LaunchDarkly Guarded Rollouts auto-pause feature releases when ML detects a regression.
LaunchDarkly separates deployment from release behind feature flags, and Guarded Rollouts progressively ramp traffic while watching conversion, latency, and custom metrics. When the ML engine detects statistically significant negative impact, the rollout pauses or rolls back at the flag layer without any redeployment. It is also the first FedRAMP-authorized feature management product.
Who it's for. Platform teams of 10 to 200 engineers at organizations where a bad release means revenue loss or compliance exposure. Scenario: a new checkout flow ships at 1 percent traffic, ramps to 5 then 10 percent as metrics hold, and at 10 percent conversion drops 2 points. The ML detects the shift, pauses the rollout, and pages the team before the blast radius widens.
Tradeoffs. Adds a runtime dependency on the relay proxy or SaaS endpoint; if flag evaluation goes down, your app needs a fallback path. Pricing scales with context instances (users + devices + services), which can grow unpredictably in microservice estates. Free tier is basic flags only; Guarded Rollouts requires Pro or Enterprise.
Compare: Statsig, Split, Optimizely, Unleash
