99% uptime downtime & error budget
99% uptime allows about 7h 12m of downtime per 30-day month, roughly 3d 16h a year. Here is the full ladder, the error budget in requests, and how fast you can burn it.
allowed_downtime
Allowed downtime at 99%, by window
| window | allowed downtime |
|---|---|
| per day | 14m 24s |
| per week | 1h 41m |
| per 30 days | 7h 12m |
| per 90 days | 21h 36m |
| per 365 days | 3d 16h |
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.
| burn rate | budget gone in |
|---|---|
| 1x | 30d |
| 2x | 15d |
| 5x | 6d |
| 10x | 3d |
| 14.4x (fast-burn page) | 2d 2h |
what_it_takes
What it takes to hold 99% (two nines)
This is where unmonitored side projects and internal tools sit by default. It needs no redundancy, just a server that mostly stays up. For anything customer-facing it is usually too loose, because a single bad deploy or one long restart can spend the whole month in an afternoon.
Need more? See 99.5% uptime and what the next nine costs.
every_tier
The full uptime ladder
New to the terms? What availability and the nines mean, plus error budgets and burn rate.
faq
Questions & answers
- How much downtime does 99% uptime allow?
- 99% uptime allows about 7h 12m of downtime per 30-day month, which works out to roughly 3d 16h a year. Go past that in a window and you have missed the target for that window.
- What is the error budget for 99% uptime?
- Over 5M requests in a month, 99% permits about 50K failed requests before the budget is spent. The budget is 1% of whatever volume you serve, so it scales with traffic.
- Is 99% uptime good enough?
- It depends on what your users need and what the next tier costs to hold. This is where unmonitored side projects and internal tools sit by default. The breakdown below shows what it takes to hold 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.
Prefer proof first? See how this plays out in real case studies →