[{"data":1,"prerenderedAt":802},["ShallowReactive",2],{"/en-us/blog/google-cloud-integrations-for-secure-cloud-run-deployments-at-gitlab":3,"navigation-en-us":43,"banner-en-us":442,"footer-en-us":452,"blog-post-authors-en-us-Regnard Raquedan|Matt Genelin":694,"blog-related-posts-en-us-google-cloud-integrations-for-secure-cloud-run-deployments-at-gitlab":720,"assessment-promotions-en-us":753,"next-steps-en-us":792},{"id":4,"title":5,"authorSlugs":6,"body":9,"categorySlug":10,"config":11,"content":15,"description":9,"extension":30,"isFeatured":13,"meta":31,"navigation":32,"path":33,"publishedDate":22,"seo":34,"stem":38,"tagSlugs":39,"__hash__":42},"blogPosts/en-us/blog/google-cloud-integrations-for-secure-cloud-run-deployments-at-gitlab.yml","Google Cloud Integrations For Secure Cloud Run Deployments At Gitlab",[7,8],"regnard-raquedan","matt-genelin",null,"product",{"slug":12,"featured":13,"template":14},"google-cloud-integrations-for-secure-cloud-run-deployments-at-gitlab",false,"BlogPost",{"title":16,"description":17,"authors":18,"heroImage":21,"date":22,"body":23,"category":10,"tags":24},"Google Cloud integrations for secure Cloud Run deployments at GitLab","This tutorial demonstrates how to use GitLab’s Google Artifact Management integration to deploy to Google Cloud Run, a serverless runtime for containers application.\n",[19,20],"Regnard Raquedan","Matt Genelin","https://res.cloudinary.com/about-gitlab-com/image/upload/v1750099336/Blog/Hero%20Images/Blog/Hero%20Images/blog-image-template-1800x945_fJKX41PJHKCfSOWw4xQxm_1750099336757.png","2025-01-15","*This tutorial is from a recent Arctiq, GitLab, and Google in-person workshop. The goal was to explore common security challenges faced by organizations as they journey to the cloud.*\n\nThis tutorial will help you learn about the [Google Cloud integrations in GitLab](https://cloud.google.com/docs/gitlab). These features are meant to help accelerate and improve security of deployments to Google Cloud.\n\n![Google integrations list](https://res.cloudinary.com/about-gitlab-com/image/upload/v1750099345/Blog/Content%20Images/Blog/Content%20Images/image2_aHR0cHM6_1750099345112.png)\n\n## Prerequisites\n\n1. [Google Cloud project](https://cloud.google.com/resource-manager/docs/creating-managing-projects)  \n2. Appropriate [IAM permissions](https://cloud.google.com/iam/docs/) for security, Artifact Registry, and Cloud Run usage. For this tutorial, ensure you have the \"Owner\" role with the aforementioned project.\n\n## Setting up Workload Identity Federation\n\nIn this step, we configure GitLab to connect Google Cloud's Workload Identity Federation to reduce the need for service accounts and let the two platforms use short-lived credentials on-demand.\n\n1. On the left sidebar, select **Search** or go to and find your group or project. If you configure this in a group, settings apply to all projects within by default.  \n2. Select **Settings \\> Integrations**.  \n3. Select **Google Cloud IAM**.  \n4. Input the Project ID and Project number in the respective fields. This information can be obtained from the Google Cloud console [Welcome](https://console.cloud.google.com/welcome) page of your project.  \n5. Input the desired Pool ID and Provider ID in the respective fields. These are values that you provide and must be unique from other Pool and Provider IDs.  \n6. Copy the generated command and then go to the **Google Cloud console**.  \n7. Run **Cloud Shell** and execute the generated command from the Workload Identity Federation integration page.  \n8. Once successful, the **Google Cloud IAM** integration will be designated as active in the Integrations list at the GitLab project.\n\n## Artifact Registry configuration\n\nAs an alternative to GitLab's own place to host artifacts, deploying to Google Cloud's Artifact Registry is another way to leverage their infrastructure. This section will provide steps on how to use GitLab's native integration with Artifact Registry. Note that Workload Identity Federation must already be configured prior to this.\n\n1. At the **Google Cloud** console, go to **Artifact Registry** via search or the main navigation.  \n2. Create a new repository by clicking the **\"+\"** icon. At the creation page, provide a name and keep the **Docker** format and **Standard** mode selected. Select **Region** and choose **us-central1**. Leave the rest at the default settings and click **Create**.  \n3. Once the repository is created and confirmed, go back to your GitLab project.  \n4. In your GitLab project, on the left sidebar, select **Settings > Integrations**. Then select **Google Artifact Registry**.  \n5. Under Enable integration, select the **Active** checkbox, then complete the fields:  \n   * Google Cloud project ID: The ID of the Google Cloud project where your Artifact Registry repository is located.  \n   * Repository name: The name of your Artifact Registry repository.  \n   * Repository location: The location of your Artifact Registry repository. (`us-central1` is assumed.)  \n6. In **Configure Google Cloud IAM policies**, follow the onscreen instructions to set up the IAM policies in Google Cloud. These policies are required to use the Artifact Registry repository in your GitLab project. Select **Save** changes.  \n7. To view your Google Cloud artifacts, on the left sidebar, select **Deploy > Google Artifact Registry**.\n\n## Cloud Run configuration\n\n1. Enable the Cloud Run API, if not done already. Go to **APIs & Services > Enabled APIs & Services**. From there, click **Enable APIs & Services** at the top and search for **Cloud Run Admin API**. Select the search result and enable the API.  \n2. Configure the IAM policies in Google Cloud to grant permissions to allow the Cloud Run CI/CD component to deploy to Cloud Run.\n\n```shell\nGCP_PROJECT_ID=\"\u003CPROJECT ID>\"\nGCP_PROJECT_NUMBER=\"\u003CPROJECT NUMBER>\"\nGCP_WORKLOAD_IDENTITY_POOL=\"\u003CPOOL ID>\"\n\ngcloud projects add-iam-policy-binding ${GCP_PROJECT_ID} \\\n  --member=\"principalSet://iam.googleapis.com/projects/${GCP_PROJECT_NUMBER}/locations/global/workloadIdentityPools/${GCP_WORKLOAD_IDENTITY_POOL}/attribute.developer_access/true\" \\\n  --role='roles/run.admin'\n\ngcloud projects add-iam-policy-binding ${GCP_PROJECT_ID} \\\n  --member=\"principalSet://iam.googleapis.com/projects/${GCP_PROJECT_NUMBER}/locations/global/workloadIdentityPools/${GCP_WORKLOAD_IDENTITY_POOL}/attribute.developer_access/true\" \\\n  --role='roles/iam.serviceAccountUser'\n\ngcloud projects add-iam-policy-binding ${GCP_PROJECT_ID} \\\n  --member=\"principalSet://iam.googleapis.com/projects/${GCP_PROJECT_NUMBER}/locations/global/workloadIdentityPools/${GCP_WORKLOAD_IDENTITY_POOL}/attribute.developer_access/true\" \\\n  --role='roles/cloudbuild.builds.editor'\n\n```\n\n## Deploy to Cloud Run\n\nIn this section, you will use Gitlab's CI/CD components to deploy to Cloud Run, Google Cloud's serverless runtime for containers.\n\n1. Go to the GitLab project and from the list of files in the source code, find `.gitlab-ci.yaml`. Click the **file name** and the single file editor will show up. Click the **Edit** button and select the **Open in Web IDE** option.  \n2. In Web IDE, copy-paste the following code:\n\n```yaml\nstages:\n    - build\n    - upload\n    - deploy\n\n```\n\nThis code snippet sets up three stages in the pipeline: build, upload, and deploy.\n\n1. The next step is to create two CI/CD variables in the same YAML file:\n\n```yaml\nvariables:\n    GITLAB_IMAGE: $CI_REGISTRY_IMAGE/main:$CI_COMMIT_SHORT_SHA\n    AR_IMAGE: $GOOGLE_ARTIFACT_REGISTRY_REPOSITORY_LOCATION-docker.pkg.dev/$GOOGLE_ARTIFACT_REGISTRY_PROJECT_ID/$GOOGLE_ARTIFACT_REGISTRY_REPOSITORY_NAME/main:$CI_COMMIT_SHORT_SHA\n\n```\n\nThe first variable, `GITLAB\\_IMAGE`, denotes the container image that the pipeline creates by default. The second one, `AR\\_IMAGE`, denotes the location at Google Cloud's Artifact Registry where the container image will be pushed to.\n\n2. Next, define the code that will build the container image:\n\n```yaml\nbuild:\n    image: docker:24.0.5\n    stage: build\n    services:\n        - docker:24.0.5-dind\n    before_script:\n        - docker login -u $CI_REGISTRY_USER -p $CI_REGISTRY_PASSWORD $CI_REGISTRY\n    script:\n        - docker build -t $GITLAB_IMAGE .\n        - docker push $GITLAB_IMAGE\n\n```\n\nThis code uses [pre-defined CI/CD variables](https://docs.gitlab.com/ee/ci/variables/predefined_variables.html) for the Docker commands.\n\n3. The final step is using two CI/CD components to deploy to Google Cloud. The first component integrates with Artifact Registry and the second is the deployment to Cloud Run:\n\n```yaml\ninclude:\n    - component: gitlab.com/google-gitlab-components/artifact-registry/upload-artifact-registry@main\n      inputs:\n        stage: upload\n        source: $GITLAB_IMAGE\n        target: $AR_IMAGE\n\n    - component: gitlab.com/google-gitlab-components/cloud-run/deploy-cloud-run@main\n      inputs:\n        stage: deploy\n        project_id: \"\u003CPROJECT_ID>\"\n        service: \"tanuki-racing\"\n        region: \"\u003CREGION>\"\n        image: $AR_IMAGE\n\n```\n\nReplace \u003CPROJECT_ID> with your Google Cloud Project ID. Replace with the [Google Cloud region](https://cloud.google.com/compute/docs/regions-zones) most appropriate to your location. `us-central1` is assumed.\n\nCommit the changes and push to the main branch. For reference, the final `.gitlab-ci.yaml` should look like this, noting to replace the \u003CPROJECT ID> and \u003CREGION> with the appropriate values:\n\n```yaml\nstages:\n    - build\n    - upload\n    - deploy\nvariables:\n    GITLAB_IMAGE: $CI_REGISTRY_IMAGE/main:$CI_COMMIT_SHORT_SHA\n    AR_IMAGE: $GOOGLE_ARTIFACT_REGISTRY_REPOSITORY_LOCATION-docker.pkg.dev/$GOOGLE_ARTIFACT_REGISTRY_PROJECT_ID/$GOOGLE_ARTIFACT_REGISTRY_REPOSITORY_NAME/main:$CI_COMMIT_SHORT_SHA\n\nbuild:\n    image: docker:24.0.5\n    stage: build\n    services:\n        - docker:24.0.5-dind\n    before_script:\n        - docker login -u $CI_REGISTRY_USER -p $CI_REGISTRY_PASSWORD $CI_REGISTRY\n    script:\n        - docker build -t $GITLAB_IMAGE .\n        - docker push $GITLAB_IMAGE\n\ninclude:\n    - component: gitlab.com/google-gitlab-components/artifact-registry/upload-artifact-registry@main\n      inputs:\n        stage: upload\n        source: $GITLAB_IMAGE\n        target: $AR_IMAGE\n\n    - component: gitlab.com/google-gitlab-components/cloud-run/deploy-cloud-run@main\n      inputs:\n        stage: deploy\n        project_id: \"\u003CPROJECT_ID>\"\n        service: \"tanuki-racing\"\n        region: \"\u003CREGION>\"\n        image: $AR_IMAGE\n\n```\n\n1. Go back to the main GitLab project and view the pipeline that was just initiated. Take note of the stages that should be the same stages that were defined in Step 2.  \n2. Once the pipeline is complete, go to the Google Cloud console and then **Cloud Run** via search or navigation. A new Cloud Run service called `tanuki-racing` should be created.  \n3. Click the **service name** and then go to the **Security** tab. Ensure that the service is set to **Allow unauthenticated invocations**. This will make the deployed app publicly available. The app URL posted on screen is now available and should open a new browser tab when clicked.\n\nBy utilizing GitLab’s CI/CD pipelines to build and push a containerized application to Google Artifact Registry, you can see the power of GitLab’s AI-powered DevSecOps Platform as a means to building secure applications. GitLab also deployed the containerized application to Google’s Cloud Run as a low-cost running application on the public internet. Using GitLab to instrument building an application, pushing a container and triggering a cloud run deployment allows DevOps engineers to have the assurance that secure applications are being run on the public-facing internet.\n\n> [Sign up for a free trial of GitLab Ultimate](https://about.gitlab.com/free-trial/devsecops/) to begin working with these integrations. Also, check out our [solutions architecture area](https://about.gitlab.com/blog/tags/solutions-architecture/) for more Gitlab and Google Cloud tutorials.\n",[25,26,27,28,29],"integrations","google","GKE","tutorial","solutions 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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.",[726],"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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