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99.99% uptime downtime & error budget

99.99% uptime allows about 4m 19s of downtime per 30-day month, roughly 52m 34s a year. Here is the full ladder, the error budget in requests, and how fast you can burn it.

allowed downtime / month4m 19s30-day window
error budget500 reqsof 5M / month
failure allowance0.01%of all requests

allowed_downtime

Allowed downtime at 99.99%, by window

Allowed downtime at 99.99% across time windows
windowallowed downtime
per day8.6s
per week1m
per 30 days4m 19s
per 90 days12m 58s
per 365 days52m 34s

burn_rate

How fast the monthly budget burns

At 1x you spend the whole 30-day budget exactly over 30 days. 14.4x is Google's fast-burn alert threshold, the rate that drains the month in about two days.

Time to exhaust the 30-day budget at each burn rate
burn ratebudget gone in
1x30d
2x15d
5x6d
10x3d
14.4x (fast-burn page)2d 2h

what_it_takes

What it takes to hold 99.99% (four nines)

Less monthly downtime than many teams take to notice an alert and open the right dashboard. You cannot hold it with a human on the recovery path. It needs multi-zone (often multi-region) redundancy and automated failover that is genuinely tested, which is a different order of investment from three nines.

Need more? See 99.999% uptime and what the next nine costs. Lighter need? 99.95% uptime is cheaper to hold.

faq

Questions & answers

How much downtime does 99.99% uptime allow?
99.99% uptime allows about 4m 19s of downtime per 30-day month, which works out to roughly 52m 34s a year. Go past that in a window and you have missed the target for that window.
What is the error budget for 99.99% uptime?
Over 5M requests in a month, 99.99% permits about 500 failed requests before the budget is spent. The budget is 0.01% of whatever volume you serve, so it scales with traffic.
Is 99.99% uptime good enough?
It depends on what your users need and what the next tier costs to hold. Less monthly downtime than many teams take to notice an alert and open the right dashboard. The breakdown below shows what it takes to hold 99.99% and whether the next nine is worth it.

Picking a target is easy. Holding it in production is the work.

I review where a system actually spends its error budget and what the next nine really costs. Book a call, or leave your email and I'll reach out.

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Prefer proof first? See how this plays out in real case studies →