[{"data":1,"prerenderedAt":806},["ShallowReactive",2],{"/en-us/blog/migration-from-azure-devops-to-gitlab":3,"navigation-en-us":41,"banner-en-us":440,"footer-en-us":450,"blog-post-authors-en-us-Evgeny Rudinsky|Michael Leopard":691,"blog-related-posts-en-us-migration-from-azure-devops-to-gitlab":717,"assessment-promotions-en-us":757,"next-steps-en-us":796},{"id":4,"title":5,"authorSlugs":6,"body":9,"categorySlug":10,"config":11,"content":15,"description":9,"extension":29,"isFeatured":12,"meta":30,"navigation":12,"path":31,"publishedDate":21,"seo":32,"stem":37,"tagSlugs":38,"__hash__":40},"blogPosts/en-us/blog/migration-from-azure-devops-to-gitlab.yml","Migration From Azure Devops To Gitlab",[7,8],"evgeny-rudinsky","michael-leopard",null,"engineering",{"featured":12,"template":13,"slug":14},true,"BlogPost","migration-from-azure-devops-to-gitlab",{"category":10,"tags":16,"body":20,"date":21,"updatedDate":22,"heroImage":23,"authors":24,"title":27,"description":28},[17,18,19],"tutorial","git","CI/CD","\nEnterprise teams are increasingly migrating from Azure DevOps to GitLab to gain strategic advantages and accelerate secure software delivery. \n\n\n- GitLab comes with integrated controls, policies, and [compliance frameworks](https://docs.gitlab.com/user/compliance/compliance_frameworks/) that allow organizations to implement software delivery standards at scale. This is especially important for regulated industries.\n\n- [Security testing](https://docs.gitlab.com/user/application_security/) is embedded in the pipeline and results show in the developer workflow, including static application security testing (SAST), source code analysis (SCA), dynamic application security testing (DAST), infrastructure-as-code scanning (IaC), container scanning, and API scanning.\n\n- [AI capabilities](https://about.gitlab.com/gitlab-duo-agent-platform/) across the full software delivery lifecycle include advanced agent orchestration and customizable flows to support how your organizational teams work.\n\n\nGitLab's open-source, open-core approach, flexible deployment options such as single-tenant dedicated and self-managed, and truly unified platform eliminate integration complexity and security gaps. \n\n\nFor teams facing mounting pressure to accelerate delivery while strengthening security posture and maintaining regulatory compliance, GitLab represents not just a migration but a platform evolution.\n\n\nMigrating from Azure DevOps to GitLab can seem like a daunting task, but with the right approach and tools, it can be a smooth and efficient process. This guide will walk you through the steps needed to successfully migrate your projects, repositories, and pipelines from Azure DevOps to GitLab.\n\n\n## Overview\n\nGitLab provides both [Congregate](https://gitlab.com/gitlab-org/professional-services-automation/tools/migration/congregate/) (maintained by [GitLab Professional Services](https://about.gitlab.com/professional-services/) organization) and [a built-in Git repository import](https://docs.gitlab.com/user/project/import/repo_by_url/) for migrating projects from Azure DevOps (ADO). These options support repository-by-repository or bulk migration and preserve git commit history, branches, and tags. With Congregate and professional services tools, we support additional assets such as wikis, work items, CI/CD variables, container images, packages, pipelines, and more (see this [feature matrix](https://gitlab.com/gitlab-org/professional-services-automation/tools/migration/congregate/-/blob/master/customer/ado-migration-features-matrix.md)). Use this guide to plan and execute your migration and complete post-migration follow-up tasks.\n\n\nEnterprises migrating from ADO to GitLab commonly follow a multi-phase approach:\n\n\n- Migrate repositories from ADO to GitLab using Congregate or GitLab's built-in repository migration.\n\n- Migrate pipelines from Azure Pipelines to GitLab CI/CD.\n\n- Migrate remaining assets such as boards, work items, and artifacts to GitLab Issues, Epics, and the Package and Container Registries.\n\n\nHigh-level migration phases:\n\n\n```mermaid\ngraph LR\n    subgraph Prerequisites\n        direction TB\n        A[\"Set up identity provider (IdP) and\u003Cbr/>provision users\"]\n        A --> B[\"Set up runners and\u003Cbr/>third-party integrations\"]\n        B --> I[\"Users enablement and\u003Cbr/>change management\"]\n    end\n    \n    subgraph MigrationPhase[\"Migration phase\"]\n        direction TB\n        C[\"Migrate source code\"]\n        C --> D[\"Preserve contributions and\u003Cbr/> format history\"]\n        D --> E[\"Migrate work items and\u003Cbr/>map to \u003Ca href=\"https://docs.gitlab.com/topics/plan_and_track/\">GitLab Plan \u003Cbr/>and track work\"]\n    end\n    \n    subgraph PostMigration[\"Post-migration steps\"]\n        direction TB\n        F[\"Create or translate \u003Cbr/>ADO pipelines to GitLab CI\"]\n        F --> G[\"Migrate other assets\u003Cbr/>packages and container images\"]\n        G --> H[\"Introduce \u003Ca href=\"https://docs.gitlab.com/user/application_security/secure_your_application/\">security\u003C/a> and\u003Cbr/>SDLC improvements\"]\n    end\n    \n    Prerequisites --> MigrationPhase\n    MigrationPhase --> PostMigration\n\n    style A fill:#FC6D26\n    style B fill:#FC6D26\n    style I fill:#FC6D26\n    style C fill:#8C929D\n    style D fill:#8C929D\n    style E fill:#8C929D\n    style F fill:#FFA500\n    style G fill:#FFA500\n    style H fill:#FFA500\n```\n\n\n## Planning your migration\n\n\n**To plan your migration, ask these questions:**\n\n\n- How soon do we need to complete the migration?\n\n- Do we understand what will be migrated?\n\n- Who will run the migration?\n\n- What organizational structure do we want in GitLab?\n\n- Are there any constraints, limitations, or pitfalls that need to be taken into account?\n\n\nDetermine your timeline, as it will largely dictate your migration approach. Identify champions or groups familiar with both ADO and GitLab platforms (such as early adopters) to help drive adoption and provide guidance.\n\n\n**Inventory what you need to migrate:**\n\n\n- The number of repositories, pull requests, and contributors\n\n- The number and complexity of work items and pipelines\n\n- Repository sizes and dependency relationships\n\n- Critical integrations and runner requirements (agent pools with specific capabilities)\n\n\nUse GitLab Professional Services's [Evaluate](https://gitlab.com/gitlab-org/professional-services-automation/tools/utilities/evaluate#beta-azure-devops) tool to produce a complete inventory of your entire Azure DevOps organization, including repositories, PR counts, contributor lists, number of pipelines, work items, CI/CD variables and more. If you're working with the GitLab Professional Services team, share this report with your engagement manager or technical architect to help plan the migration.\n\n\nMigration timing is primarily driven by pull request count, repository size, and amount of contributions (e.g. comments in PR, work items, etc). For example, 1,000 small repositories with few PRs and limited contributors can migrate much faster than a smaller set of repositories containing tens of thousands of PRs and thousands of contributors. Use your inventory data to estimate effort and plan test runs before proceeding with production migrations.\n\n\nCompare inventory against your desired timeline and decide whether to migrate all repositories at once or in batches. If teams cannot migrate simultaneously, batch and stagger migrations to align with team schedules. For example, in Professional Services engagements, we organize migrations into waves of 200-300 projects to manage complexity and respect API rate limits, both in [GitLab](https://docs.gitlab.com/security/rate_limits/) and [ADO](https://learn.microsoft.com/en-us/azure/devops/integrate/concepts/rate-limits?view=azure-devops).\n\n\nGitLab's built-in [repository importer](https://docs.gitlab.com/user/project/import/repo_by_url/) migrates Git repositories (commits, branches, and tags) one-by-one. Congregate is designed to preserve pull requests (known in GitLab as merge requests), comments, and related metadata where possible; the simple built-in repository import focuses only on the Git data (history, branches, and tags).\n\n\n**Items that typically require separate migration or manual recreation:**\n\n\n- Azure Pipelines - create equivalent GitLab CI/CD pipelines (consult with [CI/CD YAML](https://docs.gitlab.com/ci/yaml/) and/or with [CI/CD components](https://docs.gitlab.com/ci/components/)). Alternatively, consider using AI-based pipeline conversion available in Congregate.\n\n- Work items and boards - map to GitLab Issues, Epics, and Issue Boards.\n\n- Artifacts, container images (ACR) - migrate to GitLab Package Registry or Container Registry.\n\n- Service hooks and external integrations - recreate in GitLab.\n\n- [Permissions models](https://docs.gitlab.com/user/permissions/) differ between ADO and GitLab; review and plan permissions mapping rather than assuming exact preservation.\n\n\nReview what each tool (Congregate vs. built-in import) will migrate and choose the one that fits your needs. Make a list of any data or integrations that must be migrated or recreated manually.\n\n\n**Who will run the migration?**\n\n\nMigrations are typically run by a GitLab group owner or instance administrator, or by a designated migrator who has been granted the necessary permissions on the destination group/project. Congregate and the GitLab import APIs require valid authentication tokens for both Azure DevOps and GitLab.\n\n\n- Decide whether a group owner/admin will perform the migrations or whether you will grant a specific team/person delegated access.\n\n- Ensure the migrator has correctly configured personal access tokens (Azure DevOps and GitLab) with the scopes required by your chosen migration tool (for example, api/read_repository scopes and any tool-specific requirements). \n\n- Test tokens and permissions with a small pilot migration.\n\n**Note:** Congregate leverages file-based import functionality for ADO migrations and requires instance administrator permissions to run ([see our documentation](https://docs.gitlab.com/user/project/settings/import_export/#migrate-projects-by-uploading-an-export-file)). If you are migrating to GitLab.com, consider engaging Professional Services. For more information, see the [Professional Services Full Catalog](https://about.gitlab.com/professional-services/catalog/). Non-admin account cannot preserve contribution attribution!\n\n\n**What organizational structure do we want in GitLab?**\n\nWhile it's possible to map ADO structure directly to GitLab structure, it's recommended to rationalize and simplify the structure during migration. Consider how teams will work in GitLab and design the structure to facilitate collaboration and access management. Here is a way to think about mapping ADO structure to GitLab structure:\n\n\n```mermaid\ngraph TD\n    subgraph GitLab\n        direction TB\n        A[\"Top-level Group\"]\n        B[\"Subgroup (optional)\"]\n        C[\"Projects\"]\n        A --> B\n        A --> C\n        B --> C\n    end\n\n    subgraph AzureDevOps[\"Azure DevOps\"]\n        direction TB\n        F[\"Organizations\"]\n        G[\"Projects\"]\n        H[\"Repositories\"]\n        F --> G\n        G --> H\n    end\n\n    style A fill:#FC6D26\n    style B fill:#FC6D26\n    style C fill:#FC6D26\n    style F fill:#8C929D\n    style G fill:#8C929D\n    style H fill:#8C929D\n```\n\nRecommended approach:\n\n\n- Map each ADO organization to a GitLab group (or a small set of groups), not to many small groups. Avoid creating a GitLab group for every ADO team project. Use migration as an opportunity to rationalize your GitLab structure.\n\n- Use subgroups and project-level permissions to group related repositories.\n\n- Manage access to sets of projects by using GitLab groups and group membership (groups and subgroups) rather than one group per team project.\n\n- Review GitLab [permissions](https://docs.gitlab.com/ee/user/permissions.html) and consider [SAML Group Links](https://docs.gitlab.com/user/group/saml_sso/group_sync/) to implement an enterprise RBAC model for your GitLab instance (or a GitLab.com namespace).\n\n\n**ADO Boards and work items: State of migration**\n\n\nIt's important to understand how work items migrate from ADO into GitLab Plan (issues, epics, and boards).\n\n\n- ADO Boards and work items map to GitLab Issues, Epics, and Issue Boards. Plan how your workflows and board configurations will translate.\n\n- ADO Epics and Features become GitLab Epics.\n\n- Other work item types (e.g., user stories, tasks, bugs) become project-scoped issues.\n\n- Most standard fields are preserved; selected custom fields can be migrated when supported.\n\n- Parent-child relationships are retained so Epics reference all related issues.\n\n- Links to pull requests are converted to merge request links to maintain development traceability.\n\n\nExample: Migration of an individual work item to a GitLab Issue, including field accuracy and relationships:\n\n\n![Example: Migration of an individual work item to a GitLab Issue](https://res.cloudinary.com/about-gitlab-com/image/upload/v1764769188/ztesjnxxfbwmfmtckyga.png)\n\n\nBatching guidance:\n\n\n- If you need to run migrations in batches, use your new group/subgroup structure to define batches (for example, by ADO organization or by product area).\n\n- Use inventory reports to drive batch selection and test each batch with a pilot migration before scaling.\n\n\n**Pipelines migration**\n\n\nCongregate [recently introduced](https://gitlab.com/gitlab-org/professional-services-automation/tools/migration/congregate/-/merge_requests/1298) AI-powered conversion for multi-stage YAML pipelines from Azure DevOps to GitLab CI/CD. This automated conversion works best for simple, single-file pipelines and is designed to provide a working starting point rather than a production-ready `.gitlab-ci.yml` file. The tool generates a functionally equivalent GitLab pipeline that you can then refine and optimize for your specific needs.\n\n\n- Converts Azure Pipelines YAML to `.gitlab-ci.yml` format automatically.\n\n- Best suited for straightforward, single-file pipeline configurations.\n\n- Provides a boilerplate to accelerate migration, not a final production artifact.\n\n- Requires review and adjustment for complex scenarios, custom tasks, or enterprise requirements.\n\n- Does not support Azure DevOps classic release pipelines — [convert these to multi-stage YAML](https://learn.microsoft.com/en-us/azure/devops/pipelines/release/from-classic-pipelines?view=azure-devops) first.\n\n\nRepository owners should review the [GitLab CI/CD documentation](https://docs.gitlab.com/ci/) to further optimize and enhance their pipelines after the initial conversion.\n\n\nExample of converted pipelines:\n\n\n```yml \n\n# azure-pipelines.yml\n\ntrigger:\n  - main\n\nvariables:\n  imageName: myapp\n\nstages:\n  - stage: Build\n    jobs:\n      - job: Build\n        pool:\n          vmImage: 'ubuntu-latest'\n        steps:\n          - checkout: self\n\n          - task: Docker@2\n            displayName: Build Docker image\n            inputs:\n              command: build\n              repository: $(imageName)\n              Dockerfile: '**/Dockerfile'\n              tags: |\n                $(Build.BuildId)\n\n  - stage: Test\n    jobs:\n      - job: Test\n        pool:\n          vmImage: 'ubuntu-latest'\n        steps:\n          - checkout: self\n\n          # Example: run tests inside the container\n          - script: |\n              docker run --rm $(imageName):$(Build.BuildId) npm test\n            displayName: Run tests\n\n  - stage: Push\n    jobs:\n      - job: Push\n        pool:\n          vmImage: 'ubuntu-latest'\n        steps:\n          - checkout: self\n\n          - task: Docker@2\n            displayName: Login to ACR\n            inputs:\n              command: login\n              containerRegistry: '\u003Cyour-acr-service-connection>'\n\n          - task: Docker@2\n            displayName: Push image to ACR\n            inputs:\n              command: push\n              repository: $(imageName)\n              tags: |\n                $(Build.BuildId)\n\n```\n\n```yaml\n\n# .gitlab-ci.yml\n\nvariables:\n  imageName: myapp\n\nstages:\n  - build\n  - test\n  - push\n\nbuild:\n  stage: build\n  image: docker:latest\n  services:\n    - docker:dind\n  script:\n    - docker build -t $imageName:$CI_PIPELINE_ID -f $(find . -name Dockerfile) .\n  only:\n    - main\n\ntest:\n  stage: test\n  image: docker:latest\n  services:\n    - docker:dind\n  script:\n    - docker run --rm $imageName:$CI_PIPELINE_ID npm test\n  only:\n    - main\n\npush:\n  stage: push\n  image: docker:latest\n  services:\n    - docker:dind\n  before_script:\n    - docker login -u $CI_REGISTRY_USER -p $CI_REGISTRY_PASSWORD $CI_REGISTRY\n  script:\n    - docker tag $imageName:$CI_PIPELINE_ID $CI_REGISTRY/$CI_PROJECT_PATH/$imageName:$CI_PIPELINE_ID\n    - docker push $CI_REGISTRY/$CI_PROJECT_PATH/$imageName:$CI_PIPELINE_ID\n  only:\n    - main\n\n```\n\n**Final checklist:**\n\n\n- Decide timeline and batch strategy.\n\n- Produce a full inventory of repositories, PRs, and contributors.\n\n- Choose Congregate or the built-in import based on scope (PRs and metadata vs. Git data only).\n\n- Decide who will run migrations and ensure tokens/permissions are configured.\n\n- Identify assets that must be migrated separately (pipelines, work items, artifacts, and hooks) and plan those efforts.\n\n- Run pilot migrations, validate results, then scale according to your plan.\n\n\n## Running your migrations\n\n\nAfter planning, execute migrations in stages, starting with trial runs. Trial migrations help surface org-specific issues early and let you measure duration, validate outcomes, and fine-tune your approach before production.\n\n\nWhat trial migrations validate:\n\n\n- Whether a given repository and related assets migrate successfully (history, branches, tags; plus MRs/comments if using Congregate)\n\n- Whether the destination is usable immediately (permissions, runners, CI/CD variables, integrations)\n\n- How long each batch takes, to set schedules and stakeholder expectations\n\n\nDowntime guidance:\n\n\n- GitLab's built-in Git import and Congregate do not inherently require downtime.\n\n- For production waves, freeze changes in ADO (branch protections or read-only) to avoid missed commits, PR updates, or work items created mid-migration.\n\n- Trial runs do not require freezes and can be run anytime.\n\n\nBatching guidance:\n\n\n- Run trial batches back-to-back to shorten elapsed time; let teams validate results asynchronously.\n\n- Use your planned group/subgroup structure to define batches and respect API rate limits.\n\n\nRecommended steps:\n\n\n1. Create a test destination in GitLab for trials:\n\n\n  - GitLab.com: create a dedicated group/namespace (for example, my-org-sandbox)\n\n  - Self-managed: create a top-level group or a separate test instance if needed\n\n\n2. Prepare authentication:\n\n\n  - Azure DevOps PAT with required scopes.\n\n  - GitLab Personal Access Token with api and read_repository (plus admin access for file-based imports used by Congregate).\n\n\n3. Run trial migrations:\n\n\n  - Repos only: use GitLab's built-in import (Repo by URL)\n\n  - Repos + PRs/MRs and additional assets: use Congregate\n\n\n4. Post-trial follow-up:\n\n\n  - Verify repo history, branches, tags; merge requests (if migrated), issues/epics (if migrated), labels, and relationships.\n\n  - Check permissions/roles, protected branches, required approvals, runners/tags, variables/secrets, integrations/webhooks.\n\n  - Validate pipelines (`.gitlab-ci.yml`) or converted pipelines where applicable.\n\n\n5. Ask users to validate functionality and data fidelity.\n\n6. Resolve issues uncovered during trials and update your runbooks.\n\n7. Network and security:\n\n\n  - If your destination uses IP allow lists, add the IPs of your migration host and any required runners/integrations so imports can succeed.\n\n\n8. Run production migrations in waves:\n\n\n  - Enforce change freezes in ADO during each wave.\n\n  - Monitor progress and logs; retry or adjust batch sizes if you hit rate limits.\n\n\n9. Optional: remove the sandbox group or archive it after you finish.\n\n\n\u003Cfigure class=\"video_container\">\n  \u003Ciframe src=\"https://www.youtube.com/embed/ibIXGfrVbi4?si=ZxOVnXjCF-h4Ne0N\" frameborder=\"0\" allowfullscreen=\"true\">\u003C/iframe>\n\u003C/figure>\n\n\n## Terminology reference for GitLab and Azure DevOps\n\n| GitLab                                                           | Azure DevOps                                 | Similarities & Key Differences                                                                                                                                          |\n| ---------------------------------------------------------------- | -------------------------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------- |\n| Group                                                            | Organization                                 | Top-level namespace, membership, policies. ADO org contains Projects; GitLab Group contains Subgroups and Projects.                                                   |\n| Group or Subgroup                                                | Project                                      | Logical container, permissions boundary. ADO Project holds many repos; GitLab Groups/Subgroups organize many Projects.                                                |\n| Project (includes a Git repo)                                    | Repository (inside a Project)                | Git history, branches, tags. In GitLab, a \"Project\" is the repo plus issues, CI/CD, wiki, etc. One repo per Project.                                                  |\n| Merge Request (MR)                                               | Pull Request (PR)                            | Code review, discussions, approvals. MR rules include approvals, required pipelines, code owners.                                                                     |\n| Protected Branches, MR Approval Rules, Status Checks             | Branch Policies                              | Enforce reviews and checks. GitLab combines protections + approval rules + required status checks.                                                                    |\n| GitLab CI/CD                                                     | Azure Pipelines                              | YAML pipelines, stages/jobs, logs. ADO also has classic UI pipelines; GitLab centers on .gitlab-ci.yml.                                                               |\n| .gitlab-ci.yml                                                   | azure-pipelines.yml                          | Defines stages/jobs/triggers. Syntax/features differ; map jobs, variables, artifacts, and triggers.                                                                   |\n| Runners (shared/specific)                                        | Agents / Agent Pools                         | Execute jobs on machines/containers. Target via demands (ADO) vs tags (GitLab). Registration/scoping differs.                                                         |\n| CI/CD Variables (project/group/instance), Protected/Masked       | Pipeline Variables, Variable Groups, Library | Pass config/secrets to jobs. GitLab supports group inheritance and masking/protection flags.                                                                          |\n| Integrations, CI/CD Variables, Deploy Keys                       | Service Connections                          | External auth to services/clouds. Map to integrations or variables; cloud-specific helpers available.                                                                 |\n| Environments & Deployments (protected envs)                      | Environments (with approvals)                | Track deploy targets/history. Approvals via protected envs and manual jobs in GitLab.                                                                                 |\n| Releases (tag + notes)                                           | Releases (classic or pipelines)              | Versioned notes/artifacts. GitLab Release ties to tags; deployments tracked separately.                                                                               |\n| Job Artifacts                                                    | Pipeline Artifacts                           | Persist job outputs. Retention/expiry configured per job or project.                                                                                                  |\n| Package Registry (NuGet/npm/Maven/PyPI/Composer, etc.)           | Azure Artifacts (NuGet/npm/Maven, etc.)      | Package hosting. Auth/namespace differ; migrate per package type.                                                                                                     |\n| GitLab Container Registry                                        | Azure Container Registry (ACR) or others     | OCI images. GitLab provides per-project/group registries.                                                                                                             |\n| Issue Boards                                                     | Boards                                       | Visualize work by columns. GitLab boards are label-driven; multiple boards per project/group.                                                                         |\n| Issues (types/labels), Epics                                     | Work Items (User Story/Bug/Task)             | Track units of work. Map ADO types/fields to labels/custom fields; epics at group level.                                                                              |\n| Epics, Parent/Child Issues                                       | Epics/Features                               | Hierarchy of work. Schema differs; use epics + issue relationships.                                                                                                   |\n| Milestones and Iterations                                        | Iteration Paths                              | Time-boxing. GitLab Iterations (group feature) or Milestones per project/group.                                                                                       |\n| Labels (scoped labels)                                           | Area Paths                                   | Categorization/ownership. Replace hierarchical areas with scoped labels.                                                                                              |\n| Project/Group Wiki                                               | Project Wiki                                 | Markdown wiki. Backed by repos in both; layout/auth differ slightly.                                                                                                  |\n| Test reports via CI, Requirements/Test Management, integrations  | Test Plans/Cases/Runs                        | QA evidence/traceability. No 1:1 with ADO Test Plans; often use CI reports + issues/requirements.                                                                     |\n| Roles (Owner/Maintainer/Developer/Reporter/Guest) + custom roles | Access levels + granular permissions         | Control read/write/admin. Models differ; leverage group inheritance and protected resources.                                                                          |\n| Webhooks                                                         | Service Hooks                                | Event-driven integrations. Event names/payloads differ; reconfigure endpoints.                                                                                        |\n| Advanced Search                                                  | Code Search                                  | Full-text repo search. 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But sometimes, you have to step away from the Zoom calls and stand in a crowded university hall to remember why we do this.",[723],"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",[262,613,727],"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/) 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":730,"featured":731,"template":13},"how-iit-bombay-students-code-future-with-gitlab",false,{"content":733,"config":742},{"title":734,"description":735,"authors":736,"heroImage":737,"date":738,"category":10,"tags":739,"body":741},"Artois University elevates research and curriculum with GitLab Ultimate for Education","Artois University's CRIL leveraged the GitLab for Education program to gain free access to Ultimate, transforming advanced research and computer science curricula.",[723],"https://res.cloudinary.com/about-gitlab-com/image/upload/v1750099203/Blog/Hero%20Images/Blog/Hero%20Images/blog-image-template-1800x945%20%2820%29_2bJGC5ZP3WheoqzlLT05C5_1750099203484.png","2025-12-10",[613,262,740],"product","Leading academic institutions face a critical challenge: how to provide thousands of students and researchers with industry-standard, **full-featured DevSecOps tools** without compromising institutional control. Many start with basic version control, but the modern curriculum demands integrated capabilities for planning, security, and advanced CI/CD.\n\nThe **GitLab for Education program** is designed to solve this by providing access to **GitLab Ultimate** for qualifying institutions, allowing them to scale their operations and elevate their academic offerings. \n\nThis article showcases a powerful success story from the **Centre de Recherche en Informatique de Lens (CRIL)**, a joint laboratory of **Artois University** and CNRS in France. After years of relying solely on GitLab Community Edition (CE), the university's move to GitLab Ultimate through the GitLab for Education program immediately unlocked advanced capabilities, transforming their teaching, research, and contribution workflows virtually overnight. This story demonstrates why GitLab Ultimate is essential for institutions seeking to deliver advanced computer science and research curricula.\n\n## GitLab Ultimate unlocked: Managing scale and driving academic value\n\n**Artois University's** self-managed GitLab instance is a large-scale operation, supporting nearly **3,000 users** across approximately **19,000 projects**, primarily serving computer science students and researchers. While GitLab Community Edition was robust, the upgrade to GitLab Ultimate provided the sophisticated tooling necessary for managing this scale and facilitating advanced university-level work.\n\n***\"We can see the difference,\" says Daniel Le Berre, head of research at CRIL and the instance maintainer. \"It's a completely different product. Each week reveals new features that directly enhance our productivity and teaching.\"***\n\nThe institution joined the GitLab for Education program specifically because it covers both **instructional and non-commercial research use cases** and offers full access to Ultimate's features, removing significant cost barriers.\n\n### Key GitLab Ultimate benefits for students and researchers\n\n* **Advanced project management at scale:** Master's students now benefit from **GitLab Ultimate's project planning features**. This enables them to structure, track, and manage complex, long-term research projects using professional methodologies like portfolio management and advanced issue tracking that seamlessly roll up across their thousands of projects.\n\n* **Enhanced visibility:** Features like improved dashboards and code previews directly in Markdown files dramatically streamline tracking and documentation review, reducing administrative friction for both instructors and students managing large project loads.\n\n## Comprehensive curriculum: From concepts to continuous delivery\n\nGitLab Ultimate is deeply integrated into the computer science curriculum, moving students beyond simple `git` commands to practical **DevSecOps implementation**.\n\n* **Git fundamentals:** Students begin by visualizing concepts using open-source tools to master Git concepts.\n\n* **Full CI/CD implementation:** Students use GitLab CI for rigorous **Test-Driven Development (TDD)** in their software projects. They learn to build, test, and perform quality assurance using unit and integration testing pipelines—core competency made seamless by the integrated platform.\n\n* **DevSecOps for research and documentation:** The university teaches students that DevSecOps principles are vital for all collaborative work. Inspired by earlier work in Delft, students manage and produce critical research documentation (PDFs from Markdown files) using GitLab, incorporating quality checks like linters and spell checks directly in the CI pipeline. This ensures high-quality, reproducible research output.\n\n* **Future-proofing security skills:** The GitLab Ultimate platform immediately positions the institution to incorporate advanced DevSecOps features like SAST and DAST scanning as their research and development code projects grow, ensuring students are prepared for industry security standards.\n\n## Accelerating open source contributions with GitLab Duo\n\nAccess to the full GitLab platform, including our AI capabilities, has empowered students to make impactful contributions to the wider open source community faster than ever before.\n\nTwo Master's students recently completed direct contributions to the GitLab product, adding the **ORCID identifier** into user profiles. Working on GitLab.com, they leveraged **GitLab Duo's AI chat and code suggestions** to navigate the codebase efficiently.\n\n***\"This would not have been possible without GitLab Duo,\" Daniel Le Berre notes. \"The AI features helped students, who might have lacked deep codebase knowledge, deliver meaningful contributions in just two weeks.\"***\n\nThis demonstrates how providing students with cutting-edge tools **accelerates their learning and impact**, allowing them to translate classroom knowledge into real-world contributions immediately.\n\n## Empowering open research and institutional control\n\nThe stability of the self-managed instance at Artois University is key to its success. This model guarantees **institutional control and stability** — a critical factor for long-term research preservation.\n\nThe institution's expertise in this area was recently highlighted in a major 2024 study led by CRIL, titled: \"[Higher Education and Research Forges in France - Definition, uses, limitations encountered and needs analysis](https://hal.science/hal-04208924v4)\" ([Project on GitLab](https://gitlab.in2p3.fr/coso-college-codes-sources-et-logiciels/forges-esr-en)). The research found that the vast majority of public forges in French Higher Education and Research relied on **GitLab**. This finding underscores the consensus among academic leaders that self-hosted solutions are essential for **data control and longevity**, especially when compared to relying on external, commercial forges.\n\n## Unlock GitLab Ultimate for your institution today\n\nThe success story of **Artois University's CRIL** proves the transformative power of the GitLab for Education program. By providing **free access to GitLab Ultimate**, we enable large-scale institutions to:\n\n1.  **Deliver a modern, integrated DevSecOps curriculum.**\n\n2.  **Support advanced, collaborative research projects with Ultimate planning features.**\n\n3.  **Empower students to make AI-assisted open source contributions.**\n\n4.  **Maintain institutional control and data longevity.**\n\nIf your academic institution is ready to equip its students and researchers with the complete DevSecOps platform and its most advanced features, we invite you to join the program.\n\nThe program provides **free access to GitLab Ultimate** for qualifying instructional and non-commercial research use cases.\n\n**Apply now [online](https://about.gitlab.com/solutions/education/join/).**\n",{"slug":743,"featured":12,"template":13},"artois-university-elevates-curriculum-with-gitlab-ultimate-for-education",{"content":745,"config":755},{"heroImage":746,"title":747,"description":748,"authors":749,"date":751,"category":10,"tags":752,"body":754},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1764108112/tyntnsy3xotlmehtnfkb.png","How we deploy the largest GitLab instance 12 times daily","Take a deep technical dive into GitLab.com's deployment pipeline, including progressive rollouts, Canary strategies, database migrations, and multiversion compatibility.",[750],"John Skarbek","2025-12-01",[740,753],"inside GitLab","Every day, GitLab deploys code changes to the world's largest GitLab instance — GitLab.com  — up to 12 times without any downtime. We use GitLab's own CI/CD platform to manage these deployments, which impact millions of developers worldwide. This deployment frequency serves as our primary quality gate and stress test. It also means our customers get access to new features within hours of development rather than waiting weeks or months. When organizations depend on GitLab for their DevOps workflows, they're using a platform that's proven at scale on our own infrastructure. In this article, you'll learn how we built an automated deployment pipeline using core GitLab CI/CD functionality to handle this deployment complexity.\n\n\n## The business case for deployment velocity\n\n\nFor GitLab: Our deployment frequency isn't just an engineering metric — it's a business imperative. Rapid deployment cycles mean we can respond to customer feedback within hours, ship security patches immediately, and validate new features in production before scaling them.\n\n\nFor our customers: Every deployment to GitLab.com validates the deployment practices we recommend to our users. When you use GitLab's deployment features, you're using the same battle-tested approach that handles millions of git operations, CI/CD pipelines, and user interactions daily. You benefit from:\n\n- Latest features available immediately: New capabilities reach you within hours of completion, not in quarterly release cycles\n- Proven reliability at scale: If a feature works on GitLab.com, you can trust it in your environment\n- Full value of GitLab: Zero-downtime deployments mean you never lose access to your DevOps platform, even during updates\n- Real-world tested practices: Our deployment documentation isn't theory — it's exactly how we run the largest GitLab instance in existence\n\n\n## Code flow architecture\n\n\nOur deployment pipeline follows a structured progression through multiple stages, each acting as a checkpoint on the journey from code proposal to production deployment.\n\n```mermaid\n  graph TD\n      A[Code Proposed] --> B[Merge Request Created]\n      B --> C[Pipeline Triggered]\n      C --> D[Build & Test]\n      D --> E{Spec/Integration/QA Tests Pass?}\n      E -->|No| F[Feedback Loop]\n      F --> B\n      E -->|Yes| G[Merge to default branch]\n      G -->|Periodically| H[Auto-Deploy Branch]\n\n      subgraph \"Deployment Pipeline\"\n          H --> I[Package Creation]\n          I --> K[Canary Environment]\n          K --> L[QA Validation]\n          L --> M[Main Environment]\n\n      end\n```\n\n\n## Deployment pipeline makeup\n\nOur deployment approach uses GitLab's native CI/CD capabilities to orchestrate complex deployments across hybrid infrastructure.\nHere's how we do it.\n\n\n### Build\n\n\nBuilding GitLab is a complex topic in and of itself, so I'll go over the details at a high level.\n\nWe build both our Omnibus package and our Cloud Native GitLab (CNG) images. The Omnibus packages deploy to our Gitaly fleet (our Git storage layer), while CNG images run all other components as containerized workloads. Other stateful services like Postgres and Redis have grown so large we have dedicated teams managing them separately. For GitLab.com, those systems are not deployed during our Auto-Deploy procedures.\n\n\nWe have a scheduled pipeline that will regularly look at `gitlab-org/gitlab` and search for the most recent commit on the default branch with a successful (“green”) pipeline. Green pipelines signal that every component of GitLab has passed its comprehensive test suite. We then create an **auto-deploy branch** from that commit.\n\n\nThis triggers a sequence of events: primarily, the need to build this package and all components that are a part of our monolith.\nAnother scheduled pipeline selects the latest built package and initiates the deployment pipeline. Procedurally, it looks this simple:\n\n\n```mermaid\n  graph LR\n      A[Create branch] --> B[Build]\n      B --> C[Choose Built package]\n      C --> D[Start Deploy Pipeline]\n```\n\n\nBuilding takes some time, and since deployments can vary due to various circumstances, we choose the latest build to deploy. We technically build more versions of GitLab for .com than will ever be deployed. This enables us to always have a package lined up ready to go, and this brings us the closest we can be to having a full continuously delivered product for .com.\n\n\n### Environment-based validation and Canary strategy\n\nQuality assurance (QA) isn't just an afterthought here — it's baked into every layer from development through deployment. Our QA process leverages automated test suites that include unit tests, integration tests, and end-to-end tests that simulate real user interactions with GitLab's features. But more importantly for our deployment pipeline, our QA process works hand-in-hand with our Canary strategy through environment-based validation.\n\n\nAs part of our validation approach, we leverage GitLab's native [Canary deployments](https://docs.gitlab.com/user/project/canary_deployments/), enabling controlled validation of changes with limited traffic exposure before full production deployment. [We send roughly 5% of all traffic through our Canary stage](https://handbook.gitlab.com/handbook/engineering/infrastructure/environments/canary-stage/#environments-canary-stage). This approach increases the complexity of database migrations, but successfully navigating Canary deployments ensures we deploy a reliable product seamlessly.\n\nThe Canary deployment features you use in GitLab were refined through managing one of the most complex deployment scenarios in production. When you implement Canary deployments for your applications, you're using patterns proven at massive scale.\n\nOur deployment process follows a progressive rollout strategy:\n\n1. **Staging Canary:** Initial validation environment\n\n2. **Production Canary:** Limited production traffic\n\n3. **Staging Main:** Full staging environment deployment\n\n4. **Production Main:** Full production rollout\n\n```mermaid\n  graph TD\n      C[Staging Canary Deploy]\n      C --> D[QA Smoke Main Stage Tests]\n      C --> E[QA Smoke Canary Stage Tests]\n      D --> F\n      E --> F{Tests Pass?}\n      F -->|Yes| G[Production Canary Deploy]\n      G --> S[QA Smoke Main Stage Tests]\n      G --> T[QA Smoke Canary Stage Tests]\n      F -->|No| H[Issue Creation]\n      H --> K[Fix & Backport]\n      K --> C\n\n      S --> M[Canary Traffic Monitoring]\n      T --> M[Canary Traffic Monitoring baking period]\n      M --> U[Production Safety Checks]\n      U --> N[Staging Main]\n      N --> V[Production Main]\n```\n\nOur QA validation occurs at multiple checkpoints throughout this progressive deployment process: after each Canary deployment, and again after post-deploy migrations. This multilayered approach ensures that each phase of our deployment strategy has its own safety net. You can learn more about [GitLab's comprehensive testing approach](https://handbook.gitlab.com/handbook/engineering/testing/) in our handbook.\n\n## Deployment pipeline\n\nHere are the challenges we address across our deployment pipeline.\n\n### Technical architecture considerations\n\n GitLab.com represents real-world deployment complexity at scale. As the largest known GitLab instance, deployments use our official GitLab Helm chart and the official Linux package — the same artifacts our customers use. You can learn more about [the GitLab.com architecture](https://handbook.gitlab.com/handbook/engineering/infrastructure/production/architecture/#gitlab-com-architecture) in our handbook. This hybrid approach means our deployment pipeline must intelligently handle both containerized services and traditional Linux services in the same deployment cycle.\n\n **Dogfooding at scale:** We deploy using the same procedures we document for [zero-downtime upgrades](https://docs.gitlab.com/update/zero_downtime/). If something doesn't work smoothly for us, we don't recommend it to customers. This self-imposed constraint drives continuous improvement in our deployment tooling.\n\n The following stages are run for all environment and stage upgrades:\n\n```mermaid\n  graph LR\n      a[prep] --> c[Regular Migrations - Canary stage only]\n      a --> f[Assets - Canary stage only]\n      c --> d[Gitaly]\n      d --> k8s\n\n      subgraph subGraph0[\"VM workloads\"]\n        d[\"Gitaly\"]\n      end\n\n      subgraph subGraph1[\"Kubernetes workloads\"]\n        k8s[\"k8s\"]\n      end\n\n      subgraph fleet[\"fleet\"]\n        subGraph0\n        subGraph1\n      end\n```\n\n\n**Stage details:**\n\n\n- **Prep:** Validates deployment readiness and performs pre-deployment checks\n\n- **Migrations:** Executes database regular migrations. This only happens during the Canary stage. Because both Canary and Main stages share the same database, these changes are already available when the Main stage deploys, eliminating the need to repeat these tasks.\n\n- **Assets:** We leverage a GCS bucket for all static assets. If any new assets are created, we upload these to our bucket such that they are immediately available to our Canary stage. As we leverage WebPack for assets, and properly leverage SHAs in the naming of our assets, we can confidently not worry that we override an older asset. Therefore, old assets continue to be available for older deployments and new assets are imemdiately made available when Canary begins its deploy. This only happens during the Canary stage deployment. Because Canary and Main stages share the same asset storage, these changes are already available when the Main stage deploys.\n\n- **Gitaly:** Updates Gitaly Virtual Machine storage layer via our Omnibus Linux package on each Gitaly node. This service is unique as we [bundle it with `git`](https://gitlab.com/gitlab-org/gitaly/-/blob/master/doc/git-execution-environments.md). Therefore, we need to ensure that this service is capable of atomic upgrades. We leverage a [wrapper around Gitaly](https://gitlab.com/gitlab-org/gitaly/-/tree/master/cmd/gitaly-wrapper), which enables us to install a newer version of Gitaly and make use of the library [`tableflip`](https://github.com/cloudflare/tableflip) to cleanly rotate the running Gitaly, ensuring high availability of this service on each of our instances.\n\n- **Kubernetes:** Deploys containerized GitLab components via our Helm chart. Note that we deploy to numerous clusters spread across Zones for redundancy, so these are usually broken into their own stages to minimize harm and sometimes allows us to stop mid-deploy if critical issues are detected.\n\n\n### Multi-version compatibility: The hidden challenge\n\n\nAs you read our process, you will notice that there's a period of time where our database schema is ahead of the code that the Main stage knows about. This happens because the Canary stage has already deployed new code and runs regular database migrations, but the Main stage is still running the previous version of the code that doesn't know about these new database changes.\n\n**Real-world example:** Imagine we're adding a new `merge_readiness` field to merge requests. During deployment, some servers are running code that expects this field. while others don't know it exists yet. If we handle this poorly, we break GitLab.com for millions of users. If we handle it well, nobody notices anything happened.\n\nThis occurs with most other services, as well. For example, if a client sends multiple requests, there's a chance one of them might land in our Canary stage; other requests might be directed to the Main stage. This is not too different from a deploy as it does take a decent amount of time to roll through the few thousand Pods that run our services.\n\n\nWith a few exceptions, the vast majority of our services will run a slightly newer version of that component in Canary for a period of time. In a sense, these scenarios are all transient states. But they can often persist for several hours or days in a live, production environment. Therefore, we must treat them with the same care as permanent states. During any deployment, we have multiple versions of GitLab running simultaneously and they all need to play nicely together.\n\n## Database operations\n\nDatabase migrations present a unique challenge in our Canary deployment model. We need schema changes to support new features while maintaining our ability to roll back if issues arise. Our solution involves careful separation of concerns:\n\n- **Regular migrations:** Run during the Canary stage, designed to be backward-compatible, consists of only reversible changes\n\n- **Post-deploy migrations:** The \"point of no return\" migrations that happen only after multiple successful deployments\n\n\nDatabase changes are handled with precision and extensive validation procedures:\n\n\n```mermaid\n  graph LR\n      A[Regular Migrations] --> B[Canary Stage Deploy]\n      B --> C[Main Stage Deploy]\n      C --> D[Post Deploy Migrations]\n\n```\n\n### Post-deploy migrations\n\n\nGitLab deployments involve many components. Updating GitLab is not atomic, so many components must be backward-compatible.\n\n\nPost-deploy migrations often contain changes that can't be easily rolled back — think data transformations, column drops, or structural changes that would break older code versions. By running them _after_ we've gained confidence through multiple successful deployments, we ensure:\n\n\n1. **The new code is stable** and we're unlikely to need a rollback\n\n2. **Performance characteristics** are well understood in production\n\n3. **Any edge cases** have been discovered and addressed\n\n4. **The blast radius** is minimized if something does go wrong\n\n\nThis approach provides the optimal balance: enabling rapid feature deployment through Canary releases while maintaining rollback capabilities until we have high confidence in deployment stability.\n\n\n**The expand-migrate-contract pattern:** Our database, frontend, and application compatibility changes follow a carefully orchestrated three-phase approach.\n\n\n1. **Expand:** Add new structures (columns, indexes) while keeping old ones functional\n\n2. **Migrate:** Deploy new application code that uses the new structures\n\n3. **Contract:** Remove old structures in post-deploy migrations after everything is stable\n\n**Real-world example:** When adding a new `merge_readiness` column to merge requests:\n\n1. **Expand:** Add the new column with a default value; existing code ignores it\n\n2. **Migrate:** Deploy code that reads and writes to the new column while still supporting the old approach\n\n3 **Contract:** After several successful deployments, remove the old column in a post-deploy migration\n\nAll database operations, application code, frontend code, and more, are subject to a set of guidelines that Engineering must adhere to, which can be found in our [Multi-Version Compatibility documentation](https://docs.gitlab.com/development/multi_version_compatibility/).\n\n\n## Results and impact\n\nOur deployment infrastructure delivers measurable benefits:\n\n**For GitLab**\n\n* Up to 12 deployments daily to GitLab.com\n* Zero-downtime deployments serving millions of developers\n* Security patches can reach production within hours, not days\n* New features validated in production at massive scale before general availability\n\n**For customers**\n\n* Proven deployment patterns you can adopt for your own applications\n* Features battle-tested on the world's largest GitLab instance before reaching your environment\n* Documentation that reflects actual production practices, not theoretical best practices\n* Confidence that GitLab's recommended upgrade procedures work at any scale\n\n## Key takeaways for engineering teams\n\nGitLab's deployment pipeline represents a sophisticated system that balances deployment velocity with operational reliability. The progressive deployment model, comprehensive testing integration, and robust rollback capabilities provide a foundation for reliable software delivery at scale.\n\n\nFor engineering teams implementing similar systems, key considerations include:\n\n\n- **Automated testing:** Comprehensive test coverage throughout the deployment pipeline\n\n- **Progressive rollout:** Staged deployments to minimize risk and enable rapid recovery\n\n- **Monitoring integration:** Comprehensive observability across all deployment stages\n\n- **Incident response:** Rapid detection and resolution capabilities for deployment issues\n\n\nGitLab's architecture demonstrates how modern CI/CD systems can manage the complexity of large-scale deployments while maintaining the velocity required for competitive software development.\n\n\n## Important note on scope\n\n\nThis article specifically covers the deployment pipeline for services that are part of the **GitLab Omnibus package** and **Helm chart** — essentially the core GitLab monolith and its tightly integrated components.\n\n\nHowever, GitLab's infrastructure landscape extends beyond what's described here. Other services, notably our **AI services** and services that might be in a **proof of concept state**, follow a different deployment approach using our internal platform called Runway.\n\n\nIf you're working with or curious about these other services, you can find more information in the [Runway documentation](https://docs.runway.gitlab.com).\n\n\nOther offerings, such as GitLab Dedicated are deployed more in alignment with what we expect customers to be capable of performing themselves by way of the **GitLab Environment Toolkit**. If you'd like to learn more, check out the [GitLab Environment Toolkit project](https://gitlab.com/gitlab-org/gitlab-environment-toolkit).\n\n\nThe deployment strategies, architectural considerations, and pipeline complexities outlined in this article represent the battle-tested approach we use for our core platform — but like any large engineering organization, we have multiple deployment strategies tailored to different service types and maturity levels.\n\nFurther documentation about Auto-Deploy and our procedures can be found at the below links:\n  - [Engineering Deployments](https://handbook.gitlab.com/handbook/engineering/deployments-and-releases/deployments/)\n  - [Release Procedural Documentation](https://gitlab-org.gitlab.io/release/docs/)\n\n## More resources\n\n- [How we decreased GitLab repo backup times from 48 hours to 41 minutes](https://about.gitlab.com/blog/how-we-decreased-gitlab-repo-backup-times-from-48-hours-to-41-minutes/)\n\n- [How we supercharged GitLab CI statuses with WebSockets](https://about.gitlab.com/blog/how-we-supercharged-gitlab-ci-statuses-with-websockets/)\n\n- [How we reduced MR review time with Value Stream Management](https://about.gitlab.com/blog/how-we-reduced-mr-review-time-with-value-stream-management/)\n",{"featured":12,"template":13,"slug":756},"continuously-deploying-the-largest-gitlab-instance",{"promotions":758},[759,773,784],{"id":760,"categories":761,"header":763,"text":764,"button":765,"image":770},"ai-modernization",[762],"ai-ml","Is AI achieving its promise at scale?","Quiz will take 5 minutes or less",{"text":766,"config":767},"Get your AI maturity score",{"href":768,"dataGaName":769,"dataGaLocation":244},"/assessments/ai-modernization-assessment/","modernization assessment",{"config":771},{"src":772},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/qix0m7kwnd8x2fh1zq49.png",{"id":774,"categories":775,"header":776,"text":764,"button":777,"image":781},"devops-modernization",[740,559],"Are you just managing tools or shipping innovation?",{"text":778,"config":779},"Get your DevOps maturity score",{"href":780,"dataGaName":769,"dataGaLocation":244},"/assessments/devops-modernization-assessment/",{"config":782},{"src":783},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138785/eg818fmakweyuznttgid.png",{"id":785,"categories":786,"header":788,"text":764,"button":789,"image":793},"security-modernization",[787],"security","Are you trading speed for security?",{"text":790,"config":791},"Get your security maturity score",{"href":792,"dataGaName":769,"dataGaLocation":244},"/assessments/security-modernization-assessment/",{"config":794},{"src":795},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/p4pbqd9nnjejg5ds6mdk.png",{"header":797,"blurb":798,"button":799,"secondaryButton":804},"Start building faster today","See what your team can do with the intelligent orchestration platform for DevSecOps.\n",{"text":800,"config":801},"Get your free trial",{"href":802,"dataGaName":52,"dataGaLocation":803},"https://gitlab.com/-/trial_registrations/new?glm_content=default-saas-trial&glm_source=about.gitlab.com/","feature",{"text":496,"config":805},{"href":56,"dataGaName":57,"dataGaLocation":803},1772652073579]