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Product docsGuidesSDKsIntegrationsAPI docsTutorialsFlagship Blog
  • Tutorials
    • Build a User Frustration Detection & Response System
    • When to use prompt-based vs agent mode in LaunchDarkly
    • When to Add Online Evals to Your AI Configs
    • Detecting User Frustration: Understanding Rage Clicks and Session Replay
    • AI Config CI/CD Pipeline: Automated Quality Gates and Safe Deployment
    • Resilient architecture patterns for LaunchDarkly's SDKs
    • Proving ROI with Data-Driven AI Agent Experiments
    • A Deeper Look at LaunchDarkly Architecture: More than Feature Flags
    • Add Observability to Your React Native App in 5 minutes
    • Smart AI Agent Targeting with MCP Tools
    • Build a LangGraph Multi-Agent System in 20 Minutes with LaunchDarkly AI Configs
    • Snowflake Cortex Completion API + LaunchDarkly SDK Integration
    • Using AI Configs to review database changes
    • How to implement WebSockets and kill switches in a Python application
    • 4 hacks to turbocharge your Cursor productivity
    • Create a feature flag in your IDE in 5 minutes with LaunchDarkly's MCP server
    • DeepSeek vs Qwen: local model showdown featuring LaunchDarkly AI Configs
    • Video tutorials
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On this page
  • Overview
  • How to roll out a feature to 1% of users and then scale safely
  • Overview
  • How to roll out a feature to 1% of users and then scale safely
TutorialsVideo tutorials

How to roll out a feature with LaunchDarkly

Overview

This walkthrough explains how to progressively release a new feature. We’ll start by rolling the feature out to a small percentage of users, and then gradually increase to 100% if the metrics look good.

How to roll out a feature to 1% of users and then scale safely

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