[{"data":1,"prerenderedAt":787},["ShallowReactive",2],{"/en-us/blog/new-machine-types-for-gitlab-saas-runners":3,"navigation-en-us":39,"banner-en-us":439,"footer-en-us":449,"blog-post-authors-en-us-Darren Eastman":691,"blog-related-posts-en-us-new-machine-types-for-gitlab-saas-runners":705,"assessment-promotions-en-us":738,"next-steps-en-us":777},{"id":4,"title":5,"authorSlugs":6,"body":8,"categorySlug":9,"config":10,"content":14,"description":8,"extension":26,"isFeatured":12,"meta":27,"navigation":28,"path":29,"publishedDate":20,"seo":30,"stem":34,"tagSlugs":35,"__hash__":38},"blogPosts/en-us/blog/new-machine-types-for-gitlab-saas-runners.yml","New Machine Types For Gitlab Saas Runners",[7],"darren-eastman",null,"product",{"slug":11,"featured":12,"template":13},"new-machine-types-for-gitlab-saas-runners",false,"BlogPost",{"title":15,"description":16,"authors":17,"heroImage":19,"date":20,"body":21,"category":9,"tags":22},"GitLab introduces new machine types for GitLab SaaS Linux Runners","GitLab SaaS now offers larger machine types for running CI jobs on Linux.",[18],"Darren Eastman","https://res.cloudinary.com/about-gitlab-com/image/upload/v1749672836/Blog/Hero%20Images/multiple-machine-types-cover.png","2022-09-22","Our GitLab SaaS vision is to provide a solution where you can easily choose and use the correct type of public cloud-hosted compute resources for your CI/CD jobs. In this first iteration towards achieving that vision, we are pleased to announce that two larger compute machines are generally available for GitLab SaaS Runners on Linux.\n\nWith these two machine types, you can now access more choices for your GitLab SaaS CI/CD jobs. And with 100% job isolation on an ephemeral virtual machine, and security and autoscaling fully managed by GitLab, you can confidently run your critical [CI/CD](/topics/ci-cd/) jobs on GitLab SaaS.\n\n## New machine type details\n\nThe new [SaaS Runners on Linux](https://docs.gitlab.com/ee/ci/runners/saas/linux_saas_runner.html) are a 2 vCPU, 8GB RAM (`saas-linux-medium-amd64`), and a 4 vCPU, 16GB RAM (`saas-linux-large-amd64`) machine type. These machine types, powered by the latest generation of Google Compute N2D virtual machines, deliver significant performance improvements for general-purpose CI workloads. The medium machine type, `saas-linux-medium-amd64`,  is available to all subscriptions (Free, Premium, Ultimate). The large machine type, `saas-linux-large-amd64` is only available to paid plans (Premium and Ultimate) and GitLab for Open Source program members.\n\nNote: If you are in a Free plan and tag a CI job with the large machine type, `saas-linux-large-amd64`, you will get an error at the job level and the job will not run.\n\n```text\nThis job is stuck because of one of the following problems. There are no active runners online, no runners for the protected branch, or no runners that match all of the job's tags: saas-linux-large-amd64\n```\n\n## Are the new machine types right for my CI job?\n\nThe answer is that it depends. If the CI job is compute-intensive, you will likely see a performance improvement measured by reduced build times. We ran a series of  [Linux kernel](https://gitlab.com/gitlab-org/ci-cd/gitlab-runner-stress/linux-kernel) builds on the medium machine type to test the potential performance gains for compute-intensive CI jobs.\n\n![Linux kernel build CI job execution time benchmark](https://about.gitlab.com/images/blogimages/new-machine-types-gitlab-saas-linux/linux-kernel-build-runner-saas-benchmark_2022-09-22.png)\n\nOur testing found an average 41% reduction in CI job execution time for the medium machine types compared to the baseline small machine type. We recommend you experiment with the new machine types for your CI jobs to determine the right choice based on your build workflows.\n\n## Getting started\n\nTo get started with the new machine types, simply add a tag to your CI file. Without the tag, a job in your pipeline will automatically run on the small machine type.\n\n### Example pipeline configuration\n\nIn this example pipeline configuration, `job_001` will run on the default Linux SaaS Runner as no machine type tag is defined. The subsequent job, `job_002`, in the build stage will run on the medium machine type, and `job_003` will run on the large machine type. So you have flexibility within a GitLab CI/CD pipeline to choose the right machine type for each job.\n\n```text\nstages:\n  - Prebuild\n  - Build\n  - Unit Test\n\njob_001:\n stage: Prebuild\n script:\n  - echo \"this job runs on the default (small) machine type\"\n\njob_002:\n tags: [ saas-linux-medium-amd64 ]\n stage: Build\n script:\n  - echo \"this job runs on the medium machine type\"\n\njob_003:\n tags: [ saas-linux-large-amd64 ]\n stage: Unit Test\n script:\n  - echo \"this job runs on the large machine type\"\n\n```\n\n## Understanding the new machine types and cost factors\n\nYou can start using the new machine types now with the CI minutes currently available in your plan. The new machine types will consume your CI minutes at a different rate than the default (small) machine type based on an applied cost factor. If you are a GitLab for Open Source program member, then refer to the [cost factor documentation page](https://docs.gitlab.com/ee/ci/pipelines/cicd_minutes.html#cost-factor) for details on how cost factors are applied to your CI/CD jobs.\n\n|  | saas-linux-small-amd64 | saas-linux-medium-amd64 | saas-linux-large-amd64 |\n| --- | --- | --- | --- |\n| CI minutes consumed per 1 minute of build time | 1 | 2 | 3 |\n\nToday your CI minutes usage report on GitLab SaaS will be an aggregate of all of the CI minutes consumed across all the machine types you select in your jobs. In this [issue](https://gitlab.com/gitlab-org/gitlab/-/issues/356076), we are working towards adding visibility into usage by each Runner type. So you will soon have more granular reporting of use across the various Runner classes (Linux, Windows, macOS) and machine types we plan to offer.\n\n## Feedback\n\nAt GitLab, we value your input and use it as a critical sensing mechanism in planning roadmap investments. To provide feedback on the machine types you need on GitLab SaaS Runners on Linux, add a comment to the respective comment thread in this [issue](https://gitlab.com/gitlab-org/gitlab/-/issues/373196)\n\nCover image by [Julian Hochgesang](https://unsplash.com/@julianhochgesang) on 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GitLab metrics and registry features help reduce CI/CD bottlenecks","See how CI/CD Job Performance Metrics and Container Virtual Registry, currently in beta, help platform teams quickly spot slow jobs and simplify multi-registry container pulls.",[711],"Talia Armato-Helle","https://res.cloudinary.com/about-gitlab-com/image/upload/v1771438388/t6sts5qw4z8561gtlxiq.png","Platform and DevOps engineers spend too much time piecing together visibility across fragmented tools and managing infrastructure that should just work.\n\nTwo new GitLab features currently in beta tackle this from different angles but share the same goal: giving practitioners direct control over the CI/CD infrastructure they depend on, without adding another third-party tool. One surfaces job-level performance data right where you monitor pipelines. The other simplifies how you pull container images from multiple registries with built-in caching.\n\nBoth features are open for feedback now. Your input will help shape what ships next.\n\n## CI/CD Job Performance Metrics\n\n* **Available tiers:** GitLab Premium, GitLab Ultimate\n* **Status:** Limited-availability beta on GitLab.com; available on GitLab Self-Managed and GitLab Dedicated when ClickHouse is configured\n\nToday, there’s no simple way to see when a particular job’s duration starts increasing or which jobs are quietly dragging down your pipeline runtimes. Most teams either build custom dashboards or manually dig through logs to answer basic questions like:\n\n* Which jobs are slowest?  \n* Where are failure rates climbing?  \n* Which stage is the real bottleneck?\n\nCI/CD Job Performance Metrics changes that by adding a new job-focused panel to the CI/CD analytics page at the project level.\n\nFor each job in your pipelines, you can see:\n\n* Typical (P50, median) and worst‑case (P95) job duration, so you can quickly view normal versus slowest runs  \n* Failure rate, so you can spot fragile or flaky jobs  \n* Job name and stage, covering the last 30 days by default\n\nThe table is sortable, searchable by job name, and paginated, so platform teams get a single view to answer questions that previously required separate tools or custom reporting.\n\n**Try it now**\n\n* Navigate to your project and select **Analyze \\> CI/CD analytics**.  \n* Look for the CI/CD job performance metrics panel and sort by duration or failure rate to find your slowest or least reliable jobs.\n\n**Documentation**\n\n* [CI/CD analytics – CI/CD job performance metrics](https://docs.gitlab.com/user/analytics/ci_cd_analytics/#cicd-job-performance-metrics)\n\n**What’s coming next**\n\nWe’re working on stage-level grouping, so you can view aggregated metrics across your build, test, and deploy stages, and quickly understand where to focus optimization work.\n\n**Share your feedback:**\n\n* [CI/CD job performance metrics epic](https://gitlab.com/groups/gitlab-org/-/work_items/18548)\n\n## Container Virtual Registry\n\n**Tier:** GitLab Premium, GitLab Ultimate\n**Status:** Beta, API-ready in 18.9\n\nMost organizations pulling container images into CI/CD pipelines rely on multiple registries: Docker Hub, Harbor, Quay, and internal registries, to name a few. Managing authentication, availability, and caching across all of them is operational overhead that slows pipelines down and introduces fragility.\n\nThe Container Virtual Registry lets you create a single GitLab endpoint that pulls from multiple upstream container sources with built-in caching.\n\nInstead of configuring credentials and availability for each registry individually in your pipeline configuration, you can:\n\n* Point your pipelines at one GitLab virtual registry endpoint  \n* Configure multiple upstream registries (Docker Hub, Harbor, Quay, and others using long-lived token authentication)  \n* Let GitLab resolve image pulls automatically, with pull-through caching to reduce bandwidth costs and improve reliability\n\nFor teams evaluating GitLab as a container registry replacement, this closes a critical capability gap. For teams already managing multi-registry container workflows, it centralizes image management into GitLab and cuts down on repeated pulls.\n\n**What the beta supports today**\n\n* Upstream registries using long-lived token authentication: Docker Hub, Harbor, Quay, and other compatible registries  \n* Pull-through caching so commonly used images are served from GitLab after the first pull  \n* API-first configuration, with UI management in progress++\n\nCloud provider registries requiring IAM authentication (such as Amazon Elastic Container Registry, Google Artifact Registry, and Azure Container Registry) are being considered for future iterations.\n\n**Test it today**\n\n* The Container Virtual Registry is API-ready in 18.9.  \n* SaaS (GitLab.com): Request access through your CSM or by commenting on the feedback issue below to have the feature flag enabled for your group.  \n* Self-managed: Enable the feature flag and configure the virtual registry using the API.\n\n**Documentation**\n\n* [Container Virtual Registry API](https://docs.gitlab.com/api/container_virtual_registries/)  \n* [Pull container images from the virtual registry](https://docs.gitlab.com/user/packages/virtual_registry/container/#pull-container-images-from-the-virtual-registry)\n\n\n Watch this walkthrough of the Container Virtual Registry Beta:\n   \n\n  \u003Ciframe src=\"https://player.vimeo.com/video/1167512082?title=0&byline=0&portrait=0&badge=0&autopause=0&player_id=0&app_id=58479\" frameborder=\"0\" allow=\"autoplay; fullscreen; picture-in-picture; clipboard-write; encrypted-media; web-share\" referrerpolicy=\"strict-origin-when-cross-origin\" style=\"position:absolute;top:0;left:0;width:100%;height:100%;\" title=\"20260223_Container Virtual Registry Beta_V1\">\u003C/iframe>\u003C\u003Cscript src=\"https://player.vimeo.com/api/player.js\">\u003C/script>\n\n  \u003Cbr>\u003C/br>\n\n\n\n**Share your feedback:**\n\n* [Container virtual registry feedback issue](https://gitlab.com/gitlab-org/gitlab/-/issues/589630)\n\n## Help us build what matters\n\nEveryone in the GitLab community is a contributor. We built these betas based on community requests.\n\n* **CI/CD Job Performance Metrics** came from teams who had no easy way to see when build times started trending in the wrong direction, or which jobs were hurting pipeline reliability.  \n* **Container Virtual Registry** came from enterprise customers managing multiple registries and looking to reduce tool sprawl and bandwidth costs while evaluating GitLab as a central registry.\n\nYour feedback shapes what we create next. Try one or both of these betas, and share your experience in the linked feedback issues.\n\nThis is the first in a series of Core DevOps betas we plan to highlight. 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