Even though a container starts "damn fast" in Docker, you're still adding significant latency to each call of your function if it requires a new container.

    $ time docker run alpine:3.6 sh -c exit
    docker run alpine:3.6 sh -c exit  0.02s user 0.02s system 3% cpu 1.191 total
    $ time sh -c exit
    sh -c exit  0.00s user 0.01s system 94% cpu 0.007 total

Then add to that the cost of loading your function's code into memory. For an interpreted language, that is probably going to take a while as the runtime does a bunch of system calls and parses text files. Even for a compiled, statically-linked language like Go, the executable (a random binary I picked was 50MB) needs to be copied into RAM.

For this reason, Lambda tries to keep your containers/function-wrapper-applications booted up in RAM: https://aws.amazon.com/blogs/compute/container-reuse-in-lamb...