99.95% uptime downtime & error budget
99.95% uptime allows about 21m 36s of downtime per 30-day month, roughly 4h 23m a year. Here is the full ladder, the error budget in requests, and how fast you can burn it.
allowed_downtime
Allowed downtime at 99.95%, by window
| window | allowed downtime |
|---|---|
| per day | 43s |
| per week | 5m 2s |
| per 30 days | 21m 36s |
| per 90 days | 1h 5m |
| per 365 days | 4h 23m |
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 99.95%
The step teams take when a 99.9% commitment is no longer enough for larger customers. It usually means multi-zone redundancy and removing the slowest human step from incident response, though not yet full multi-region. A sensible target for a B2B platform heading upmarket.
Need more? See 99.99% uptime and what the next nine costs. Lighter need? 99.9% uptime is cheaper to hold.
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 99.95% uptime allow?
- 99.95% uptime allows about 21m 36s of downtime per 30-day month, which works out to roughly 4h 23m a year. Go past that in a window and you have missed the target for that window.
- What is the error budget for 99.95% uptime?
- Over 5M requests in a month, 99.95% permits about 2.5K failed requests before the budget is spent. The budget is 0.05% of whatever volume you serve, so it scales with traffic.
- Is 99.95% uptime good enough?
- It depends on what your users need and what the next tier costs to hold. The step teams take when a 99.9% commitment is no longer enough for larger customers. The breakdown below shows what it takes to hold 99.95% 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 →