Table of Contents
Shadow deployment for AI sends production traffic to a new candidate model in parallel with the production model, without exposing the candidate's output to users. You get real-traffic eval signal before any user-facing risk. The right validation step before canary rollout.
Production model serves user; shadow model gets the same prompt, response logged but not returned. Compare metrics across both: latency, cost, quality (LLM-as-judge or eval harness). Shadow for 7-14 days; promote to canary if metrics hold. Cost: ~2× inference for shadow window.
How shadow works
- Production model serves user normally; response returned
- Same prompt async-fired to shadow model; response logged but not returned
- Shadow response paired with production response in logs
- Compare metrics across the pair: latency, cost, quality
- After 7-14 days of shadow data: promote to canary or reject
What to compare
- Latency: shadow p99 TTFT vs production
- Cost: shadow tokens consumed vs production
- Quality: LLM-as-judge comparing the two responses on the same prompt
- Failure rate: shadow errors / OOMs / refusals vs production
- Output distribution: response length, structure, language match
Promotion triggers
Promote shadow to canary if:
- Quality (judged) ≥ production on aggregate + per important segment
- Latency within acceptable envelope
- Cost no worse than 1.2× production (or improved)
- Failure rate ≤ production
- No surprising output-distribution shifts
If any signal fails: investigate before promotion; don't paper over.
Verdict
Shadow deployment is the right validation step before user-facing canary for AI changes. Real-traffic eval signal at zero user risk. Cost (~2× inference for shadow window) is modest insurance against regressions. Always shadow before canary for any non-trivial AI change.
Bottom line
Shadow before canary; eval on real traffic. See canary pattern.
