[{"data":1,"prerenderedAt":817},["ShallowReactive",2],{"/en-us/blog/gitlab-eks-integration-how-to":3,"navigation-en-us":37,"banner-en-us":447,"footer-en-us":457,"blog-post-authors-en-us-Abubakar Siddiq Ango":699,"blog-related-posts-en-us-gitlab-eks-integration-how-to":713,"blog-promotions-en-us":754,"next-steps-en-us":807},{"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__":36},"blogPosts/en-us/blog/gitlab-eks-integration-how-to.yml","Gitlab Eks Integration How To",[7],"abubakar-siddiq-ango",null,"engineering",{"slug":11,"featured":12,"template":13},"gitlab-eks-integration-how-to",false,"BlogPost",{"title":15,"description":16,"authors":17,"heroImage":19,"date":20,"body":21,"category":9,"tags":22},"How to create a Kubernetes cluster on Amazon EKS in GitLab","A Kubernetes tutorial: Create clusters in a few clicks with GitLab and Amazon EKS.",[18],"Abubakar Siddiq Ango","https://res.cloudinary.com/about-gitlab-com/image/upload/v1749666959/Blog/Hero%20Images/gitlab-aws-cover.png","2020-03-09","Kubernetes has created a whole new world for running infrastructure at scale. With the right setup, an application can go from serving a few users to millions effortlessly. Setting up Kubernetes can be tasking and can require a lot of expertise to put all the pieces together. You’ll need to set up virtual or bare metal machines to use as nodes and manage SSL certificates, networking, load balancers, and many other moving parts.\n\nThe introduction of Amazon Elastic Kubernetes Service (EKS) was widely applauded as it streamlines the abstraction of the complexities in an environment most organizations are already familiar with and on a provider they already trust. Amazon EKS makes creating and managing Kubernetes clusters easier with more granular controls around security and straightforward policies of how resources are used.\n\nGitLab strives to increase developer productivity by automating repetitive tasks and allowing developers to focus on business logic. We recently introduced support for auto-creating Kubernetes clusters on Amazon EKS. In a few clicks with the right permissions, you’ll have a fully functional Kubernetes cluster on Amazon EKS. It doesn’t stop there however – GitLab also gives you the power to achieve the following use cases and more :\n\n* [Highly scalable CI/CD system using GitLab Runner](https://docs.gitlab.com/runner/): There are times like holidays when little to no changes to code are pushed to production, so why keep resources tied down? With the Amazon EKS integration with GitLab, you can install GitLab Runner with just a click and your CI/CD will run effortlessly without worrying about running out of resources.\n* Shared Cluster: Maintaining multiple Kubernetes clusters can be a pain and capital intensive. With Amazon EKS, GitLab allows you to setup a cluster at [Instance](https://docs.gitlab.com/ee/user/instance/clusters/index.html), [Group](https://docs.gitlab.com/ee/user/group/clusters/index.html) and [Project](https://docs.gitlab.com/ee/user/project/clusters/) levels. Kubernetes Namespaces are created for each GitLab project when the Amazon EKS is integrated at Instance and Project level, allowing isolation and ensuring security.\n* [Review Apps](https://docs.gitlab.com/ee/ci/review_apps/index.html): Reviewing changes to code or design can be tricky, you’ll need to check out your branch and run the code in a test environment. GitLab integrated with Amazon EKS deploys your app with new changes to a dynamic environment and all you need to do is click on a “View App“ button to review changes.\n* [AutoDevOps](https://docs.gitlab.com/ee/topics/autodevops/cloud_deployments/auto_devops_with_gke.html) takes DevOps to a whole new level. AutoDevOps detects, builds, tests, deploys, and monitors your applications, leveraging the Amazon EKS integration. All you have to do is push your code and the magic happens. In this tutorial, we will deploy a sample application to the Amazon EKS cluster we will be creating using AutoDevOps.\n\nTo show you how easy it is to create an Amazon EKS cluster from GitLab, the rest of this tutorial will walk you through the steps of the integration, starting with a one-time setup of necessary resources on AWS.\n\n## One-time setup on AWS to access resources\n\nFirst, we need to create a “provision\" role and a “service” role on AWS to grant GitLab access to your AWS resources and set up the necessary permissions to create and manage EKS clusters. You only need to perform these steps once and you can reuse them anytime you want to perform another integration or create more clusters.\n\n### Step 1 - Create Provision Role\n\nTo grant GitLab access to your AWS resources, a “provision role” is required. Let’s create one:\n\n1. Access GitLab Kubernetes Integration Page by clicking on the ”Kubernetes” menu for groups and Operations > Kubernetes menu for projects and click the “Add Kubernetes Cluster” button.\n2. Select “Amazon EKS” in the options provided under the “Create new cluster on EKS” tab.\n3. You are provided with an Account and External ID to use for authentication. Make note of these values to be used in a later step.\n\n ![Gitlab EKS Integration Page](https://about.gitlab.com/images/blogimages/gitlab-eks-integration/gitlab_eks_integration_page.png)\n\n4. Open IAM Management Console in another tab and click on “Create Role”\n5. Click on the “Another AWS account” tab and provide the Account and External ID obtained from GitLab and click Next to set permissions as shown below:\n\n ![AWS Provision Role](https://about.gitlab.com/images/blogimages/gitlab-eks-integration/provision_role.png)\n\n6. On the permissions page, click on “Create policy.” This will open a new tab where you can set either of the permissions below using JSON:\n\n ```json\n {\n \"Version\": \"2012-10-17\",\n \"Statement\": [\n {\n \"Effect\": \"Allow\",\n \"Action\": [\n \"autoscaling:*\",\n \"cloudformation:*\",\n \"ec2:*\",\n \"eks:*\",\n \"iam:*\",\n \"ssm:*\"\n ],\n \"Resource\": \"*\"\n }\n ]\n }\n ```\n\n This gives GitLab full access to create and manage resources, as seen in the image below:\n\n ![AWS Role Policy](https://about.gitlab.com/images/blogimages/gitlab-eks-integration/create_role_policy.png)\n\n If you prefer limited permission, you can give GitLab the ability to create resources, but not delete them with the JSON snippet below. The drawback here is if an error is encountered during the creation process, changes will not be rolled back and you must remove resources manually. You can do this by deleting the relevant CloudFormation stack.\n\n ```json\n {\n \"Version\": \"2012-10-17\",\n \"Statement\": [\n {\n \"Effect\": \"Allow\",\n \"Action\": [\n \"autoscaling:CreateAutoScalingGroup\",\n \"autoscaling:DescribeAutoScalingGroups\",\n \"autoscaling:DescribeScalingActivities\",\n \"autoscaling:UpdateAutoScalingGroup\",\n \"autoscaling:CreateLaunchConfiguration\",\n \"autoscaling:DescribeLaunchConfigurations\",\n \"cloudformation:CreateStack\",\n \"cloudformation:DescribeStacks\",\n \"ec2:AuthorizeSecurityGroupEgress\",\n \"ec2:AuthorizeSecurityGroupIngress\",\n \"ec2:RevokeSecurityGroupEgress\",\n \"ec2:RevokeSecurityGroupIngress\",\n \"ec2:CreateSecurityGroup\",\n \"ec2:createTags\",\n \"ec2:DescribeImages\",\n \"ec2:DescribeKeyPairs\",\n \"ec2:DescribeRegions\",\n \"ec2:DescribeSecurityGroups\",\n \"ec2:DescribeSubnets\",\n \"ec2:DescribeVpcs\",\n \"eks:CreateCluster\",\n \"eks:DescribeCluster\",\n \"iam:AddRoleToInstanceProfile\",\n \"iam:AttachRolePolicy\",\n \"iam:CreateRole\",\n \"iam:CreateInstanceProfile\",\n \"iam:CreateServiceLinkedRole\",\n \"iam:GetRole\",\n \"iam:ListRoles\",\n \"iam:PassRole\",\n \"ssm:GetParameters\"\n ],\n \"Resource\": \"*\"\n }\n ]\n }\n ```\n\n The image below visualizes what permissions are granted:\n\n ![Limited Role Policy](https://about.gitlab.com/images/blogimages/gitlab-eks-integration/limited_role_policy.png)\n\n7. Once the policy is created, return to the “Create Role” browser tab and refresh to see the policy we created listed. Select the policy and click “Next.”\n8. In the Tags section, we don’t need to set any Tags, except if it’s required in your organization. Let’s proceed to Review.\n9. Specify a Name for your new Role. You will see the policy we created listed under policies and click “Create Role” to complete the process.\n10. Click on the new Role you created in the list of Roles to view its details. You may have to search for it in the list of Roles if it’s not listed in the first view. Copy the Role ARN provided – we will need it on the GitLab Kubernetes Integration page.\n\n### Step 2 - Create Service Role\n\nThe Service Role is required to allow Amazon EKS and the Kubernetes control plane to manage AWS resources on your behalf.\n\n1. In the IAM Management Console, click on “Create Role” and select the “AWS service” tab.\n2. Select EKS in the list of services and Use Cases as shown below and click Next.\n\n ![Service Role](https://about.gitlab.com/images/blogimages/gitlab-eks-integration/service_role.png)\n\n3. You will notice the “AmazonEKSClusterPolicy” and “AmazonEKSServicePolicy” permissions are selected; these are all we need. Click through the Tags step and create if necessary, then click Next to get to the Review step. Click “Create Role” to complete the process.\n\n ![Role Summary](https://about.gitlab.com/images/blogimages/gitlab-eks-integration/role_summary.png)\n\n## GitLab EKS Integration\n\nThis is the easy part! As mentioned earlier, you only need to create the Provision and Service role once if you don’t already have them in your organization’s AWS setup. You can reuse the roles for other integrations or cluster creations.\n\n1. Return to the GitLab Kubernetes Integration page and provide the Role ARN of the Provision Role we created earlier and click “Authenticate with AWS.”\n\n ![Gitlab EKS Integration Page](https://about.gitlab.com/images/blogimages/gitlab-eks-integration/gitlab_eks_integration_page.png)\n\n2. Once authenticated, you’ll have a page to set the parameters needed to set up your cluster as shown in the image below and click on “Create Kubernetes Cluster” to let GitLab do its magic!\n\n The parameters you’ll need to provide are:\n * **Kubernetes cluster name** - The name you wish to give the cluster.\n * **Environment scope** - The [GitLab environment](https://docs.gitlab.com/ee/user/project/clusters/index.html#setting-the-environment-scope) associated with this cluster; `*` denotes the cluster will be used for deployments to all environments.\n * **Kubernetes version** - The Kubernetes version to use. Currently, the only version supported is 1.14.\n * **Role name** - The service role we created earlier.\n * **Region** - The [AWS region](https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/using-regions-availability-zones.html) in which the cluster will be created.\n * **Key pair name** - Select the [key pair](https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/ec2-key-pairs.html) that you can use to connect to your worker nodes if required.\n * **VPC** - Select a [VPC](https://docs.aws.amazon.com/vpc/latest/userguide/what-is-amazon-vpc.html) to use for your EKS Cluster resources.\n * **Subnets** - Choose the [subnets](https://docs.aws.amazon.com/vpc/latest/userguide/VPC_Subnets.html) in your VPC where your worker nodes will run.\n * **Security group** - Choose the [security group](https://docs.aws.amazon.com/vpc/latest/userguide/VPC_SecurityGroups.html) to apply to the EKS-managed Elastic Network Interfaces that are created in your worker node subnets. AWS provides a default group, which can be used for the purpose of this guide. However, you are advised to setup up the right rules required for your resources.\n * **Instance type** - The AWS [instance type](https://aws.amazon.com/ec2/instance-types/) of your worker nodes.\n * **Node count** - The number of worker nodes.\n * **GitLab-managed cluster** - Leave this checked if you want [GitLab to manage namespaces and service accounts](https://docs.gitlab.com/ee/user/project/clusters/index.html#gitlab-managed-clusters) for this cluster.\n\n ![Gitlab EKS Integration Page](https://about.gitlab.com/images/blogimages/gitlab-eks-integration/gitlab_eks_integration_post_auth.png)\n\n3. The cluster creation process will take approximately 10 minutes. Once done you can proceed to install some predefined applications. At the very least, you need to install the following:\n - **Helm Tiller**: This is required to install the other applications.\n - **Ingress**: This provides SSL termination, load balancing and name-based virtual hosting you your applications. It acts as a web proxy for your application, which is useful when using AutoDevOps or deploying your own apps.\n - **Cert Manager**: This is a native Kubernetes certificate management controller, which helps in issuing certificates using Let’s Encrypt. You don’t need this if you want to use a custom Certificate issuer.\n - **Prometheus**: GitLab uses the Prometheus integration for automatic monitoring of your applications to collect metrics from Kubernetes containers allowing you to understand what is going on from within the GitLab UI.\n\n ![Gitlab EKS Integration Page](https://about.gitlab.com/images/blogimages/gitlab-eks-integration/gitlab_eks_integration_post_cluster.png)\n\n4. To make use of Auto Review Apps and Auto Deploy stages of [AutoDevOps](https://docs.gitlab.com/ee/topics/autodevops/quick_start_guide.html), you will need to specify a Base Domain name with a wild card DNS pointing to the Ingress Endpoint generated when you Install Ingress in the list of predefined apps.\n\n## Summary\n\nIn this tutorial, we looked at how GitLab integrates with Amazon EKS, allowing Kubernetes clusters to be created easily from the GitLab UI after setting the right permissions. As we’ve seen, developer productivity is greatly improved by no longer having to manually set up clusters. Also, the same cluster can be used for multiple projects when Amazon EKS is integrated with GitLab at the Group and Instance levels, thus making onboarding new projects a breeze. After integration, the possibilities of what developers can achieve is enormous.\n\nIn the next part of this tutorial, we will look at how to deploy your applications on an Amazon EKS cluster using AutoDevOps.\n",[23,24,25],"kubernetes","features","demo","yml",{},true,"/en-us/blog/gitlab-eks-integration-how-to",{"title":15,"description":16,"ogTitle":15,"ogDescription":16,"noIndex":12,"ogImage":19,"ogUrl":31,"ogSiteName":32,"ogType":33,"canonicalUrls":31},"https://about.gitlab.com/blog/gitlab-eks-integration-how-to","https://about.gitlab.com","article","en-us/blog/gitlab-eks-integration-how-to",[23,24,25],"u8gOJw9NJP6usLaBazn96skHXR2MefbH7_On5csRtpA",{"data":38},{"logo":39,"freeTrial":44,"sales":49,"login":54,"items":59,"search":367,"minimal":398,"duo":417,"switchNav":426,"pricingDeployment":437},{"config":40},{"href":41,"dataGaName":42,"dataGaLocation":43},"/","gitlab logo","header",{"text":45,"config":46},"Get free 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CI/CD tools can run a build and ship a deployment. Where they diverge is what happens when your delivery needs get real: a monorepo with a dozen services, microservices spread across multiple repositories, deployments to dozens of environments, or a platform team trying to enforce standards without becoming a bottleneck.\n \nGitLab's pipeline execution model was designed for that complexity. Parent-child pipelines, DAG execution, dynamic pipeline generation, multi-project triggers, merge request pipelines with merged results, and CI/CD Components each solve a distinct class of problems. Because they compose, understanding the full model unlocks something more than a faster pipeline. In this article, you'll learn about the five patterns where that model stands out, each mapped to a real engineering scenario with the configuration to match.\n \nThe configs below are illustrative. The scripts use echo commands to keep the signal-to-noise ratio low. Swap them out for your actual build, test, and deploy steps and they are ready to use.\n\n\n## 1. Monorepos: Parent-child pipelines + DAG execution\n\n\nThe problem: Your monorepo has a frontend, a backend, and a docs site. Every commit triggers a full rebuild of everything, even when only a README changed.\n\n\nGitLab solves this with two complementary features: [parent-child pipelines](https://docs.gitlab.com/ci/pipelines/downstream_pipelines/#parent-child-pipelines) (which let a top-level pipeline spawn isolated sub-pipelines) and [DAG execution via `needs`](https://docs.gitlab.com/ci/yaml/#needs) (which breaks rigid stage-by-stage ordering and lets jobs start the moment their dependencies finish).\n\n\nA parent pipeline detects what changed and triggers only the relevant child pipelines:\n\n```yaml\n# .gitlab-ci.yml\nstages:\n - trigger\n\ntrigger-services:\n stage: trigger\n trigger:\n include:\n - local: '.gitlab/ci/api-service.yml'\n - local: '.gitlab/ci/web-service.yml'\n - local: '.gitlab/ci/worker-service.yml'\n strategy: depend\n```\n\n\nEach child pipeline is a fully independent pipeline with its own stages, jobs, and artifacts. The parent waits for all of them via [strategy: depend](https://docs.gitlab.com/ci/pipelines/downstream_pipelines/#wait-for-downstream-pipeline-to-complete) so you get a single green/red signal at the top level, with full drill-down into each service's pipeline. This organizational separation is the bigger win for large teams: each service owns its pipeline config, changes in one cannot break another, and the complexity stays manageable as the repo grows.\n\n\nOne thing worth knowing: when you pass [multiple files to a single `trigger: include:`](https://docs.gitlab.com/ci/pipelines/downstream_pipelines/#combine-multiple-child-pipeline-configuration-files), GitLab merges them into a single child pipeline configuration. This means jobs defined across those files share the same pipeline context and can reference each other with `needs:`, which is what makes the DAG optimization possible. If you split them into separate trigger jobs instead, each would be its own isolated pipeline and cross-file `needs:` references would not work.\n\n\nCombine this with `needs:` inside each child pipeline and you get DAG execution. Your integration tests can start the moment the build finishes, without waiting for other jobs in the same stage.\n\n```yaml\n# .gitlab/ci/api-service.yml\nstages:\n - build\n - test\n\nbuild-api:\n stage: build\n script:\n - echo \"Building API service\"\n\ntest-api:\n stage: test\n needs: [build-api]\n script:\n - echo \"Running API tests\"\n```\n\n\nWhy it matters: Teams with large monorepos typically report significant reductions in pipeline runtime after switching to DAG execution, since jobs no longer wait on unrelated work in the same stage. Parent-child pipelines add the organizational layer that keeps the configuration maintainable as the repo and team grow.\n\n![Local downstream pipelines](https://res.cloudinary.com/about-gitlab-com/image/upload/v1775738759/Blog/Imported/hackathon-fake-blog-post-s/image3_vwj3rz.png \"Local downstream pipelines\")\n\n## 2. Microservices: Cross-repo, multi-project pipelines\n\n\nThe problem: Your frontend lives in one repo, your backend in another. When the frontend team ships a change, they have no visibility into whether it broke the backend integration and vice versa.\n\n\nGitLab's [multi-project pipelines](https://docs.gitlab.com/ci/pipelines/downstream_pipelines/#multi-project-pipelines) let one project trigger a pipeline in a completely separate project and wait for the result. The triggering project gets a linked downstream pipeline right in its own pipeline view.\n\n\nThe frontend pipeline builds an API contract artifact and publishes it, then triggers the backend pipeline. The backend fetches that artifact directly using the [Jobs API](https://docs.gitlab.com/ee/api/jobs.html#download-a-single-artifact-file-from-specific-tag-or-branch) and validates it before allowing anything to proceed. If a breaking change is detected, the backend pipeline fails and the frontend pipeline fails with it.\n\n```yaml\n# frontend repo: .gitlab-ci.yml\nstages:\n - build\n - test\n - trigger-backend\n\nbuild-frontend:\n stage: build\n script:\n - echo \"Building frontend and generating API contract...\"\n - mkdir -p dist\n - |\n echo '{\n \"api_version\": \"v2\",\n \"breaking_changes\": false\n }' > dist/api-contract.json\n - cat dist/api-contract.json\n artifacts:\n paths:\n - dist/api-contract.json\n expire_in: 1 hour\n\ntest-frontend:\n stage: test\n script:\n - echo \"All frontend tests passed!\"\n\ntrigger-backend-pipeline:\n stage: trigger-backend\n trigger:\n project: my-org/backend-service\n branch: main\n strategy: depend\n rules:\n - if: $CI_COMMIT_BRANCH == \"main\"\n```\n\n```yaml\n# backend repo: .gitlab-ci.yml\nstages:\n - build\n - test\n\nbuild-backend:\n stage: build\n script:\n - echo \"All backend tests passed!\"\n\nintegration-test:\n stage: test\n rules:\n - if: $CI_PIPELINE_SOURCE == \"pipeline\"\n script:\n - echo \"Fetching API contract from frontend...\"\n - |\n curl --silent --fail \\\n --header \"JOB-TOKEN: $CI_JOB_TOKEN\" \\\n --output api-contract.json \\\n \"${CI_API_V4_URL}/projects/${FRONTEND_PROJECT_ID}/jobs/artifacts/main/raw/dist/api-contract.json?job=build-frontend\"\n - cat api-contract.json\n - |\n if grep -q '\"breaking_changes\": true' api-contract.json; then\n echo \"FAIL: Breaking API changes detected - backend integration blocked!\"\n exit 1\n fi\n echo \"PASS: API contract is compatible!\"\n```\n\n\nA few things worth noting in this config. The `integration-test` job uses `$CI_PIPELINE_SOURCE == \"pipeline\"` to ensure it only runs when triggered by an upstream pipeline, not on a standalone push to the backend repo. The frontend project ID is referenced via `$FRONTEND_PROJECT_ID`, which should be set as a [CI/CD variable](https://docs.gitlab.com/ci/variables/) in the backend project settings to avoid hardcoding it.\n\n\nWhy it matters: Cross-service breakage that previously surfaced in production gets caught in the pipeline instead. The dependency between services stops being invisible and becomes something teams can see, track, and act on.\n\n\n![Cross-project pipelines](https://res.cloudinary.com/about-gitlab-com/image/upload/v1775738762/Blog/Imported/hackathon-fake-blog-post-s/image4_h6mfsb.png \"Cross-project pipelines\")\n\n\n## 3. Multi-tenant / matrix deployments: Dynamic child pipelines\n\n\nThe problem: You deploy the same application to 15 customer environments, or three cloud regions, or dev/staging/prod. Updating a deploy stage across all of them one by one is the kind of work that leads to configuration drift. Writing a separate pipeline for each environment is unmaintainable from day one.\n\n\nGitLab's [dynamic child pipelines](https://docs.gitlab.com/ci/pipelines/downstream_pipelines/#dynamic-child-pipelines) let you generate a pipeline at runtime. A job runs a script that produces a YAML file, and that YAML becomes the pipeline for the next stage. The pipeline structure itself becomes data.\n\n\n```yaml\n# .gitlab-ci.yml\nstages:\n - generate\n - trigger-environments\n\ngenerate-config:\n stage: generate\n script:\n - |\n # ENVIRONMENTS can be passed as a CI variable or read from a config file.\n # Default to dev, staging, prod if not set.\n ENVIRONMENTS=${ENVIRONMENTS:-\"dev staging prod\"}\n for ENV in $ENVIRONMENTS; do\n cat > ${ENV}-pipeline.yml \u003C\u003C EOF\n stages:\n - deploy\n - verify\n deploy-${ENV}:\n stage: deploy\n script:\n - echo \"Deploying to ${ENV} environment\"\n verify-${ENV}:\n stage: verify\n script:\n - echo \"Running smoke tests on ${ENV}\"\n EOF\n done\n artifacts:\n paths:\n - \"*.yml\"\n exclude:\n - \".gitlab-ci.yml\"\n\n.trigger-template:\n stage: trigger-environments\n trigger:\n strategy: depend\n\ntrigger-dev:\n extends: .trigger-template\n trigger:\n include:\n - artifact: dev-pipeline.yml\n job: generate-config\n\ntrigger-staging:\n extends: .trigger-template\n needs: [trigger-dev]\n trigger:\n include:\n - artifact: staging-pipeline.yml\n job: generate-config\n\ntrigger-prod:\n extends: .trigger-template\n needs: [trigger-staging]\n trigger:\n include:\n - artifact: prod-pipeline.yml\n job: generate-config\n when: manual\n```\n\n\nThe generation script loops over an `ENVIRONMENTS` variable rather than hardcoding each environment separately. Pass in a different list via a CI variable or read it from a config file and the pipeline adapts without touching the YAML. The trigger jobs use [extends:](https://docs.gitlab.com/ci/yaml/#extends) to inherit shared configuration from `.trigger-template`, so `strategy: depend` is defined once rather than repeated on every trigger job. Add a new environment by updating the variable, not by duplicating pipeline config. Add [when: manual](https://docs.gitlab.com/ci/yaml/#when) to the production trigger and you get a promotion gate baked right into the pipeline graph.\n\n\nWhy it matters: SaaS companies and platform teams use this pattern to manage dozens of environments without duplicating pipeline logic. The pipeline structure itself stays lean as the deployment matrix grows.\n\n\n![Dynamic pipeline](https://res.cloudinary.com/about-gitlab-com/image/upload/v1775738765/Blog/Imported/hackathon-fake-blog-post-s/image7_wr0kx2.png \"Dynamic pipeline\")\n\n\n## 4. MR-first delivery: Merge request pipelines, merged results, and workflow routing\n\n\nThe problem: Your pipeline runs on every push to every branch. Expensive tests run on feature branches that will never merge. Meanwhile, you have no guarantee that what you tested is actually what will land on `main` after a merge.\n\n\nGitLab has three interlocking features that solve this together:\n\n\n* [Merge request pipelines](https://docs.gitlab.com/ci/pipelines/merge_request_pipelines/) run only when a merge request exists, not on every branch push. This alone eliminates a significant amount of wasted compute.\n\n* [Merged results pipelines](https://docs.gitlab.com/ci/pipelines/merged_results_pipelines/) go further. GitLab creates a temporary merge commit (your branch plus the current target branch) and runs the pipeline against that. You are testing what will actually exist after the merge, not just your branch in isolation.\n\n* [Workflow rules](https://docs.gitlab.com/ci/yaml/workflow/) let you define exactly which pipeline type runs under which conditions and suppress everything else. The `$CI_OPEN_MERGE_REQUESTS` guard below prevents duplicate pipelines firing for both a branch and its open MR simultaneously.\n\n\nWith those three working together, here is what a tiered pipeline looks like:\n\n```yaml\n# .gitlab-ci.yml\nworkflow:\n rules:\n - if: $CI_PIPELINE_SOURCE == \"merge_request_event\"\n - if: $CI_COMMIT_BRANCH && $CI_OPEN_MERGE_REQUESTS\n when: never\n - if: $CI_COMMIT_BRANCH\n - if: $CI_PIPELINE_SOURCE == \"schedule\"\n\nstages:\n - fast-checks\n - expensive-tests\n - deploy\n\nlint-code:\n stage: fast-checks\n script:\n - echo \"Running linter\"\n rules:\n - if: $CI_PIPELINE_SOURCE == \"push\"\n - if: $CI_PIPELINE_SOURCE == \"merge_request_event\"\n - if: $CI_COMMIT_BRANCH == \"main\"\n\nunit-tests:\n stage: fast-checks\n script:\n - echo \"Running unit tests\"\n rules:\n - if: $CI_PIPELINE_SOURCE == \"push\"\n - if: $CI_PIPELINE_SOURCE == \"merge_request_event\"\n - if: $CI_COMMIT_BRANCH == \"main\"\n\nintegration-tests:\n stage: expensive-tests\n script:\n - echo \"Running integration tests (15 min)\"\n rules:\n - if: $CI_PIPELINE_SOURCE == \"merge_request_event\"\n - if: $CI_COMMIT_BRANCH == \"main\"\n\ne2e-tests:\n stage: expensive-tests\n script:\n - echo \"Running E2E tests (30 min)\"\n rules:\n - if: $CI_PIPELINE_SOURCE == \"merge_request_event\"\n - if: $CI_COMMIT_BRANCH == \"main\"\n\nnightly-comprehensive-scan:\n stage: expensive-tests\n script:\n - echo \"Running full nightly suite (2 hours)\"\n rules:\n - if: $CI_PIPELINE_SOURCE == \"schedule\"\n\ndeploy-production:\n stage: deploy\n script:\n - echo \"Deploying to production\"\n rules:\n - if: $CI_COMMIT_BRANCH == \"main\"\n when: manual\n```\n\nWith this setup, the pipeline behaves differently depending on context. A push to a feature branch with no open MR runs lint and unit tests only. Once an MR is opened, the workflow rules switch from a branch pipeline to an MR pipeline, and the full integration and E2E suite runs against the merged result. Merging to `main` queues a manual production deployment. A nightly schedule runs the comprehensive scan once, not on every commit.\n\n\nWhy it matters: Teams routinely cut CI costs significantly with this pattern, not by running fewer tests, but by running the right tests at the right time. Merged results pipelines catch the class of bugs that only appear after a merge, before they ever reach `main`.\n\n\n![Conditional pipelines (within a branch with no MR)](https://res.cloudinary.com/about-gitlab-com/image/upload/v1775738768/Blog/Imported/hackathon-fake-blog-post-s/image6_dnfcny.png \"Conditional pipelines (within a branch with no MR)\")\n\n\n\n![Conditional pipelines (within an MR)](https://res.cloudinary.com/about-gitlab-com/image/upload/v1775738772/Blog/Imported/hackathon-fake-blog-post-s/image1_wyiafu.png \"Conditional pipelines (within an MR)\")\n\n\n\n![Conditional pipelines (on the main branch)](https://res.cloudinary.com/about-gitlab-com/image/upload/v1775738774/Blog/Imported/hackathon-fake-blog-post-s/image5_r6lkfd.png \"Conditional pipelines (on the main branch)\")\n\n## 5. Governed pipelines: CI/CD Components\n\n\nThe problem: Your platform team has defined the right way to build, test, and deploy. But every team has their own `.gitlab-ci.yml` with subtle variations. Security scanning gets skipped. Deployment standards drift. Audits are painful.\n\n\nGitLab [CI/CD Components](https://docs.gitlab.com/ci/components/) let platform teams publish versioned, reusable pipeline building blocks. Application teams consume them with a single `include:` line and optional inputs — no copy-paste, no drift. Components are discoverable through the [CI/CD Catalog](https://docs.gitlab.com/ci/components/#cicd-catalog), which means teams can find and adopt approved building blocks without needing to go through the platform team directly.\n\n\nHere is a component definition from a shared library:\n\n```yaml\n# templates/deploy.yml\nspec:\n inputs:\n stage:\n default: deploy\n environment:\n default: production\n---\ndeploy-job:\n stage: $[[ inputs.stage ]]\n script:\n - echo \"Deploying $APP_NAME to $[[ inputs.environment ]]\"\n - echo \"Deploy URL: $DEPLOY_URL\"\n environment:\n name: $[[ inputs.environment ]]\n```\nAnd here is how an application team consumes it:\n\n```yaml\n# Application repo: .gitlab-ci.yml\nvariables:\n APP_NAME: \"my-awesome-app\"\n DEPLOY_URL: \"https://api.example.com\"\n\ninclude:\n - component: gitlab.com/my-org/component-library/build@v1.0.6\n - component: gitlab.com/my-org/component-library/test@v1.0.6\n - component: gitlab.com/my-org/component-library/deploy@v1.0.6\n inputs:\n environment: staging\n\nstages:\n - build\n - test\n - deploy\n```\n\nThree lines of `include:` replace hundreds of lines of duplicated YAML. The platform team can push a security fix to `v1.0.7` and teams opt in on their own schedule — or the platform team can pin everyone to a minimum version. Either way, one change propagates everywhere instead of needing to be applied repo by repo.\n\n\nPair this with [resource groups](https://docs.gitlab.com/ci/resource_groups/) to prevent concurrent deployments to the same environment, and [protected environments](https://docs.gitlab.com/ci/environments/protected_environments/) to enforce approval gates - and you have a governed delivery platform where compliance is the default, not the exception.\n\n\nWhy it matters: This is the pattern that makes GitLab CI/CD scale across hundreds of teams. Platform engineering teams enforce compliance without becoming a bottleneck. Application teams get a fast path to a working pipeline without reinventing the wheel.\n\n\n![Component pipeline (imported jobs)](https://res.cloudinary.com/about-gitlab-com/image/upload/v1775738776/Blog/Imported/hackathon-fake-blog-post-s/image2_pizuxd.png \"Component pipeline (imported jobs)\")\n\n## Putting it all together\n\nNone of these features exist in isolation. The reason GitLab's pipeline model is worth understanding deeply is that these primitives compose:\n\n* A monorepo uses parent-child pipelines, and each child uses DAG execution\n\n* A microservices platform uses multi-project pipelines, and each project uses MR pipelines with merged results\n\n* A governed platform uses CI/CD components to standardize the patterns above across every team\n\n\nMost teams discover one of these features when they hit a specific pain point. The ones who invest in understanding the full model end up with a delivery system that actually reflects how their engineering organization works, not a pipeline that fights it.\n\n## Other patterns worth exploring\n\n\nThe five patterns above cover the most common structural pain points, but GitLab's pipeline model goes further. A few others worth looking into as your needs grow:\n\n\n* [Review apps with dynamic environments](https://docs.gitlab.com/ci/environments/) let you spin up a live preview for every feature branch and tear it down automatically when the MR closes. Useful for teams doing frontend work or API changes that need stakeholder sign-off before merging.\n\n* [Caching and artifact strategies](https://docs.gitlab.com/ci/caching/) are often the fastest way to cut pipeline runtime after the structural work is done. Structuring `cache:` keys around dependency lockfiles and being deliberate about what gets passed between jobs with [artifacts:](https://docs.gitlab.com/ci/yaml/#artifacts) can make a significant difference without changing your pipeline shape at all.\n\n* [Scheduled and API-triggered pipelines](https://docs.gitlab.com/ci/pipelines/schedules/) are worth knowing about because not everything should run on a code push. Nightly security scans, compliance reports, and release automation are better modeled as scheduled or [API-triggered](https://docs.gitlab.com/ci/triggers/) pipelines with `$CI_PIPELINE_SOURCE` routing the right jobs for each context.\n\n## How to get started\n\nModern software delivery is complex. Teams are managing monorepos with dozens of services, coordinating across multiple repositories, deploying to many environments at once, and trying to keep standards consistent as organizations grow. GitLab's pipeline model was built with all of that in mind.\n\nWhat makes it worth investing time in is how well the pieces fit together. Parent-child pipelines bring structure to large codebases. Multi-project pipelines make cross-team dependencies visible and testable. Dynamic pipelines turn environment management into something that scales gracefully. MR-first delivery with merged results ensures confidence at every step of the review process. And CI/CD Components give platform teams a way to share best practices across an entire organization without becoming a bottleneck.\n\nEach of these features is powerful on its own, and even more so when combined. GitLab gives you the building blocks to design a delivery system that fits how your team actually works, and grows with you as your needs evolve.\n\n> [Start a free trial of GitLab Ultimate](https://about.gitlab.com/free-trial/) to use pipeline logic today.\n\n## Read more\n\n* [Variable and artifact sharing in GitLab parent-child pipelines](https://about.gitlab.com/blog/variable-and-artifact-sharing-in-gitlab-parent-child-pipelines/)\n* [CI/CD inputs: Secure and preferred method to pass parameters to a pipeline](https://about.gitlab.com/blog/ci-cd-inputs-secure-and-preferred-method-to-pass-parameters-to-a-pipeline/)\n* [Tutorial: How to set up your first GitLab CI/CD component](https://about.gitlab.com/blog/tutorial-how-to-set-up-your-first-gitlab-ci-cd-component/)\n* [How to include file references in your CI/CD components](https://about.gitlab.com/blog/how-to-include-file-references-in-your-ci-cd-components/)\n* [FAQ: GitLab CI/CD Catalog](https://about.gitlab.com/blog/faq-gitlab-ci-cd-catalog/)\n* [Building a GitLab CI/CD pipeline for a monorepo the easy way](https://about.gitlab.com/blog/building-a-gitlab-ci-cd-pipeline-for-a-monorepo-the-easy-way/)\n* [A CI/CD component builder's journey](https://about.gitlab.com/blog/a-ci-component-builders-journey/)\n* [CI/CD Catalog goes GA: No more building pipelines from scratch](https://about.gitlab.com/blog/ci-cd-catalog-goes-ga-no-more-building-pipelines-from-scratch/)","5 ways GitLab pipeline logic solves real engineering problems","Learn how to scale CI/CD with composable patterns for monorepos, microservices, environments, and governance.",[720],"Omid Khan","https://res.cloudinary.com/about-gitlab-com/image/upload/v1772721753/frfsm1qfscwrmsyzj1qn.png","2026-04-09",[106,724,725,24],"DevOps platform","tutorial",{"featured":28,"template":13,"slug":727},"5-ways-gitlab-pipeline-logic-solves-real-engineering-problems",{"content":729,"config":739},{"title":730,"description":731,"authors":732,"heroImage":734,"date":735,"body":736,"category":9,"tags":737},"How to use GitLab Container Virtual Registry with Docker Hardened Images","Learn how to simplify container image management with this step-by-step guide.",[733],"Tim Rizzi","https://res.cloudinary.com/about-gitlab-com/image/upload/v1772111172/mwhgbjawn62kymfwrhle.png","2026-03-12","If you're a platform engineer, you've probably had this conversation:\n \n*\"Security says we need to use hardened base images.\"*\n\n*\"Great, where do I configure credentials for yet another registry?\"*\n\n*\"Also, how do we make sure everyone actually uses them?\"*\n\nOr this one:\n\n*\"Why are our builds so slow?\"*\n\n*\"We're pulling the same 500MB image from Docker Hub in every single job.\"*\n\n*\"Can't we just cache these somewhere?\"*\n\nI've been working on [Container Virtual Registry](https://docs.gitlab.com/user/packages/virtual_registry/container/) at GitLab specifically to solve these problems. It's a pull-through cache that sits in front of your upstream registries — Docker Hub, dhi.io (Docker Hardened Images), MCR, and Quay — and gives your teams a single endpoint to pull from. Images get cached on the first pull. Subsequent pulls come from the cache. Your developers don't need to know or care which upstream a particular image came from.\n\nThis article shows you how to set up Container Virtual Registry, specifically with Docker Hardened Images in mind, since that's a combination that makes a lot of sense for teams concerned about security and not making their developers' lives harder.\n\n## What problem are we actually solving?\n\nThe Platform teams I usually talk to manage container images across three to five registries:\n\n* **Docker Hub** for most base images\n* **dhi.io** for Docker Hardened Images (security-conscious workloads)\n* **MCR** for .NET and Azure tooling\n* **Quay.io** for Red Hat ecosystem stuff\n* **Internal registries** for proprietary images\n\nEach one has its own:\n\n* Authentication mechanism\n* Network latency characteristics\n* Way of organizing image paths\n\nYour CI/CD configs end up littered with registry-specific logic. Credential management becomes a project unto itself. And every pipeline job pulls the same base images over the network, even though they haven't changed in weeks.\n\nContainer Virtual Registry consolidates this. One registry URL. One authentication flow (GitLab's). Cached images are served from GitLab's infrastructure rather than traversing the internet each time.\n\n## How it works\n\nThe model is straightforward:\n\n```text\nYour pipeline pulls:\n gitlab.com/virtual_registries/container/1000016/python:3.13\n\nVirtual registry checks:\n 1. Do I have this cached? → Return it\n 2. No? → Fetch from upstream, cache it, return it\n\n```\n\nYou configure upstreams in priority order. When a pull request comes in, the virtual registry checks each upstream until it finds the image. The result gets cached for a configurable period (default 24 hours).\n\n```text\n┌─────────────────────────────────────────────────────────┐\n│ CI/CD Pipeline │\n│ │ │\n│ ▼ │\n│ gitlab.com/virtual_registries/container/\u003Cid>/image │\n└─────────────────────────────────────────────────────────┘\n │\n ▼\n┌─────────────────────────────────────────────────────────┐\n│ Container Virtual Registry │\n│ │\n│ Upstream 1: Docker Hub ────────────────┐ │\n│ Upstream 2: dhi.io (Hardened) ────────┐│ │\n│ Upstream 3: MCR ─────────────────────┐││ │\n│ Upstream 4: Quay.io ────────────────┐│││ │\n│ ││││ │\n│ ┌─────────────────┴┴┴┴──┐ │\n│ │ Cache │ │\n│ │ (manifests + layers) │ │\n│ └───────────────────────┘ │\n└─────────────────────────────────────────────────────────┘\n```\n\n## Why this matters for Docker Hardened Images\n\n[Docker Hardened Images](https://docs.docker.com/dhi/) are great because of the minimal attack surface, near-zero CVEs, proper software bills of materials (SBOMs), and SLSA provenance. If you're evaluating base images for security-sensitive workloads, they should be on your list.\n\nBut adopting them creates the same operational friction as any new registry:\n\n* **Credential distribution**: You need to get Docker credentials to every system that pulls images from dhi.io.\n* **CI/CD changes**: Every pipeline needs to be updated to authenticate with dhi.io.\n* **Developer friction**: People need to remember to use the hardened variants.\n* **Visibility gap**: It's difficult to tell if teams are actually using hardened images vs. regular ones.\n\nVirtual registry addresses each of these:\n\n**Single credential**: Teams authenticate to GitLab. The virtual registry handles upstream authentication. You configure Docker credentials once, at the registry level, and they apply to all pulls.\n\n**No CI/CD changes per-team**: Point pipelines at your virtual registry. Done. The upstream configuration is centralized.\n\n**Gradual adoption**: Since images get cached with their full path, you can see in the cache what's being pulled. If someone's pulling `library/python:3.11` instead of the hardened variant, you'll know.\n\n**Audit trail**: The cache shows you exactly which images are in active use. Useful for compliance, useful for understanding what your fleet actually depends on.\n\n## Setting it up\n\nHere's a real setup using the Python client from this demo project.\n\n### Create the virtual registry\n\n```python\nfrom virtual_registry_client import VirtualRegistryClient\n\nclient = VirtualRegistryClient()\n\nregistry = client.create_virtual_registry(\n group_id=\"785414\", # Your top-level group ID\n name=\"platform-images\",\n description=\"Cached container images for platform teams\"\n)\n\nprint(f\"Registry ID: {registry['id']}\")\n# You'll need this ID for the pull URL\n```\n\n### Add Docker Hub as an upstream\n\nFor official images like Alpine, Python, etc.:\n\n```python\ndocker_upstream = client.create_upstream(\n registry_id=registry['id'],\n url=\"https://registry-1.docker.io\",\n name=\"Docker Hub\",\n cache_validity_hours=24\n)\n```\n\n### Add Docker Hardened Images (dhi.io)\n\nDocker Hardened Images are hosted on `dhi.io`, a separate registry that requires authentication:\n\n```python\ndhi_upstream = client.create_upstream(\n registry_id=registry['id'],\n url=\"https://dhi.io\",\n name=\"Docker Hardened Images\",\n username=\"your-docker-username\",\n password=\"your-docker-access-token\",\n cache_validity_hours=24\n)\n```\n\n### Add other upstreams\n\n```python\n# MCR for .NET teams\nclient.create_upstream(\n registry_id=registry['id'],\n url=\"https://mcr.microsoft.com\",\n name=\"Microsoft Container Registry\",\n cache_validity_hours=48\n)\n\n# Quay for Red Hat stuff\nclient.create_upstream(\n registry_id=registry['id'],\n url=\"https://quay.io\",\n name=\"Quay.io\",\n cache_validity_hours=24\n)\n```\n\n### Update your CI/CD\n\nHere's a `.gitlab-ci.yml` that pulls through the virtual registry:\n\n```yaml\nvariables:\n VIRTUAL_REGISTRY_ID: \u003Cyour_virtual_registry_ID>\n\n \nbuild:\n image: docker:24\n services:\n - docker:24-dind\n before_script:\n # Authenticate to GitLab (which handles upstream auth for you)\n - echo \"${CI_JOB_TOKEN}\" | docker login -u gitlab-ci-token --password-stdin gitlab.com\n script:\n # All of these go through your single virtual registry\n \n # Official Docker Hub images (use library/ prefix)\n - docker pull gitlab.com/virtual_registries/container/${VIRTUAL_REGISTRY_ID}/library/alpine:latest\n \n # Docker Hardened Images from dhi.io (no prefix needed)\n - docker pull gitlab.com/virtual_registries/container/${VIRTUAL_REGISTRY_ID}/python:3.13\n \n # .NET from MCR\n - docker pull gitlab.com/virtual_registries/container/${VIRTUAL_REGISTRY_ID}/dotnet/sdk:8.0\n```\n\n### Image path formats\n\nDifferent registries use different path conventions:\n\n| Registry | Pull URL Example |\n|----------|------------------|\n| Docker Hub (official) | `.../library/python:3.11-slim` |\n| Docker Hardened Images (dhi.io) | `.../python:3.13` |\n| MCR | `.../dotnet/sdk:8.0` |\n| Quay.io | `.../prometheus/prometheus:latest` |\n\n### Verify it's working\n\nAfter some pulls, check your cache:\n\n```python\nupstreams = client.list_registry_upstreams(registry['id'])\nfor upstream in upstreams:\n entries = client.list_cache_entries(upstream['id'])\n print(f\"{upstream['name']}: {len(entries)} cached entries\")\n\n```\n\n## What the numbers look like\n\nI ran tests pulling images through the virtual registry:\n\n| Metric | Without Cache | With Warm Cache |\n|--------|---------------|-----------------|\n| Pull time (Alpine) | 10.3s | 4.2s |\n| Pull time (Python 3.13 DHI) | 11.6s | ~4s |\n| Network roundtrips to upstream | Every pull | Cache misses only |\n\n\n\n\nThe first pull is the same speed (it has to fetch from upstream). Every pull after that, for the cache validity period, comes straight from GitLab's storage. No network hop to Docker Hub, dhi.io, MCR, or wherever the image lives.\n\nFor a team running hundreds of pipeline jobs per day, that's hours of cumulative build time saved.\n\n## Practical considerations\nHere are some considerations to keep in mind:\n\n### Cache validity\n\n24 hours is the default. For security-sensitive images where you want patches quickly, consider 12 hours or less:\n\n```python\nclient.create_upstream(\n registry_id=registry['id'],\n url=\"https://dhi.io\",\n name=\"Docker Hardened Images\",\n username=\"your-username\",\n password=\"your-token\",\n cache_validity_hours=12\n)\n```\n\nFor stable, infrequently-updated images (like specific version tags), longer validity is fine.\n\n### Upstream priority\n\nUpstreams are checked in order. If you have images with the same name on different registries, the first matching upstream wins.\n\n### Limits\n\n* Maximum of 20 virtual registries per group\n* Maximum of 20 upstreams per virtual registry\n\n## Configuration via UI\n\nYou can also configure virtual registries and upstreams directly from the GitLab UI—no API calls required. Navigate to your group's **Settings > Packages and registries > Virtual Registry** to:\n\n* Create and manage virtual registries\n* Add, edit, and reorder upstream registries\n* View and manage the cache\n* Monitor which images are being pulled\n\n## What's next\n\nWe're actively developing:\n\n* **Allow/deny lists**: Use regex to control which images can be pulled from specific upstreams.\n\nThis is beta software. It works, people are using it in production, but we're still iterating based on feedback.\n\n## Share your feedback\n\nIf you're a platform engineer dealing with container registry sprawl, I'd like to understand your setup:\n\n* How many upstream registries are you managing?\n* What's your biggest pain point with the current state?\n* Would something like this help, and if not, what's missing?\n\nPlease share your experiences in the [Container Virtual Registry feedback issue](https://gitlab.com/gitlab-org/gitlab/-/work_items/589630).\n## Related resources\n- [New GitLab metrics and registry features help reduce CI/CD bottlenecks](https://about.gitlab.com/blog/new-gitlab-metrics-and-registry-features-help-reduce-ci-cd-bottlenecks/#container-virtual-registry)\n- [Container Virtual Registry documentation](https://docs.gitlab.com/user/packages/virtual_registry/container/)\n- [Container Virtual Registry API](https://docs.gitlab.com/api/container_virtual_registries/)",[725,738,24],"product",{"featured":12,"template":13,"slug":740},"using-gitlab-container-virtual-registry-with-docker-hardened-images",{"content":742,"config":752},{"title":743,"description":744,"authors":745,"heroImage":747,"date":748,"category":9,"tags":749,"body":751},"How IIT Bombay students are coding the future with GitLab","At GitLab, we often talk about how software accelerates innovation. But sometimes, you have to step away from the Zoom calls and stand in a crowded university hall to remember why we do this.",[746],"Nick Veenhof","https://res.cloudinary.com/about-gitlab-com/image/upload/v1750099013/Blog/Hero%20Images/Blog/Hero%20Images/blog-image-template-1800x945%20%2814%29_6VTUA8mUhOZNDaRVNPeKwl_1750099012960.png","2026-01-08",[259,621,750],"open source","The GitLab team recently had the privilege of judging the **iHack Hackathon** at **IIT Bombay's E-Summit**. The energy was electric, the coffee was flowing, and the talent was undeniable. But what struck us most wasn't just the code — it was the sheer determination of students to solve real-world problems, often overcoming significant logistical and financial hurdles to simply be in the room.\n\n\nThrough our [GitLab for Education program](https://about.gitlab.com/solutions/education/), we aim to empower the next generation of developers with tools and opportunity. Here is a look at what the students built, and how they used GitLab to bridge the gap between idea and reality.\n\n## The challenge: Build faster, build securely\n\nThe premise for the GitLab track of the hackathon was simple: Don't just show us a product; show us how you built it. We wanted to see how students utilized GitLab's platform — from Issue Boards to CI/CD pipelines — to accelerate the development lifecycle.\n\nThe results were inspiring.\n\n## The winners\n\n### 1st place: Team Decode — Democratizing Scientific Research\n\n**Project:** FIRE (Fast Integrated Research Environment)\n\nTeam Decode took home the top prize with a solution that warms a developer's heart: a local-first, blazing-fast data processing tool built with [Rust](https://about.gitlab.com/blog/secure-rust-development-with-gitlab/) and Tauri. They identified a massive pain point for data science students: existing tools are fragmented, slow, and expensive.\n\nTheir solution, FIRE, allows researchers to visualize complex formats (like NetCDF) instantly. What impressed the judges most was their \"hacker\" ethos. They didn't just build a tool; they built it to be open and accessible.\n\n**How they used GitLab:** Since the team lived far apart, asynchronous communication was key. They utilized **GitLab Issue Boards** and **Milestones** to track progress and integrated their repo with Telegram to get real-time push notifications. As one team member noted, \"Coordinating all these technologies was really difficult, and what helped us was GitLab... the Issue Board really helped us track who was doing what.\"\n\n![Team Decode](https://res.cloudinary.com/about-gitlab-com/image/upload/v1767380253/epqazj1jc5c7zkgqun9h.jpg)\n\n### 2nd place: Team BichdeHueDost — Reuniting to Solve Payments\n\n**Project:** SemiPay (RFID Cashless Payment for Schools)\n\nThe team name, BichdeHueDost, translates to \"Friends who have been set apart.\" It's a fitting name for a group of friends who went to different colleges but reunited to build this project. They tackled a unique problem: handling cash in schools for young children. Their solution used RFID cards backed by a blockchain ledger to ensure secure, cashless transactions for students.\n\n**How they used GitLab:** They utilized [GitLab CI/CD](https://about.gitlab.com/topics/ci-cd/) to automate the build process for their Flutter application (APK), ensuring that every commit resulted in a testable artifact. This allowed them to iterate quickly despite the \"flaky\" nature of cross-platform mobile development.\n\n![Team BichdeHueDost](https://res.cloudinary.com/about-gitlab-com/image/upload/v1767380253/pkukrjgx2miukb6nrj5g.jpg)\n\n### 3rd place: Team ZenYukti — Agentic Repository Intelligence\n\n**Project:** RepoInsight AI (AI-powered, GitLab-native intelligence platform)\n\nTeam ZenYukti impressed us with a solution that tackles a universal developer pain point: understanding unfamiliar codebases. What stood out to the judges was the tool's practical approach to onboarding and code comprehension: RepoInsight-AI automatically generates documentation, visualizes repository structure, and even helps identify bugs, all while maintaining context about the entire codebase.\n\n**How they used GitLab:** The team built a comprehensive CI/CD pipeline that showcased GitLab's security and DevOps capabilities. They integrated [GitLab's Security Templates](https://gitlab.com/gitlab-org/gitlab/-/tree/master/lib/gitlab/ci/templates/Security) (SAST, Dependency Scanning, and Secret Detection), and utilized [GitLab Container Registry](https://docs.gitlab.com/user/packages/container_registry/) to manage their Docker images for backend and frontend components. They created an AI auto-review bot that runs on merge requests, demonstrating an \"agentic workflow\" where AI assists in the development process itself.\n\n![Team ZenYukti](https://res.cloudinary.com/about-gitlab-com/image/upload/v1767380253/ymlzqoruv5al1secatba.jpg)\n\n## Beyond the code: A lesson in inclusion\n\nWhile the code was impressive, the most powerful moment of the event happened away from the keyboard.\n\nDuring the feedback session, we learned about the journey Team ZenYukti took to get to Mumbai. They traveled over 24 hours, covering nearly 1,800 kilometers. Because flights were too expensive and trains were booked, they traveled in the \"General Coach,\" a non-reserved, severely overcrowded carriage.\n\nAs one student described it:\n\n*\"You cannot even imagine something like this... there are no seats... people sit on the top of the train. This is what we have endured.\"*\n\nThis hit home. [Diversity, Inclusion, and Belonging](https://handbook.gitlab.com/handbook/company/culture/inclusion/) are core values at GitLab. We realized that for these students, the barrier to entry wasn't intellect or skill, it was access.\n\nIn that moment, we decided to break that barrier. We committed to reimbursing the travel expenses for the participants who struggled to get there. It's a small step, but it underlines a massive truth: **talent is distributed equally, but opportunity is not.**\n\n![hackathon class together](https://res.cloudinary.com/about-gitlab-com/image/upload/v1767380252/o5aqmboquz8ehusxvgom.jpg)\n\n### The future is bright (and automated)\n\nWe also saw incredible potential in teams like Prometheus, who attempted to build an autonomous patch remediation tool (DevGuardian), and Team Arrakis, who built a voice-first job portal for blue-collar workers using [GitLab Duo](https://about.gitlab.com/gitlab-duo-agent-platform/) to troubleshoot their pipelines.\n\nTo all the students who participated: You are the future. Through [GitLab for Education](https://about.gitlab.com/solutions/education/), we are committed to providing you with the top-tier tools (like GitLab Ultimate) you need to learn, collaborate, and change the world — whether you are coding from a dorm room, a lab, or a train carriage. **Keep shipping.**\n\n> :bulb: Learn more about the [GitLab for Education program](https://about.gitlab.com/solutions/education/).\n",{"slug":753,"featured":12,"template":13},"how-iit-bombay-students-code-future-with-gitlab",{"promotions":755},[756,770,781,793],{"id":757,"categories":758,"header":760,"text":761,"button":762,"image":767},"ai-modernization",[759],"ai-ml","Is AI achieving its promise at scale?","Quiz will take 5 minutes or less",{"text":763,"config":764},"Get your AI maturity score",{"href":765,"dataGaName":766,"dataGaLocation":241},"/assessments/ai-modernization-assessment/","modernization assessment",{"config":768},{"src":769},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/qix0m7kwnd8x2fh1zq49.png",{"id":771,"categories":772,"header":773,"text":761,"button":774,"image":778},"devops-modernization",[738,567],"Are you just managing tools or shipping innovation?",{"text":775,"config":776},"Get your DevOps maturity score",{"href":777,"dataGaName":766,"dataGaLocation":241},"/assessments/devops-modernization-assessment/",{"config":779},{"src":780},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138785/eg818fmakweyuznttgid.png",{"id":782,"categories":783,"header":785,"text":761,"button":786,"image":790},"security-modernization",[784],"security","Are you trading speed for security?",{"text":787,"config":788},"Get your security maturity score",{"href":789,"dataGaName":766,"dataGaLocation":241},"/assessments/security-modernization-assessment/",{"config":791},{"src":792},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/p4pbqd9nnjejg5ds6mdk.png",{"id":794,"paths":795,"header":798,"text":799,"button":800,"image":805},"github-azure-migration",[796,797],"migration-from-azure-devops-to-gitlab","integrating-azure-devops-scm-and-gitlab","Is your team ready for GitHub's Azure move?","GitHub is already rebuilding around Azure. Find out what it means for you.",{"text":801,"config":802},"See how GitLab compares to GitHub",{"href":803,"dataGaName":804,"dataGaLocation":241},"/compare/gitlab-vs-github/github-azure-migration/","github azure migration",{"config":806},{"src":780},{"header":808,"blurb":809,"button":810,"secondaryButton":815},"Start building faster today","See what your team can do with the intelligent orchestration platform for DevSecOps.\n",{"text":811,"config":812},"Get your free trial",{"href":813,"dataGaName":48,"dataGaLocation":814},"https://gitlab.com/-/trial_registrations/new?glm_content=default-saas-trial&glm_source=about.gitlab.com/","feature",{"text":503,"config":816},{"href":52,"dataGaName":53,"dataGaLocation":814},1776450002704]