Resource Class for Remote_Docker and Machine executor

Currently for building Docker images there are no options to increase resource_class as the machine executor and remote docker environment share the same specs at 2 cores and 8Gb of ram.

This is a request to extend the resource class feature to all executors. (MacOS has a current post already for this issue).

  • Guest
  • Nov 28 2018
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  • Adam Dry commented
    November 18, 2019 17:35

    Thanks Alexey, I've migrated from docker inside docker (where remote-docker had to be used) to a machine executor with the large resource class.


    I can confirm my spin up is slightly faster (for anyone interested we're spinning up 13 containers using docker-compose as an env to test against, I wrote a blog post about it here). Went from 7m 30s to 5m.


    I could do with more CPU though, so if the xlarge resource class became available on the machine executor I would definitely give it a go. If there's a big enough speed increase we'd pay for it.

  • Admin
    Alexey Klochay commented
    November 18, 2019 14:01

    A quick update here: a large resource class is now available for the Machine executor for customers on our Free and Performance plans.  Link to the relevant docs section.

  • Guest commented
    November 12, 2019 08:16


  • Adam Dry commented
    November 12, 2019 07:07



    Our test scenario requires quite a number of docker containers, it takes ~8 mins just to spin up the environment as it's CPU constrained.


    8GB mem is just about ok for us.

  • Mathias Dahl-Olsen commented
    October 18, 2019 07:06

    1+ for this

  • Hadle Selsaas commented
    October 17, 2019 13:48


  • Kjartan Elias commented
    October 17, 2019 13:48

    +1 from me as well

  • Mikal Sande commented
    October 17, 2019 13:46

    +1 For configurable resource_class for remote_docker and machine executors. Our CI jobs that require more resources also require running multiple containers.

  • Tal Tchernihovski commented
    September 20, 2019 08:58


  • Guest commented
    September 20, 2019 07:41

    It would be really useful for us to have machine executor resource_class medium+. We want to have middle option between medium and large. Actually in this case we need more RAM and not additional cpu cores. It would be nice to have compute intensive resource_class and memory intensive resource_classes.

  • Thibault Derousseaux commented
    June 24, 2019 16:48

    My company is on a CircleCI performance plan and we're stuck with the small specs of the machine executor. It's a real blocker for us. We'll have to move to another CI provider next month if the resource_class feature available for docker containers is not made available to machine executors too.

    Can you share your timeline for this feature?


  • Brett Logan commented
    June 10, 2019 20:37

    This is a full blocker for bringing our large open source project to CircleCI. We have a mandate to run tests on VM's and not in containers. We were in the process of beginning migration to CircleCI, but this will prevent us from doing so. Is there any hope for this in the near future? We are about to choose our next-gen platform for CI and will be vendor locked in once we do. As it stands now, this can't be CircleCI, despite my overwhelming want to make the recommendation to our Technical Steering Commitee

  • Admin
    Sara Read commented
    June 06, 2019 20:34

    Following up on the question about documentation, here are the links to current resources sizes:



  • Bob Ziuchkovski commented
    May 28, 2019 18:33

    Another +1 for this.

  • Brian O'Halloran commented
    May 06, 2019 20:19

    +1 yes please, we are spinning up several containers during our circleci integration test job

  • Guest commented
    April 30, 2019 17:34

    George what do you mean by  resource size ?
    where is the documentation for that?

  • George Reyes commented
    April 30, 2019 17:28

    We understand the value of giving our customers more control over the resources their remote_docker and machine jobs are using. Right now only one resource size for both remote_docker and machine is available, but we will consider expanding our offering here in the future.

  • Guest commented
    April 25, 2019 22:08


  • Lev Bornovalov commented
    April 17, 2019 05:52

    +1 for this