Shadow Deployment

Shadow deployment runs a new model alongside the live one, feeding it the exact same production traffic — but its responses are logged for analysis instead of being shown to users. The current model still answers every real request, so users are never exposed to the candidate, yet you get to observe how it would have behaved on genuine, unfiltered inputs. This is the safest way to de-risk a model change. You can measure the new version's latency, error rate, infrastructure cost, and output differences against the incumbent before a single customer sees it. It is distinct from a canary release, where the new model actually serves a small slice of users. For AI builders, shadowing a candidate LLM or prompt over a day of real traffic surfaces the weird edge cases synthetic tests miss. Practical note: log both outputs side by side, diff them automatically, and only graduate to a canary once the shadow numbers look boring.