Managing Experiment Lifecycle

Managing experiment lifecycle

This skill covers experiment state transitions — what each action does, when to use it, and how it affects variant assignment and analysis.

State diagram

draft ──launch──▶ running ──end──▶ stopped ──archive──▶ archived
                  │ │   ▲              │
                  │ pause resume  ship_variant
                  │ │   │         (also ends if running)
                  │ ▼   │
                  │ paused (flag inactive, still "running" status)
                  │
                  ├─freeze_exposure──▶ exposure_frozen ──unfreeze_exposure──▶ running
                  │                    (enrollment closed, metrics keep flowing)

Any non-draft state ──reset──▶ draft

Actions and their implications

For each action, the two key questions:

1. Who sees what variant? (user perspective)
2. Who is in my analysis? (statistical perspective)

Launch (experiment-launch)

Transitions draft → running. Activates the feature flag and sets start_date.

• Preconditions: must be in draft, flag needs 2-20 multivariate variants (no specific key required; the baseline defaults to "control" when present, else the first variant)
• Pre-launch checklist: has at least one metric? Variants correct? Flag implemented in code?
• Variants: users start being bucketed into variants based on the configured split
• Analysis: data collection begins from start_date

No request body needed.

One optional item worth a single mention at launch, when the change is user-facing and substantial: a short survey, shown when users finish the experimented flow (e.g. triggered by the form's submit event), collects qualitative feedback (a rating, an optional comment) alongside the metrics, from day one. Offer it once as setup advice, drop it if declined, and never let it delay the launch. Do not raise it at end or ship-variant time — there it reads as a gate on rolling out. → See references/qualitative-feedback.md in [[diagnosing-experiment-health]]

Pause (experiment-pause)

Deactivates the feature flag. Users fall back to the default experience (typically control).

• Preconditions: must be running and not already paused
• Variants: flag is not returned by /decide — no new exposure events recorded
• Analysis: no new data while paused, but existing data is preserved. Experiment stays "running".

No request body. Use experiment-resume to reactivate.

Resume (experiment-resume)

Reactivates the feature flag after a pause. Users are re-bucketed deterministically into the same variants.

• Preconditions: must be paused
• Variants: same assignment as before pause — deterministic bucketing
• Analysis: exposure tracking resumes

No request body.

Freeze exposure (experiment-freeze-exposure)

Stops enrolling new users while everything else keeps going: already-enrolled users keep their variant, metrics keep flowing, and end_date stays null. Snapshots the already-exposed users into a static cohort and narrows every release condition on the feature flag to that cohort. Status becomes exposure_frozen.

Use for long-horizon metrics (revenue, LTV, retention, renewals) when the sample is big enough and you want to stop adding users without stopping measurement. Neither end nor pause fits that job: end stops measurement at end_date, and pause deactivates the flag for everyone.

• Preconditions: must be running (not draft, stopped, paused, or already frozen), flag linked and not deleted, at least one release condition
• Variants: enrolled users keep their variant (deterministic bucketing); new users no longer match the flag
• Analysis: exposures stop growing (a flat exposure curve is expected), but metric data keeps accumulating for enrolled users

Timing: the exposure scan and cohort snapshot run synchronously inside the API call, and duration scales with the number of exposed persons — an experiment with tens of thousands of exposed users can take on the order of tens of seconds. Set expectations with the user, wait for the response, and don't treat a slow call as a failure or retry it.

Not applicable (400) for:

• Group-aggregated experiments — the flag targets groups, not persons, and a person cohort can't freeze group-based matching
• Experiments in a holdout — holdout assignment is evaluated before release conditions, so new users would keep entering the holdout
• Flags with early access conditions — also evaluated before release conditions, so freezing can't stop new enrollment
• Mostly-anonymous exposure (e.g. experiments on logged-out surfaces) — anonymous "personless" users can never match a person cohort and would silently lose their variant, so freezes with more than a small unresolved share are rejected
• Very large exposed sets — the exposure scan is bounded by a person cap and a timeout; over either bound the API returns a clean 400 rather than freezing

When a freeze is rejected, explain which limitation applies rather than retrying — these are structural, not transient.

Interactions with other actions: ship-variant and reset strip the freeze (both also delete the snapshot cohort); end does NOT touch the flag, so ending a frozen experiment leaves the flag narrowed to the snapshot cohort. SDKs using local evaluation can't resolve static cohorts, so a frozen flag evaluates via the /decide endpoint (standard static-cohort behavior). Exposures ingested in the final moments before freezing may miss the snapshot (ingestion lag).

No request body. Use experiment-unfreeze-exposure to reopen enrollment.

Unfreeze exposure (experiment-unfreeze-exposure)

Reopens enrollment on an exposure-frozen experiment. Removes the snapshot-cohort condition and freeze markers from every release group, restoring the flag's original targeting, and deletes the snapshot cohort. Status returns to running.

• Preconditions: exposure must be frozen (and the experiment not ended)
• Variants: enrolled users keep their variant; new users can enroll again under the original release conditions
• Analysis: exposures resume growing

Can introduce bias: reopening enrollment re-exposes the flag to a potentially new population. Users who enrolled before the freeze and those who enroll after the unfreeze joined at different times, and possibly under different conditions — mixing the two cohorts in one analysis can bias the results. Warn the user before unfreezing, especially after a long freeze or if the audience or product changed in between. If they only wanted to sanity-check the frozen results, they may not need to unfreeze at all.

No request body.

End (experiment-end)

Sets end_date and transitions to stopped. The feature flag is NOT modified.

• Preconditions: must be running (launched, not already stopped)
• Variants: users continue seeing assigned variants (flag stays active)
• Analysis: results frozen to data up to end_date

Optional body: conclusion ("won", "lost", "inconclusive", "stopped_early", "invalid") and conclusion_comment.

Use this when you want to freeze results without changing what users see. If the experiment's exposure was frozen, ending does not strip the freeze — the flag stays narrowed to the snapshot cohort (unfreeze first, or ship a variant, if that's not desired).

Ship variant (experiment-ship-variant)

Rewrites the feature flag so the selected variant is served to 100% of users.

• Preconditions: must be launched (running or stopped). Cannot ship from draft.
• Variants: ALL users see the shipped variant. The flag is rewritten with a catch-all group.
• Analysis: if still running, the experiment is also ended (end_date set)

Always confirm with the user before shipping — this permanently rewrites the feature flag.

Required: variant_key (e.g. "test"). Optional: conclusion, conclusion_comment.

Returns 409 if an approval policy requires review before the flag change.

Flag cleanup PR (option on end and ship variant)

Both experiment-end and experiment-ship-variant accept open_cleanup_pr: true. A background PostHog Code task then removes the experiment's feature flag code and opens a draft pull request in the team's connected GitHub repository.

• Only set this when the user asks for it or confirms it.
• The key must carry the task:write scope, or the whole request is rejected with a 403 and the experiment is not ended or shipped.
• The cleanup runs only when the call actually ends the experiment and a conclusion is set — shipping an already-stopped experiment, or ending without a conclusion, skips it. It also requires the team to have the flag cleanup feature enabled; silently skipped when it isn't.
• repository ("organization/repository") picks the target when several repositories are connected. Omit it to fall back to the experiment's saved repository, the team default, or the only connected repository. With several candidates and no default, the cleanup is skipped unless provided.
• Track progress with experiment-cleanup-task — the PR URL appears there once opened; a cleanup typically takes several minutes.

Archive (experiment-archive)

Hides a stopped experiment from the default list view.

• Preconditions: must be stopped (end_date set)
• Variants: no change — flag is unaffected
• Analysis: no change — results remain accessible

No request body. Can be restored by setting archived=false via experiment-update.

Reset (experiment-reset)

Returns an experiment to draft state. Clears start_date, end_date, conclusion, and archived.

• Preconditions: must not already be in draft
• Variants: flag is left unchanged — users continue seeing assigned variants
• Analysis: previously collected data still exists but won't be included in results unless start_date is adjusted after re-launch

No request body.

Duplicate (experiment-duplicate)

Creates a copy as a new draft with fresh dates and no results.

Important: always provide a unique feature_flag_key different from the original. If the same key is used, both experiments share a flag — changes to one affect both.

Optional: custom name (defaults to "Original Name (Copy)").