A recent study evaluated the performance overhead of gVisor and Kata Containers compared to standard Docker (runc) for AI agent tasks. The tests were conducted on a laptop with an Intel Core i5-8250U processor and 11 GiB of RAM, using NixOS with Linux kernel 6.18.
Docker with runc launched containers in 0.22 seconds, consuming 11.5 MiB of memory. In contrast, gVisor started in 0.33 seconds and used 48.6 MiB. Kata Containers showed significantly higher overhead, with startup times of 6–7 seconds and memory usage between 199 MiB and 302 MiB, depending on the hypervisor.
The study tested various scenarios, including container startup, idle memory consumption, command execution, Lua compilation, and file system operations. While gVisor performed comparably to runc in file-intensive tasks, Kata Containers proved inefficient for scenarios involving frequent, short-lived container launches due to its high startup latency.
The researcher noted that absolute results are specific to the test hardware and default configurations. While gVisor and Kata offer enhanced isolation compared to standard Docker, they impose measurable costs in time and memory that must be considered when deploying AI agents that frequently spawn new execution environments.
