Start here. This is the direct spoken answer to practice first.
Why this question matters
A post-release resource spike can come from changed traffic, hot code paths, retry amplification, dependency slowdown, retained memory, or uneven instance health. The deployment timeline and per-instance evidence help separate correlation from cause before scaling hides the symptom.
I would first identify whether the spike is CPU, memory, requests, dependency latency, or a combination. I would compare the time of the spike with the deployment, traffic volume, error rate, and App Service instance metrics. If users are affected, I would reduce impact with rollback, feature disablement, scale-out, or throttling depending on the symptom. Then I would use Application Insights, logs, and App Service diagnostics to find the route, dependency, or code path that changed.