Regional Failover Calculator
Test whether surviving regions absorb the load when one fails, and price the standby capacity that makes it possible.
Inputs
Survivor Utilisation After Losing a Region
112.5%
Capacity Margin After the Failure
-10.00thousand req/s
Capacity Verdict
Losing one region overloads the rest — add capacity or another region
Composite Availability
99.9850%
Expected Annual Downtime
78.8minutes
Standby Capacity Cost
$40,000.00
Data at Risk Within the RPO
3,600.0MB
Step by step
Values used
Active regions = 3 regions; Normal total load = 90 thousand req/s; Capacity per region = 40 thousand req/s; Standby posture = Warm standby (scaled-down copy); Monthly cost of a full region = 40,000 USD; Single-region availability = 99.90 %; Failures that hit every region at once = 15 %; Recovery point objective = 5 minutes; Average write throughput = 12 MB/s
Regional Failover
survivor utilisation = load ÷ (regions − 1) ÷ capacity per region; unavailability = correlated share × single-region unavailability + (independent share × single-region unavailability)^regions; data at risk = write throughput × RPO.
Survivor Utilisation After Losing a Region
= 112.5
Capacity Margin After the Failure
= -10.00 thousand req/s
Capacity Verdict
= Losing one region overloads the rest — add capacity or another region
Composite Availability
= 99.9850
Expected Annual Downtime
= 78.8 minutes
Standby Capacity Cost
= 40,000.00
How it works
Capacity is judged on the failure case, not the normal case: with three regions the survivors each pick up half the lost traffic, so anything above 66% normal utilisation breaks under a single loss. Availability is modelled as a correlated floor plus an independent term, because a shared control plane or a bad config rollout hits every region at once — and once correlated failures dominate, adding a fourth region buys almost nothing. Multi-region designs fail for three reasons: the survivors were sized for steady state, the replication lag was larger than the stated RPO, or the outage was correlated and no amount of regions helped. All three are visible here before the incident rather than during it. Region costs vary widely, so confirm the standby figure against your provider's pricing.
Formula
Regional Failover
survivor utilisation = load ÷ (regions − 1) ÷ capacity per region; unavailability = correlated share × single-region unavailability + (independent share × single-region unavailability)^regions; data at risk = write throughput × RPO.
- survivors
- Regions still serving after one is lost
- correlated share
- Failures that take out every region together, which extra regions cannot fix
- data at risk
- Bytes written but not yet replicated when the region fails
Frequently Asked Questions
How is Regional Failover calculated?
survivor utilisation = load ÷ (regions − 1) ÷ capacity per region; unavailability = correlated share × single-region unavailability + (independent share × single-region unavailability)^regions; data at risk = write throughput × RPO. Capacity is judged on the failure case, not the normal case: with three regions the survivors each pick up half the lost traffic, so anything above 66% normal utilisation breaks under a single loss. Availability is modelled as a correlated floor plus an independent term, because a shared control plane or a bad config rollout hits every region at once — and once correlated failures dominate, adding a fourth region buys almost nothing.
Why does Regional Failover matter?
Multi-region designs fail for three reasons: the survivors were sized for steady state, the replication lag was larger than the stated RPO, or the outage was correlated and no amount of regions helped. All three are visible here before the incident rather than during it. Region costs vary widely, so confirm the standby figure against your provider's pricing.
What values do I need to enter?
This calculator takes 9 inputs: Active regions, Normal total load, Capacity per region, Standby posture, Monthly cost of a full region, Single-region availability, Failures that hit every region at once, Recovery point objective, Average write throughput. The pre-filled defaults are a realistic starting point — replace them with figures from your own environment for a result you can act on.
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