Begin with the course requirements
List the operating system, editor, language runtime, libraries and services needed for the exercise. Note whether learners need a graphical desktop, administrative access or persistent storage. These requirements should guide the environment choice.
Also identify what must be separated between participants. The appropriate setup depends on the workload and security requirements, not simply the number of learners.
Understand the difference
A virtual machine includes its own guest operating system. It can suit exercises that depend on a full desktop, a particular operating system or system-level configuration.
A container packages an application and its dependencies while sharing the host kernel. It can suit repeatable coding and service-based exercises, but it does not replace every VM use case.
Compare the complete setup
Look beyond the initial compute price. Consider preparation, support, storage, licences, access duration and resource shutdown. For AI courses, confirm separately whether the exercises need GPUs, external model APIs or both.
Pilot the actual exercise before rolling out the environment to the cohort. Check how learners save work, recover from mistakes and return after a break. The useful choice is the one that supports the course consistently.
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