After scaling an App Service API from one instance to four, users see inconsistent behavior. What would you investigate?

What breaks when an App Service API scales out with in-memory state?

Explains scale-out bugs from in-memory sessions, local caches, background queues, uploaded files, and per-instance behavior.

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I would suspect hidden in-memory state first. After scale-out, different requests can land on different instances, so sessions, static dictionaries, local files, generated reports, in-process queues, and per-instance caches can produce inconsistent behavior. Sticky sessions may hide the symptom for a while, but restarts, deployments, and uneven load still expose the design problem.

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Scale-out diagnosis, local state, distributed cache, session design, file storage, per-instance telemetry, and stateless API principles.

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Scale-out incidents often reveal state that was accidentally local. This drill matters because the production behavior is shaped by platform configuration, code boundaries, and the operational path the team will use during a real incident.
I would suspect hidden in-memory state first. After scale-out, different requests can land on different instances, so sessions, static dictionaries, local files, generated reports, in-process queues, and per-instance caches can produce inconsistent behavior. Sticky sessions may hide the symptom for a while, but restarts, deployments, and uneven load still expose the design problem.
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I would prove the issue with per-instance telemetry: instance id in logs, affected routes, cache hits, session behavior, file paths, and whether the bug disappears when traffic sticks to one worker. Then I would move the state to the right shared place: database for durable workflow state, blob storage for files, distributed cache for cacheable shared data, and queue-backed workers for background work. I would keep local memory only for disposable cache that can be rebuilt safely.

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Turning on sticky sessions and calling the design fixed.
Using local disk for generated files that users download later.
Assuming static dictionaries are shared across instances.
No per-instance logs or metrics to see where failures occur.

After scaling an App Service API from one instance to four, users see inconsistent behavior. What would you investigate?

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