Get more with LaunchDarkly.

• 01Feature Management→

  Homegrown reality:

  Manual configs, hardcoded prompts, and environment drift make it unclear what’s live—or safe to change. Ownership is scattered, and every tweak (from flags to AI prompts) risks a redeploy, slowing teams down and increasing risk.

  How LaunchDarkly fixes it:

  • Change-level health monitoring tied to real user impact and your pre-defined thresholds.
  • Error and regression detection linked to the flag or version that caused them.
  • LLM observability to surface AI agent behavior drift before it escalates.
  
• 02Guarded Releases→

  Homegrown reality

  DIY systems can enable you to toggle features, but they can’t protect you when things go wrong. Without guardrails, a single bad deployment can impact thousands of users before anyone notices.

  How LaunchDarkly fixes it:

  • Guarded Releases integrates targeting, monitoring, and rollback into one workflow.
  • Set thresholds for performance/errors; automatically pause failing rollouts.
  • Recover instantly, before users are affected.
  
• 03Experimentation→

  Homegrown reality

  Impact is hard to measure; experiments run offline, and disconnected data makes it difficult to link changes to outcomes.

  How LaunchDarkly fixes it:

  • Run statistically valid A/B and multivariate tests directly inside the release workflow.
  • Tie feature exposure to behavior and engagement, see results in real time, and automatically roll out winning variations.
  • Help every team learn continuously as they build.