Control the end-to-end AI lifecycle

Run evaluations, monitor behavior, and fix issues instantly, without redeploys.

Evaluate offline, then prove with holdouts, shadow traffic, and pass/fail rules before you scale.

Detect quality or behavior changes with judge scores and traces, see exactly what changed, and get actionable alerts.

Restore the last known good variation or redirect traffic in seconds – no code push required.

Validate changes safely

Evaluate offline, then prove with real traffic. Scale only what works.

Evaluate offline, then prove with real traffic. Scale only what works.

Use offline evals to pre-vet changes before production checks.

Run holdouts, shadow tests, or targeted rollouts with LLM-aware observability to validate under live conditions.

Use quality, latency, and cost thresholds to decide when to scale up or roll back.

Track how quality shifts over time, trace what changed between versions, and know exactly when to take action.

Track how quality shifts over time, trace what changed between versions, and know exactly when to take action.

Judge scores, latency, tokens, and cost per configuration or version.

Follow completions, tool calls, and prompt flows; compare versions to see which change triggered the shift.

Get notified on thresholds and choose to pause exposure, investigate deeper, or roll back instantly.

Restore a healthy state or reroute traffic fast: no code push required.

Restore a healthy state or reroute traffic fast: no code push required.

Roll back immediately to the most recent healthy variation.

Use targeting rules and guardrails to find the right model or configuration for quality, speed, and cost.

Update prompts, parameters, or models with approvals and an audit trail.

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If prompts were only on the backend, only the backend people could modify them. But since they're a flag in LaunchDarkly, the product managers, front-end developers, or even the designers might have access to modifying them if they want to test something out.
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