A rollback decision guide for incident leads
If you’re the incident lead, your job is to protect users and keep options open. Rollback decisions get messy when teams confuse certainty with action. You don’t need perfect diagnosis to make a safe move.
This guide is a short path to decide when to roll back without turning the incident into a guessing game.
Start with the timeline
If the incident started right after a deploy, a rollback is usually your safest first move. The key is to confirm correlation quickly.
Ask:
- Did symptoms start within minutes of the deploy?
- Are error spikes or latency increases aligned with the deploy time?
- Did the blast radius expand right after the change?
If two of these are true, treat the deploy as suspect.
Timebox the debate. If the team can’t agree within 5 minutes, roll back and keep diagnosing.
Check for irreversible changes
A rollback is safe when it doesn’t break data or contracts. Before you roll back, scan for these risk flags:
- Non-backward-compatible schema changes
- Feature flags that were fully removed (not just disabled)
- One-way migrations that rewrite data
If any of those are true, pause and choose a safer mitigation first.
Decision axis: deploy correlation vs data risk
Use this quick mental model to keep rollback decisions crisp:
Data risk ↑
High | Pause. Use a safe mitigation first. | Roll back only after validation.
Low | Gather one more signal. | Roll back now.
Low correlation ---------> High correlation
Prefer the simplest safe move
The order of safe moves is usually:
- Disable a feature flag
- Roll back the last deploy
- Scale down a misbehaving worker or consumer
If you have a flag that isolates the change, use it. If not, roll back.
Define the verification window
Before you push the button, define how you’ll decide if the rollback worked.
- Which dashboard is the source of truth?
- What metric should change?
- How long will you wait before calling it?
Do this out loud so the team doesn’t drift into endless debate.
Communicate clearly
Tell stakeholders what you’re doing and why:
- “We’re rolling back the latest deploy because symptoms started immediately after it.”
- “We expect to see error rates drop within 5 minutes.”
- “If not, we’ll move to the next mitigation.”
Clear comms prevent panic and help the team stay aligned.
After the rollback, capture the evidence
If the rollback helps, record the timeline:
- deploy time
- incident start time
- rollback time
- recovery time
This becomes a training example and saves you in the postmortem.
If rollback doesn’t help
A rollback that doesn’t improve metrics is also a signal. It usually means:
- the change was earlier than you think
- the incident is unrelated to deploys
- a dependency incident is in play
Use that evidence to move down the checklist, not to question the decision.
The short version
- If the incident starts right after a deploy, roll back unless data safety says no.
- Decide the verification window before acting.
- Use the rollback outcome as a diagnostic signal.
Rollback isn’t failure. It’s a protective move that buys clarity when time is expensive.
How we run this at DawnOps
We keep rollback decisions fast and auditable:
- deploy timeline and feature-flag changes are visible during the incident
- the team agrees on a single verification window before acting
- we capture the timeline as part of the incident record