How does a canary deployment work?
A canary deployment routes a small percentage of real traffic — say 10% — to a new model or prompt, while the rest stays on the proven version. You watch quality and cost metrics on the canary slice, and only increase its share if they hold. If something regresses, you roll back by routing traffic away from the canary, having exposed only a fraction of users to the problem.
The key is a defined progression with gates at each step. Prodinit used exactly this to replace a GPT-4.1 model with a cheaper distilled GPT-4o-mini on a high-volume voice platform: traffic moved through 10% → 25% → 50% → 75% → 90%, with hallucination detection and quality scoring at every stage. Each increase happened only after the gate passed — which is how the swap reached a 70% cost cut with no quality regression.
How does a shadow deployment differ?
A shadow deployment runs the new version on real traffic in parallel with the live one, but never shows its output to users. Both versions process the same requests; only the current production response is served. You log and compare the shadow's outputs offline.
The difference is risk exposure. A canary lets real users see the new version's output (just a small fraction of them). A shadow lets no users see it — making it the safer choice for changes you're less sure about, or for validating a new model on production-realistic traffic before any canary at all.
| Canary | Shadow | |
|---|---|---|
| Users see new output? | Yes, a small % | No |
| Risk to users | Low, bounded | None |
| Main use | Gradual safe rollout | Pre-rollout validation |
| Cost | Replaces some traffic | Doubles inference on shadowed traffic |
When should you use each?
Use a shadow deployment to validate a new model on real traffic with zero user risk — ideal for a first look at a major change. Use a canary to roll it out once you're confident, growing exposure behind quality gates. They're often sequential: shadow first to confirm parity, then canary to release. The trade-off with shadow is cost, since shadowed traffic runs inference twice.