Shadow Traffic: Testing AI Changes Without User Risk

Shadow traffic is the highest-confidence way to test an AI change: run the new version in parallel with the old on real production traffic, compare outputs, and never expose users to the new version until you are confident.

Why it matters

The cost is double inference for shadowed traffic. The benefit is catching regressions on real production distributions before any user sees them.

How Bhogar AI approaches it

Bhogar AI ships shadow traffic at the gateway and workflow level. Outputs are compared, diffs surfaced in a UI, and the shadow can be promoted to canary and then full deploy.

• Gateway and workflow-level shadow traffic
• Output diff UI
• Sample-rate control
• Promotion path: shadow → canary → full
• Cost dashboard for shadow traffic

What you get

High-stakes AI changes ship with confidence backed by side-by-side production data instead of offline samples.