Observability
Observe at the scale that you release.
Connect production signals to releases, flags, users, and agent behavior. Detect issues early, diagnose root cause quickly, and automate remediation when guardrails are breached.
Insight
Forensics that put you on a faster path.
Cut through the noise by connecting errors, system telemetry, and user impact directly to the changes that caused them.
Collect high-fidelity failure data in real time with Logging and Error Monitoring so you can identify what broke, how often a failure occurred, and which release introduced it.
Investigate where a request went (and where it failed) by pivoting from errors to logs and distributed traces with Logs and Traces.
Examine flag evaluations by user, cohort, and variation with Flag Audiences to identify who was exposed, what they experienced, and scope the blast radius.
Trace prompts, responses, and model calls to connect AI behavior to releases and user impact with LLM observability—and act before it drifts too far.
Use Session Replay to see what a user experienced before, during, and after an issue—without manual reproduction.
Identify friction or unexpected behavior introduced by a release by visualizing user interaction patterns with Heatmaps.
Capture in-app, flag-aware feedback to understand user sentiment and link comments directly to sessions and observed behavior.
Context
Impact
Fix what broke faster than you thought possible.
Resolve issues at the point of release with Vega, the LaunchDarkly observability agent that explains what broke, why, and how to fix it.
Know something’s wrong in seconds with Vega’s context-aware agent surfacing, which cuts through noisy telemetry and highlights the errors, alerts, and release changes that matter.
Understand why it may have happened with Vega’s root cause reasoning—which performs the same change analysis that developers do manually across code, flags, and environments—in seconds instead of hours.
Fix issues faster with Vega’s AI-generated remediation and rollback guidance (including GitHub-ready fixes) so teams can reduce MTTR without risky guesswork.
Release quickly without sacrificing safety.
Ship confidently by guarding every release with real-time performance insight tied to feature flags and versions.
Prevent risky releases from ever reaching customers.
Roll out changes progressively and monitor key signals with Guarded Releases to stop risky releases before they escalate.
Spot performance risk as exposure increases.
Track service and endpoint performance across environments and flag variations with APM to catch regressions early.
Keep undesired changes out of production.
Automatically roll back undesired changes when performance thresholds are breached—no redeploys, no downtime.
We've revolutionized our approach to problem-solving, identifying root causes in a tenth of the time it used to take. This leap forward delivers substantial benefits and a clear return on investment.
Abraham Soto
Director of Innovation
