Shadow traffic overload

To validate a candidate model, a team turned on shadow traffic: production requests are mirrored to a new model whose responses are discarded. Shortly after, the LIVE production model's p99 latency rises 60% and a few requests start timing out, even though real user QPS hasn't changed. Dashboards: the shadow model runs on the SAME GPU node pool and shares the same feature-store and inference-batching infrastructure as the live model; GPU utilization and feature-store QPS roughly doubled when shadowing turned on; the shadow path has no rate limit and mirrors 100% of traffic. Tracing shows live requests now queue longer in the shared batcher. How do you triage and mitigate?