Docker is not automatically slow. The performance problem usually comes from the workload around it: a database, a frontend watcher, background indexes, bind mounts, and several containers all competing for CPU, memory, and disk access.
On macOS and Windows, Docker Desktop runs Linux containers inside a virtualized environment. Sharing a large project directory between the host and that environment can make file-heavy workloads noticeably slower. Node and Python dependency trees are common offenders because they contain thousands of small files. Databases can also suffer when their data directory is placed on a slow shared mount.
Start by checking actual usage instead of guessing. Inspect container CPU and memory, remove stopped containers and unused images, and limit services that do not need to run continuously. Keep dependency directories inside the container or a named volume where appropriate. Avoid mounting the entire home directory when the application needs only one project folder.
Compose profiles are another simple improvement. Put optional services such as mail testing, analytics, or background workers behind a profile and start only what the current task needs. Finally, give Docker Desktop a sensible memory limit and leave enough RAM for the operating system and editor.
The goal is not to abandon containers. It is to make the development environment match the job. A small, observable stack is usually faster and easier to debug than a copy of production running on every laptop.