Catch a faulty release before it becomes an incident
ewake watches every release for days, catching regressions while they are still small, before they turn into customer-facing outages.
ewakeApp9:50 AM
Watching a release — acme/backend · commit 7f2c9a41 · 3 artifacts · until 10:20 AM
No problem found · watching 9 signals
ewakeApp6:41 AM
Watching release #4821, day 3 of 5.
Caught before it paged · release watch
Error rate drifting on checkout-api since deploy #4821
checkout-api has run at 0.41% to 1.94% since 03:10, about four times the baseline, and every failure is on the code path #4821 changed. No monitor has fired and nobody has been paged. At this slope it reaches the 2% threshold tomorrow afternoon, during peak.DEPLOY · #4821 · WATCHED · day 3 of 5
TREND · 0.41% → 1.94% · THRESHOLD · 2%
Release watch6:41 AMdeploy #4821
Message #dev-release
A regression rarely announces itself. It ships on a Tuesday, moves error rates by a fraction of a percent, and stays under every threshold you have set until the traffic pattern changes on Friday and it becomes an outage. By then the deploy that caused it is three days back and nobody is looking there.
ewake closes that window. Every release stays watched for days after it ships, compared against how the service behaved before it, so a drift that no threshold would catch is still caught while it is small enough to be a rollback rather than an incident.
This is the cheapest part of the on-call shift, because the work it saves is the work that never has to happen.
What ewake does after every deploy
Watched past the first hour
Most monitoring stops paying attention once a deploy looks clean. ewake keeps watching the services it touched for days, not minutes.
Drift, read against its cause
A slow rise in error rate three days after a release is read as that release drifting, not as an unexplained anomaly with no history attached.
Nothing pages until the signal is real
A watched deploy doesn't mean a noisier on-call. It means the eventual page, if it comes, already has three days of context behind it.
The map updates on its own
What a release actually touched in production gets folded back into the live map automatically, so the next incident starts from better information.
How it watches a release
- 01
It learns what normal looks like for that service
Not one global threshold across everything, but how this service behaves at this hour on this day, which is the only baseline a small drift shows up against.
- 02
It keeps watching after the deploy goes green
The window is days, not minutes. Most regressions worth catching do not appear during the deploy, they appear the first time real traffic hits the changed path.
- 03
It ties the change back to the release
When something moves, ewake already knows which deploy landed in that window and what it touched, so the finding arrives attached to a commit rather than to a graph.
- 04
It tells you while rolling back is still cheap
A finding lands in your channel with the release, the affected service and what changed, at the point where undoing it is a decision rather than an incident.
Where it stops
It does not roll back on its own
Reverting a release is a judgment call about your customers. ewake brings you the evidence for making it.
It stays quiet when nothing moved
The point of watching every release is to say nothing about almost all of them. An agent that reports on every deploy is another thing to ignore.
It needs no new instrumentation
It reads the observability data you already collect. No SDK, no code changes, no second agent on your hosts.
Point ewake at your own production
Thirty minutes to connect it, one perimeter, your real alerts. You will know inside a week whether it holds, on your stack, not in a sandbox.