AI reliability is keeping an AI system available and correct under real-world conditions, provider outages, traffic spikes, malformed inputs, by building in fallback paths and graceful degradation rather than assuming the happy path always holds.
An AI feature that only works when the model provider's API is fast, available, and returns a perfectly formatted response isn't reliable, it's lucky. Reliability means planning for the provider being slow, down, or wrong, and having a defined fallback for each case instead of a crash.
AI providers do have outages and rate limits, and a single point of failure on one model provider can take down an entire product feature if there's no fallback in place.
Common patterns include retries with backoff for transient failures, timeouts so a slow request doesn't hang the whole pipeline, circuit breakers that stop sending traffic to a failing provider until it recovers, and fallback to a secondary model or provider when the primary is unavailable. Reliability targets are usually expressed as SLOs, defined thresholds for availability and latency that the system is built to meet.
A well-designed fallback path (a smaller, always-available model as backup, or a cached response for common queries) turns a provider outage from a full feature outage into a temporary quality dip, which is usually the difference that matters to a user.