For AI agents: a documentation index is available at the root level at /llms.txt and /llms-full.txt. Append /llms.txt to any URL for a page-level index, or .md for the markdown version of any page.
Sign inTry it free
DocsGuidesSDKsIntegrationsAPI docsTutorialsFlagship blog
DocsGuidesSDKsIntegrationsAPI docsTutorialsFlagship blog
  • Get started
    • Overview
    • Onboarding
    • Get started
    • Launch Insights
    • LaunchDarkly architecture
    • LaunchDarkly vocabulary
  • AgentControl
    • AgentControl
    • Manage AgentControl
  • Feature flags
    • Create flags
    • Target with flags
    • Flag templates
    • Manage flags
    • Code references
    • Contexts
    • Segments
  • Releases
    • Releasing features with LaunchDarkly
    • Release policies
    • Percentage rollouts
    • Progressive rollouts
    • Guarded rollouts
    • Feature monitoring
    • Release pipelines
    • Engineering insights
    • Release management tools
    • Applications and app versions
    • Change history
    • Restoring previous flag versions
  • Observability
    • Observability
    • Session replay
    • Error monitoring
    • Logs
    • Traces
    • Observability metrics
    • Product analytics events
    • LLM observability
    • Alerts
    • Dashboards
    • Service map
    • Vega for auto-remediation
    • Observability MCP server
    • Search specification
    • Observability settings
    • Observability integrations
  • Experimentation
    • Experimentation
    • Experiment metric types
    • Experiment configuration
    • Managing experiments
      • Experiment health checks
      • Starting and stopping experiment iterations
      • Editing and archiving experiments
      • Experiment sample size and run time
    • Analyzing experiments
    • Multi-armed bandits
    • Holdouts
  • Metrics and events
    • Metrics in LaunchDarkly
    • Creating metrics
    • Metric groups
    • Events
    • Autogenerated metrics
  • Warehouse native
    • Warehouse native metrics
    • Setting up external warehouses
    • Creating experiments using warehouse native metrics
  • Infrastructure
    • Connect apps and services to LaunchDarkly
    • LaunchDarkly in China and Pakistan
    • LaunchDarkly in the European Union (EU)
    • LaunchDarkly in federal environments
    • Public IP list
  • Your account
    • Projects
    • Views
    • Environments
    • Tags
    • Teams
    • Members
    • Roles
    • Account security
    • Feature previews
    • Billing and usage
    • Changelog
Sign inTry it free
LogoLogo
On this page
  • Overview
  • Start experiment iterations
  • Stop experiment iterations
ExperimentationManaging experiments

Starting and stopping experiment iterations

Was this page helpful?
Previous

Editing and archiving experiments

Next
Built with

Overview

This topic explains how to start recording an experiment iteration and stop the experiment iteration when you’re finished. To learn more about running experiments, read Managing experiments.

Start experiment iterations

After you create an experiment and toggle on the flag, you can start an experiment iteration in one or more environments.

To start an experiment iteration:

  1. Navigate to the Experiments list.
  2. Click on the environment section containing the experiment you want to start.
    • If the environment you need isn’t visible, click the + next to the list of environment sections. Search for the environment you want, and select it from the list.

The environment selection menu.

The environment selection menu.
  1. Click on the name of the experiment you want to start an iteration for. The Design tab appears.
  2. Click Start, or if your environment requires approvals, click Request approval to start.
  3. Repeat steps 1-4 for each environment you want to start an iteration in.

An experiment with the "Start" button called out.

An experiment with the "Start" button called out.

Experiment iterations allow you to record experiments in individual blocks of time. To ensure accurate experiment results, when you make changes that impact an experiment, LaunchDarkly starts a new iteration of the experiment.

When you start an iteration of an experiment, LaunchDarkly sends the maintainer and anyone following the experiment an email, an in-app notification, and, if you have the Slack app integration configured, a Slack notification.

You can also use the REST API: Create iteration

Stop experiment iterations

If you need to make changes to your experiment, you can stop your experiment iteration.

Restarting an experiment reshuffles traffic

If you stop an experiment iteration, and then later start a new iteration, LaunchDarkly reshuffles traffic into new variations. To learn more, read Variation reassignment.

To stop an experiment iteration:

  1. Navigate to the experiment’s Results tab.
  2. Click Stop. The “Ship” menu appears.

An experiment with the "Stop" button called out.

An experiment with the "Stop" button called out.
  1. Select the winning variation to ship to all contexts that match the flag’s targeting rule. A “Stop experiment” dialog appears.
  2. Enter a Reason for stopping.
  3. Click Stop experiment, or if your environment requires approvals, click Request approval to stop.

After you stop an experiment iteration, the variation you chose in step 4 is served to all targets that match the experiment audience targeting rule. The flag’s Targeting tab updates to reflect this.

You can stop an experiment iteration at any time. Just like starting an iteration, stopping an iteration only impacts the experiment in one environment. If you wish to stop collecting data from every instance of an experiment, you must stop each experiment in each environment individually.

When you stop recording an experiment, LaunchDarkly ends the iteration and stops collecting data about user behavior for that experiment. The data collected for that iteration is available on the experiment’s Results tab.

When you stop an iteration of an experiment, LaunchDarkly sends the maintainer and anyone following the experiment an email, an in-app notification, and, if you have the Slack app integration configured, a Slack notification. To update your notification preferences, click the bell icon at the bottom of the left sidenav and select Manage notification settings.

Stopping an experiment does not delete the experiment. Stopping an experiment lets you retain the results and data the experiment has already collected.

You can also use the REST API: Patch experiment