This usually points to resource contention or degraded inference conditions rather than a modeling issue.

During peak hours, models often compete for CPU, GPU, memory, or I/O bandwidth. This can lead to timeouts, truncated inputs, or fallback logic silently kicking in, all of which reduce observed performance. Check system-level metrics alongside model metrics. Look for increased latency, dropped requests, or reduced batch sizes under load. If you use autoscaling, verify that new instances warm up fully before serving traffic.

Common mistakes:

1. Treating performance drops as data drift without checking infrastructure

2. Not load-testing with realistic concurrency

3. Ignoring cold-start behavior in autoscaled environments

Model quality can’t be evaluated independently of the system serving it.