Where a container's overhead actually comes from, and what's worth optimizing.
Containers have near-native CPU and memory performance compared to a VM, since there's no hypervisor or guest OS in the way — but that doesn't mean zero overhead anywhere. Networking (depending on the driver used) and disk I/O — especially through certain storage drivers, and notably on Docker Desktop's virtualized filesystem on macOS and Windows — can introduce real, measurable latency that's worth profiling directly rather than assuming away.
Image size mostly affects deployment and cold-start speed rather than steady-state runtime performance: a smaller image pulls and starts faster, which matters a great deal for autoscaling and CI turnaround, even though it won't change how fast the application itself runs once it's up. Setting explicit CPU and memory limits is a separate concern from image optimization entirely — it's what prevents one noisy container from starving others sharing the same host.
What you'll walk away knowing
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