AI Dictionary › AI Fundamentals
Failover is the automatic switch to a backup system when the primary one stops working. It is the mechanism that turns a potentially catastrophic outage into a brief transition invisible to users. Together with redundancy, it is the pillar of high availability: spare components are of little use if switching to them requires manual intervention.
The primary and the standby monitor each other through periodic signals called heartbeats. When monitoring detects that the primary has been silent beyond a threshold, it promotes the standby to new primary, redirects traffic and alerts operators. Typical setups are active-passive, where the backup waits on standby, and active-active, where both nodes work and either absorbs the other's load on failure.
In AI platforms failover applies at several layers: model replicas across cloud regions, redundant vector databases, and LLM gateways that reroute calls to an alternative provider when the main one returns errors. Beyond AI it protects banking databases, phone exchanges, air traffic control systems and any service where minutes of downtime are expensive.
The term combines fail and over: literally, moving past the failure. The practice is rooted in fault-tolerant systems engineering: a historical reference is Tandem Computers, which from 1976 built fully redundant NonStop systems for banks and stock exchanges, where every component had a twin ready to take over.
Grace's professional scenarios include business continuity cases where you have to instruct an AI to handle emergency plans: knowing what failover means makes your prompts technically credible. The 7 evaluation dimensions also measure how thoroughly you anticipate edge cases.
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