How can we automatically pause a rollout when latency or error rate regresses based on our monitoring alerts?

Most teams want to move fast with progressive rollouts—but nobody wants to be the person who missed the latency spike or error storm and woke up the on‑call at 2am. The good news: you can automatically pause or roll back a rollout the moment your performance or error metrics regress, without shipping a new deploy.

Quick Answer: Use LaunchDarkly Guarded Releases (Automated Rollout & Monitoring) to tie rollout progress to real‑time latency and error thresholds. When metrics cross your defined limits, LaunchDarkly can automatically pause or roll back the rollout—no redeploys required.

The Quick Overview

• What It Is: A runtime safety net that links your rollouts to real‑time monitoring, so LaunchDarkly automatically pauses or rolls back when latency or error rates regress.
• Who It Is For: Engineering and SRE teams running progressive delivery who want to reduce blast radius and avoid manual decision‑making under pressure.
• Core Problem Solved: Releases fail quietly before anyone notices; by the time alerts fire and humans react, customers are already impacted. You need a control plane that reacts in milliseconds, not minutes.

How It Works

LaunchDarkly evaluates feature flags at runtime across 25+ SDKs and pushes flag updates worldwide in under 200ms. Guarded Releases sit on top of that runtime control plane: you define rollout steps and attach health rules (errors, latency, other metrics). LaunchDarkly then continuously monitors those metrics during the rollout and automatically intervenes if something goes wrong.

In practice, you:

1. Define the rollout and thresholds:
   Configure a progressive rollout for a feature flag (for example, 5% → 25% → 50% → 100%) and attach metrics like error rate and latency with specific thresholds.

2. Stream metrics in and monitor in real time:
   Connect your metrics via LaunchDarkly SDKs, APIs, Sentry, or OpenTelemetry. LaunchDarkly tracks those metrics for the specific users/context exposed to the new flag variation and evaluates them over a defined window (minutes to days).

3. Automatically pause or roll back on regression:
   If latency or error rate crosses your threshold, LaunchDarkly triggers an automatic pause or rollback. You can also trigger alerts (Slack, PagerDuty) and use exposure insights to see exactly who was impacted—all without touching the codebase.

1. Define a guarded rollout

You start in LaunchDarkly by creating or editing a feature flag:

• Set your progressive rollout steps (e.g., 5% for 15 minutes, then 25% for 30 minutes, then 50%, then 100%).
• Target by segments or attributes (e.g., only free users, only EU, only Chrome, internal users first).
• Turn on Automated Rollout & Monitoring for that flag.

Then you define your guardrails:

• Error thresholds (e.g., “If the error rate for this flag’s exposed users exceeds 2% over 10 minutes, pause the rollout.”)
• Latency thresholds (e.g., “If p95 latency increases by more than 50ms compared to baseline over 5 minutes, roll back.”)
• Optionally add other metrics (e.g., timeouts, 5xx counts).

2. Connect metrics and track rollout health

Guarded Releases work by mapping real‑time events and metrics to the feature rollout:

• Use LaunchDarkly’s SDKs and APIs to send custom events, errors, and timings.
• Or connect via Sentry or OpenTelemetry to reuse metrics you already collect.
• Map each metric to the flag or experiment you’re rolling out.

LaunchDarkly then:

• Monitors those metrics during each rollout step.
• Evaluates them over a configurable monitoring window (from minutes to days).
• Computes whether your defined thresholds are being violated.

Because all of this is running at runtime against the same context IDs your flags use, you get precise attribution: metrics are tied to the users actually seeing the new variation, not just global averages.

3. Automatic pause or rollback, plus notifications

When LaunchDarkly detects a regression:

• It can Automatically Pause or Rollback the rollout based on your configuration.
• It sends Regression Detection alerts via Slack or PagerDuty so the right people know what just happened.
• It records Exposure Insights, showing which users or segments were exposed during the failed window.

This gives you a safety net that reacts in milliseconds, backed by LaunchDarkly’s production infrastructure (99.99% uptime, 45T+ flag evaluations per day, <200ms flag changes worldwide).

Features & Benefits Breakdown

Core Feature	What It Does	Primary Benefit
Automated Rollout & Monitoring	Progressively rolls out a flag while tracking real‑time metrics.	Safely increase exposure without manually babysitting.
Performance Thresholds	Lets you define latency and error limits plus monitoring windows.	Catch regressions early, before they become incidents.
Automatic Pause or Rollback	Automatically stops or reverses a rollout when metrics regress.	Minimize blast radius, no redeploys or manual toggling.
Regression Detection Alerts	Sends PagerDuty/Slack alerts when thresholds are crossed.	Get the right humans involved only when needed.
Exposure Insights	Shows which users/contexts saw the failing variation.	Faster debugging and clear blast‑radius analysis.
Advanced Observability	Connects metrics via SDKs, Sentry, or OpenTelemetry for full visibility.	One control surface for release, monitoring, and response.

Ideal Use Cases

• Best for high‑risk backend rollouts:
  Because it keeps a tight leash on error rates and latency spikes when you change APIs, services, or data paths—and auto‑rolls back if things degrade.

• Best for customer‑facing UI or AI behavior changes:
  Because it lets you progressively roll out UX changes, experiments, or AI Config updates and halt them the instant user‑visible performance or error rates regress.

Limitations & Considerations

• You need meaningful signals to monitor:
  Automated rollback is only as good as the metrics you send. Invest in good error and latency instrumentation first, then wire those metrics into LaunchDarkly.

• Guardrails don’t replace incident management:
  Automatic pause/rollback dramatically reduces impact, but you still need root‑cause analysis, post‑incident review, and longer‑term fixes in your normal workflow.

Pricing & Plans

LaunchDarkly’s runtime control platform—including feature flags, Guarded Releases, and observability integrations—is available across multiple plans. Pricing depends on factors like monthly active users, environments, and advanced governance needs.

• Team / Growth Plans: Best for product and engineering teams that need safe progressive rollouts, basic guardrails, and standard observability integrations.
• Enterprise Plans: Best for large organizations that need global scale, fine‑grained policies and approvals, advanced observability, and central governance across multiple teams and applications.

For exact details and to see how Guarded Releases fit your stack and volume, talk with our team.

Frequently Asked Questions

How do we actually connect our monitoring alerts to LaunchDarkly’s auto‑pause or rollback?

Short Answer: You don’t forward “alerts” directly; you stream metrics/events into LaunchDarkly (via SDKs, Sentry, or OpenTelemetry), define thresholds on those metrics, and LaunchDarkly acts as the guardrail that pauses or rolls back when those thresholds are violated.

Details:
Traditional monitoring tools fire alerts to humans. Guarded Releases flips this: metrics become direct inputs into the rollout decision logic. You:

1. Instrument your app to emit:
   • Error events (e.g., 5xx, exceptions).
   • Latency measurements (e.g., p95 response time).
2. Connect those via:
   • LaunchDarkly events SDKs and APIs, or
   • Integrations like Sentry or OpenTelemetry.
3. Configure a guarded rollout where those metrics are attached to the flag and thresholds are defined.

From there, LaunchDarkly continually reads those metrics for exposed traffic and automatically pauses or rolls back—while your monitoring system can still send alerts to humans for deeper investigation.

Can we tune the sensitivity, so we don’t get constant pauses from noise?

Short Answer: Yes. You control thresholds, monitoring windows, and which metrics matter so you can balance sensitivity with stability.

Details:
You can avoid noisy auto‑pauses by:

• Setting relative thresholds (e.g., “20% worse than baseline”) instead of overly tight absolutes.
• Using sensible windows (e.g., 5–15 minutes) to smooth out transient spikes.
• Watching multiple metrics together (e.g., error rate + latency) instead of a single noisy signal.
• Limiting guardrails to critical releases or specific environments (e.g., production only).

This lets you tune Guarded Releases to your reality: responsive enough to catch real issues, but not so sensitive that normal traffic variation constantly pauses rollouts.

Summary

Automatically pausing a rollout when latency or error rate regresses is about connecting your runtime metrics directly to your release levers. With LaunchDarkly Guarded Releases, you:

• Define progressive rollouts and guardrails once.
• Monitor latency and errors in real time for exposed users.
• Let the system automatically pause or roll back when things go sideways—without redeploying, and with clear visibility into who was impacted.

Rollouts stay fast and boring. Incidents get smaller. And your on‑call finally gets some sleep.

Next Step

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