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LaunchDarkly

LaunchDarkly is an American feature management and AI control software company founded in 2014.

Last updated September 15, 2026

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Overview

LaunchDarkly is an American feature management and AI control software company. Founded in 2014 in Oakland, California, by Edith Harbaugh and John Kodumal, it set out to make feature management accessible beyond companies that had built homegrown systems. It describes itself today as the runtime control layer for the AI era of software development, spanning software releases, experimentation, observability, and AI agent behavior in production. The company reports serving more than 5,500 customers, including roughly a quarter of the Fortune 500, and offers professional services covering platform and application readiness and resident architect engagements.

History

LaunchDarkly was founded in 2014 in Oakland, California, by Edith Harbaugh and John Kodumal, to solve a problem in modern software delivery: teams were releasing code faster in the cloud but still lacked safe, practical control over what happened after deployment. The company set out to make feature management accessible beyond the small group of companies that had built homegrown systems. Over time it expanded from helping developers soft-launch features to giving product, marketing, and business teams more control over rollouts and customer experiences. It now describes itself as the runtime control layer for the AI era of software development, having evolved from pioneering feature management into a broader platform for controlling software releases, experimentation, observability, and AI agent behavior in production.

Products and positioning

The runtime control layer for the AI era of software development.

  • Feature management platform

    Tools for progressively releasing changes and rolling back instantly based on real-time impact.

  • AI agent control

    Capabilities to control and govern AI agents in production, mitigating bad behavior and steering responses in real time.

  • Experimentation

    Experimenting with code and agents in production and optimizing based on real-world results.

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