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Enter an availability target and see the downtime it allows per day, week, month, quarter, and year. Then enter the downtime you have had so far to see how much error budget is left and how long it will last.
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Share on XShare on LinkedInAn availability target is easier to reason about as time. 99.9% over 30 days is about 43 minutes of downtime; 99.99% is a little over 4 minutes, which is less than it takes most teams to notice an alert and open a laptop. That difference is why each extra nine costs far more than the last: at four nines, recovery has to be automatic, because no human response fits inside the budget.
The error budget is the same number seen from the other side: the unreliability you are allowed to spend. Burn rate says how fast it is going. A burn rate of 1 uses the budget exactly over the window; 14.4 sustained for an hour uses 2% of a 30-day budget and is the usual threshold for paging someone. The calculator also shows what a chain of dependencies does to a target: three services at 99.9% in the same request path can offer about 99.7% between them, which is why a service cannot promise more than the things it depends on.
Turn an availability target into the downtime it allows, then see how much of that error budget you have already spent and how fast you are spending it.
For request-based SLOs: how many may fail.
9,999 requests may fail
A request that needs all of them succeeds only if every one does.
99.700% is the best the whole path can offer
The multi-window alerts recommended in the Google SRE Workbook for a 30-day window. Alert when the error rate over the window stays above the threshold.
| Burn rate | Over | Budget spent | Error rate threshold | Response |
|---|---|---|---|---|
| 14.4x | 1 hour | 2% | 1.440% | Page |
| 6x | 6 hours | 5% | 0.600% | Page |
| 1x | 3 days | 10% | 0.100% | Ticket |