# LaunchDarkly

**Source:** https://geo.sig.ai/brands/launchdarkly  
**Vertical:** Product Management  
**Subcategory:** Feature Management  
**Tier:** Leader  
**Website:** launchdarkly.com  
**Last Updated:** 2026-04-14

## Summary

Enterprise feature flag and experimentation platform with $3B valuation; progressive rollouts, A/B testing, and Guarded Releases framework for safe software deployments.

## Company Overview

LaunchDarkly is a feature management and experimentation platform enabling software development teams to release features safely through feature flags, progressive rollouts, and A/B testing — without requiring code deployments to activate or deactivate functionality. Founded in 2014 in Oakland, California by Edith Harbaugh and John Kodumal and having raised over $330 million in funding at a $3 billion valuation, LaunchDarkly is the recognized leader in enterprise feature flag management and has expanded into a full feature management and experimentation platform.

LaunchDarkly's core feature flags allow development teams to deploy code to production while keeping new features invisible to users until deliberately activated — enabling trunk-based development, dark launches, and instant kill switches for problematic releases. The platform's targeting rules let teams release features to specific user segments (internal beta testers, 1% of users, users in specific geographies) before broad rollout, dramatically reducing the risk of any given software deployment.

In 2025, LaunchDarkly has evolved beyond feature flags into a broader experimentation platform — running A/B tests connected to business metrics, measuring feature impact on conversion, revenue, and user behavior. The company competes with Statsig, Split.io (now Harness), Optimizely (experimentation), and cloud-provider native feature flag services (AWS AppConfig). LaunchDarkly's 2025 strategy emphasizes its Guarded Releases framework — a structured process combining feature flags, monitoring, and automated rollback — and expanding its AI/ML feature management capabilities for teams using model versioning, prompt management, and AI feature experimentation.

## Frequently Asked Questions

### What is LaunchDarkly?
LaunchDarkly LaunchDarkly serves developers as feature management and experimentation platform with feature flags, $3B+ valuation, following 2014 Edith Harbaugh founding with John Kodumal in Oakland

### When was LaunchDarkly founded?
LaunchDarkly was founded in 2014 in Oakland, California. Edith Harbaugh and John Kodumal founded LaunchDarkly in Oakland in 2014 as feature management platform with feature flags for progressive delivery using targeting, rollouts, and kill switches with A/B testing and experimentation for developer tools and CI/CD DevOps workflows, reached $3B+ valuation with Series D $200M enabling trunk-based development, continuous delivery, deployment-release separation, software delivery performance monitoring, and canary releases.

### What are LaunchDarkly's major milestones?
LaunchDarkly's history includes several key milestones: 2014: LaunchDarkly Founded Oakland 2018: Series C $44M 2021: Series D $200M $3B+ valuation 2024: Feature Management Platform

### What is LaunchDarkly's mission?
LaunchDarkly's mission is to Control and measure everything you release.

### Who founded LaunchDarkly?
LaunchDarkly was founded by Edith Harbaugh. Engineers who pioneered feature flag management platform

### What products or services does LaunchDarkly offer?
LaunchDarkly LaunchDarkly serves developers as feature management and experimentation platform with feature flags, $3B+ valuation, following 2014 Edith Harbaugh founding with John Kodumal in Oakland

### Who uses LaunchDarkly?
LaunchDarkly LaunchDarkly serves developers as feature management and experimentation platform with feature flags, $3B+ valuation, following 2014 Edith Harbaugh founding with John Kodumal in Oakland

### What is LaunchDarkly's Guarded Releases product?
LaunchDarkly's Guarded Releases (formerly Progressive Delivery) is a capability that enables engineering teams to release new features incrementally and automatically — starting with 1% of users, monitoring real-time metrics (error rates, latency, business conversion metrics), and either automatically expanding the rollout to 100% or automatically rolling back to 0% based on metric thresholds, without requiring human intervention for each rollout decision. This automated progressive delivery approach de-risks feature releases by limiting blast radius: a bug that affects 1% of users generates 1% of the support tickets, customer complaints, and revenue impact it would cause at 100% rollout, while automated rollback means the issue is contained before the engineering team has even diagnosed it. Guarded Releases represent the evolution from 'feature flags for dark launches' to 'AI-assisted safe continuous delivery.'

## Tags

automation, b2b, enterprise, productivity, project-management, saas, collaboration

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*Data from geo.sig.ai Brand Intelligence Database. Updated 2026-04-14.*