and confusion.
Document everything. Use Claude Code to generate documentation for each flag, including its purpose, rollout history, and any known issues.
Conclusion
Combining Claude Code with LaunchDarkly creates a powerful workflow for gradual feature rollouts. Claude Code handles the repetitive code generation and automation, while LaunchDarkly provides the sophisticated targeting infrastructure. Start with simple percentage-based rollouts, then evolve toward data-driven progressions as your team matures.
The key is establishing clear patterns early: consistent flag naming, centralized configuration, automated progressions, and comprehensive monitoring. With these elements in place, you can confidently release features to millions of users while minimizing risk.
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