[{"data":1,"prerenderedAt":793},["ShallowReactive",2],{"/en-us/blog/autoscale-continuous-deployment-gitlab-runner-digital-ocean":3,"navigation-en-us":37,"banner-en-us":436,"footer-en-us":446,"blog-post-authors-en-us-Owen Williams":688,"blog-related-posts-en-us-autoscale-continuous-deployment-gitlab-runner-digital-ocean":702,"assessment-promotions-en-us":744,"next-steps-en-us":783},{"id":4,"title":5,"authorSlugs":6,"body":8,"categorySlug":9,"config":10,"content":14,"description":8,"extension":25,"isFeatured":12,"meta":26,"navigation":27,"path":28,"publishedDate":20,"seo":29,"stem":33,"tagSlugs":34,"__hash__":36},"blogPosts/en-us/blog/autoscale-continuous-deployment-gitlab-runner-digital-ocean.yml","Autoscale Continuous Deployment Gitlab Runner Digital Ocean",[7],"owen-williams",null,"engineering",{"slug":11,"featured":12,"template":13},"autoscale-continuous-deployment-gitlab-runner-digital-ocean",false,"BlogPost",{"title":15,"description":16,"authors":17,"heroImage":19,"date":20,"body":21,"category":9,"tags":22},"How to autoscale continuous deployment with GitLab Runner on DigitalOcean","Our friends over at DigitalOcean share how to configure a highly scalable, responsive and cost-effective GitLab infrastructure.",[18],"Owen Williams","https://res.cloudinary.com/about-gitlab-com/image/upload/v1749680042/Blog/Hero%20Images/gitlab-digitalocean-cover.jpg","2018-06-19","[GitLab CI/CD](/solutions/continuous-integration/) is an effective way to build the habit of testing all code before it’s deployed. GitLab CI/CD is also highly scalable thanks to an additional tool, GitLab Runner, which automates scaling your build queue in order to avoid long wait times for development teams trying to release code.\n\nIn this guide, we will demonstrate how to configure a highly scalable GitLab infrastructure that manages its own costs, and automatically responds to load by increasing and decreasing available server capacity.\n\n## Goals\n\nWe’re going to build a scalable CI/CD process on DigitalOcean that automatically responds to demand by creating new servers on the platform and destroys them when the queue is empty.\n\nThese reusable servers are spawned by the GitLab Runner process and are automatically deleted when no jobs are running, reducing costs and administration overhead for your team.\n\nAs we’ll explain in this tutorial, you are in control of how many machines are created at any given time, as well as the length of time they’re retained before being destroyed.\n\nWe’ll be using three separate servers to build this project, so let’s go over terminology first:\n\n* **GitLab**: Your hosted GitLab instance or self-managed instance where your code repositories are stored.\n\n* **GitLab Bastion**: The *bastion* server or Droplet is the core of what we’ll be configuring. It is the control instance that is used to interact with the DigitalOcean API to create Droplets and destroy them when necessary. No jobs are executed on this server.\n\n* **GitLab Runner**: Your *runners* are transient servers or Droplets that are created on the fly by the *bastion* server when needed to execute a CI/CD job in your build queue. These servers are disposable, and are where your code is executed or tested before your build is marked as passing or failing.\n\n![GitLab Runners Diagram](https://assets.digitalocean.com/articles/gitlab-runner/Autoscaling-GitLab-Runners.png){: .medium.center}\n\nBy leveraging each of the GitLab components, the CI/CD process will enable you to scale responsively based on demands. With these goals in mind, we are ready to begin setting up our [continuous deployment](/topics/ci-cd/) with GitLab and DigitalOcean.\n\n## Prerequisites\n\nThis tutorial will assume you have already configured GitLab on your own server or through the hosted service, and that you have an existing DigitalOcean account.\n\nTo set this up on an Ubuntu 16.04 Droplet, you can use the DigitalOcean one-click image, or follow our guide: “[How To Install and Configure GitLab on Ubuntu 16.04](https://www.digitalocean.com/community/tutorials/how-to-install-and-configure-gitlab-on-ubuntu-16-04).”\n\nFor the purposes of this tutorial, we assume you have private networking enabled on this Droplet, which you can achieve by following our guide on “[How To Enable DigitalOcean Private Networking on Existing Droplets](https://www.digitalocean.com/community/tutorials/how-to-enable-digitalocean-private-networking-on-existing-droplets),” but it is not compulsory.\n\nThroughout this tutorial, we’ll be using non-root users with admin privileges on our Droplets.\n\n## Step 1: Import JavaScript project\nTo begin, we will create a new example project in your existing GitLab instance containing a sample Node.js application.\n\n![GitLab interface](https://assets.digitalocean.com/articles/gitlab-runner/gitlab.jpg){: .shadow.large.center}\n\nLog into your GitLab instance and click the **plus icon**, then select **New project** from the dropdown menu.\n\nOn the new project screen, select the **Import project** tag, then click **Repo by URL** to import our example project directly from GitHub.\n\nPaste the below clone URL into the Git repository URL:\n\n```bash\nhttps://github.com/do-community/hello_hapi.git\n```\n\nThis repository is a basic JavaScript application for the purposes of demonstration, which we won’t be running in production. To complete the import, click the **New Project** button.\n\nYour new project will now be in GitLab and we can get started setting up our CI pipeline.\n\n## Step 2: Set up infrastructure\n\nOur GitLab Code Runner requires specific configuration as we’re planning to programmatically create Droplets to handle CI load as it grows and shrinks.\n\nWe will create two types of machines in this tutorial: a **bastion** instance, which controls and spawns new machines, and our **runner** instances, which are temporary servers spawned by the bastion Droplet to build code when required. The bastion instance uses Docker to create your runners.\n\nHere are the DigitalOcean products we’ll use, and what each component is used for:\n\n* **Flexible Droplets** — We will create memory-optimized Droplets for our GitLab Runners as it’s a memory-intensive process which will run using Docker for containerization. You can shrink or grow this Droplet in the future as needed, however we recommend the flexible Droplet option as a starting point to understand how your pipeline will perform under load.\n\n* **DigitalOcean Spaces (Object Storage)** — We will use [DigitalOcean Spaces](https://www.digitalocean.com/products/spaces/) to persist cached build components across your runners as they’re created and destroyed. This reduces the time required to set up a new runner when the CI pipeline is busy, and allows new runners to pick up where others left off immediately.\n\n* **Private Networking** — We will create a private network for your bastion Droplet and GitLab runners to ensure secure code compilation and to reduce firewall configuration required.\n\nTo start, we’ll create the bastion Droplet. Create a [new Droplet](https://cloud.digitalocean.com/droplets/new), then under **choose an image**, select the **One-click apps** tab. From there, select **Docker 17.12.0-ce on 16.04** (note that this version is current at the time of writing), then choose the smallest Droplet size available, as our bastion Droplet will manage the creation of other Droplets rather than actually perform tests.\n\nIt is recommended that you create your server in a data center that includes  [DigitalOcean Spaces](https://www.digitalocean.com/community/tutorials/an-introduction-to-digitalocean-spaces) in order to use the object storage caching features mentioned earlier.\n\nSelect both the **Private networking** and **Monitoring** options, then click **Create Droplet**.\n\nWe also need to set up our storage space which will be used for caching. Follow the steps in “[How To Create a DigitalOcean Space and API Key](https://www.digitalocean.com/community/tutorials/how-to-create-a-digitalocean-space-and-api-key)” to create a new Space in the same or nearest data center as your hosted GitLab instance, along with an API Key.\n\nNote this key down, as we’ll need it later in the tutorial.\n\nNow it’s time to get our CI started!\n\n## Step 3: Configure the GitLab Runner Bastion Server\n\nWith the fresh Droplet ready, we can now configure GitLab Runner. We’ll be installing scripts from GitLab and GitHub repositories.\n\nAs a best practice, be sure to inspect scripts to confirm what you will be installing prior to running the full commands below.\n\nConnect to the Droplet using SSH, move into the `/tmp` directory, then add the [official GitLab Runner repository](https://docs.gitlab.com/runner/install/linux-repository.html) to Ubuntu’s package manager:\n\n```bash\ncd /tmp\ncurl -L https://packages.gitlab.com/install/repositories/runner/gitlab-runner/script.deb.sh | sudo bash\n```\n\nOnce added, install the GitLab Runner application:\n\n```bash\nsudo apt-get install gitlab-runner\n```\n\nWe also need to install **[Docker Machine](https://docs.docker.com/machine/install-machine/#install-machine-directly)**, which is an additional Docker tool that assists with automating the deployment of containers on cloud providers:\n\n```bash\ncurl -L https://github.com/docker/machine/releases/download/v0.14.0/docker-machine-`uname -s`-`uname -m` >/tmp/docker-machine && \\\nsudo install /tmp/docker-machine /usr/local/bin/docker-machine\n```\n\nWith these installations complete, we can move on to connecting our GitLab Runner to our GitLab install.\n\n## Step 4: Obtain Runner registration token\n\nTo link GitLab Runner to your existing GitLab install, we need to link the two instances together by obtaining a token that authenticates your runner to your code repositories.\n\nLogin to your existing GitLab instance as the admin user, then click the wrench icon to enter the admin settings area.\n\nOn the left of your screen, hover over **Overview** and select **Runners** from the list that appears.\n\nOn the Runners page under the **How to set up a shared Runner for a new project** section, copy the token shown in Step 3, and make a note of it along with the publicly accessible URL of your GitLab instance from Step 2. If you are using HTTPS for Gitlab, make sure it is not a self-signed certificate, or GitLab Runner will fail to start.\n\n## Step 5: Configure GitLab on the Bastion Droplet\n\nBack in your SSH connection with your bastion Droplet, run the following command:\n\n```bash\nsudo gitlab-runner register\n```\n\nThis will initiate the linking process, and you will be asked a series of questions.\n\nOn the next step, enter the **GitLab instance URL** from the previous step:\n\n```bash\nPlease enter the gitlab-ci coordinator URL (e.g. https://gitlab.com)\nhttps://example.digitalocean.com\n```\n\nEnter the token you obtained from your GitLab instance:\n\n```bash\nPlease enter the gitlab-ci token for this runner\nsample-gitlab-ci-token\n```\n\nEnter a description that will help you recognize it in the GitLab web interface. We recommend naming this instance something unique, like `runner-bastion` for clarity.\n\n```bash\nPlease enter the gitlab-ci description for this runner\n[yourhostname] runner-bastion\n```\n\nIf relevant, you may enter the tags for code you will build with your runner. However, we recommend this is left blank at this stage. This can easily be changed from the GitLab interface later.\n\n```bash\nPlease enter the gitlab-ci tags for this runner (comma separated):\ncode-tag\n```\n\nChoose whether or not your runner should be able to run untagged jobs. This setting allows you to choose whether your runner should build repositories with no tags at all, or require specific tags. Select true in this case, so your runner can execute all repositories.\n\n```bash\nWhether to run untagged jobs [true/false]: true\n```\n\nChoose if this runner should be shared among your projects, or locked to the current one, which blocks it from building any code other than those specified. Select false for now, as this can be changed later in GitLab’s interface:\n\n```bash\nWhether to lock Runner to current project [true/false]: false\n```\n\nChoose the executor which will build your machines. Because we’ll be creating new Droplets using Docker, we’ll choose `docker+machine` here, but you can read more about the advantages of each approach in this [compatibility chart](https://docs.gitlab.com/runner/executors/README.html#compatibility-chart):\n\n```bash\nPlease enter the executor: ssh, docker+machine, docker-ssh+machine, kubernetes, docker, parallels, virtualbox, docker-ssh, shell:\ndocker+machine\n```\n\nYou’ll be asked which image to use for projects that don’t explicitly define one. We’ll choose a basic, secure default:\n\n```bash\nPlease enter the Docker image (e.g. ruby:2.1):\nalpine:latest\n```\n\nNow you’re done configuring the core bastion runner! At this point it should appear within the GitLab Runner page of your GitLab admin settings, which we accessed to obtain the token.\n\nIf you encounter any issues with these steps, the [GitLab Runner documentation](https://docs.gitlab.com/runner/register/index.html) includes options for troubleshooting.\n\n## Step 6: Configure Docker caching and Docker Machine\nTo speed up Droplet creation when the build queue is busy, we’ll leverage Docker’s caching tools on the Bastion Droplet to store the images for your commonly used containers on DigitalOcean Spaces.\n\nTo do so, upgrade Docker Machine on your SSH shell using the following command:\n\n```bash\ncurl -L https://github.com/docker/machine/releases/download/v0.14.0/docker-machine-`uname -s`-`uname -m` >/tmp/docker-machine && sudo install /tmp/docker-machine /usr/local/bin/docker-machine\n```\n\nWith Docker Machine upgraded, we can move on to setting up our access tokens for GitLab Runner to use.\n\n## Step 7: Gather DigitalOcean credentials\n\nNow we need to create the credentials that GitLab Runner will use to create new Droplets using your DigitalOcean account.\n\nVisit your DigitalOcean [dashboard](https://cloud.digitalocean.com) and click **API**. On the next screen, look for **Personal access tokens** and click **Generate New Token**.\n\nGive the new token a name you will recognize such as `GitLab Runner Access` and ensure that both the read and write scopes are enabled, as we need the Droplet to create new machines without human intervention.\n\nCopy the token somewhere safe as we’ll use it in the next step. You can’t retrieve this token again without regenerating it, so be sure it’s stored securely.\n\n## Step 8: Edit GitLab Runner configuration files\nTo bring all of these components together, we need to finish configuring our bastion Droplet to communicate with your DigitalOcean account.\n\nIn your SSH connection to your bastion Droplet, use your favorite text editor, such as nano, to open the GitLab Runner configuration file for editing:\n\n```bash\nnano /etc/gitlab-runner/config.toml\n```\n\nThis configuration file is responsible for the rules your CI setup uses to scale up and down on demand. To configure the bastion to autoscale on demand, you need to add the following lines:\n\n```bash\nconcurrent = 50   # All registered Runners can run up to 50 concurrent builds\n\n[[runners]]\n  url = \"https://example.digitalocean.com\"\n  token = \"existinggitlabtoken\"             # Note this is different from the registration token used by `gitlab-runner register`\n  name = \"example-runner\"\n  executor = \"docker+machine\"        # This Runner is using the 'docker+machine' executor\n  limit = 10                         # This Runner can execute up to 10 builds (created machines)\n  [runners.docker]\n    image = \"alpine:latest\"               # Our secure image\n  [runners.machine]\n    IdleCount = 1                    # The amount of idle machines we require for CI if build queue is empty\n    IdleTime = 600                   # Each machine can be idle for up to 600 seconds, then destroyed\n    MachineName = \"gitlab-runner-autoscale-%s\"    # Each machine will have a unique name ('%s' is required and generates a random number)\n    MachineDriver = \"digitalocean\"   # Docker Machine is using the 'digitalocean' driver\n    MachineOptions = [\n        \"digitalocean-image=coreos-stable\", # The DigitalOcean system image to use by default\n        \"digitalocean-ssh-user=core\", # The default SSH user\n        \"digitalocean-access-token=DO_ACCESS_TOKEN\", # Access token from Step 7\n        \"digitalocean-region=nyc3\", # The data center to spawn runners in\n        \"digitalocean-size=1gb\", # The size (and price category) of your spawned runners\n        \"digitalocean-private-networking\" # Enable private networking on runners\n    ]\n  [runners.cache]\n    Type = \"s3\"   # The Runner is using a distributed cache with the S3-compatible Spaces service\n    ServerAddress = \"nyc3.spaces.digitaloceanspaces.com\"\n    AccessKey = \"YOUR_SPACES_KEY\"\n    SecretKey = \"YOUR_SPACES_SECRET\"\n    BucketName = \"your_bucket_name\"\n    Insecure = true # We do not have a SSL certificate, as we are only running locally\n\n```\n\nOnce you’ve added the new lines, customize the access token, region and Droplet size based on  your setup. For the purposes of this tutorial, we’ve used the smallest Droplet size of 1GB and created our Droplets in NYC3. Be sure to use the information that is relevant in your case.\n\nYou also need to customize the cache component, and enter your Space’s server address from the infrastructure configuration step, access key, secret key and the name of the Space that you created.\n\nWhen completed, restart GitLab Runner to make sure the configuration is being used:\n\n```bash\ngitlab-runner restart\n```\n\nIf you would like to learn about more all available options, including off-peak hours, you can read [GitLab’s advanced documentation](https://docs.gitlab.com/runner/configuration/autoscale.html).\n\n## Step 9 — Test Your GitLab Runner\n\nAt this point, our GitLab Runner bastion Droplet is configured and is able to create DigitalOcean Droplets on demand, as the CI queue fills up. We’ll need to test it to be sure it works by heading to your GitLab instance and the project we imported in Step 1.\n\nTo trigger a build, edit the `readme.md` file by clicking on it, then clicking **edit**, and add any relevant testing text to the file, then click **Commit changes**.\n\nNow a build will be automatically triggered, which can be found under the project’s **CI/CD** option in the left navigation.\n\nOn this page you should see a pipeline entry with the status of **running**. In your DigitalOcean account, you’ll see a number of Droplets automatically created by GitLab Runner in order to build this change.\n\nCongratulations! Your CI pipeline is cloud scalable and now manages its own resource usage. After the specified idle time, the machines should be automatically destroyed, but we recommend verifying this manually to ensure you aren’t unexpectedly billed.\n\n## Troubleshooting\n\nIn some cases, GitLab may report that the runner is unreachable and as a result perform no actions, including deploying new runners. You can troubleshoot this by stopping GitLab Runner, then starting it again in debug mode:\n\n```bash\ngitlab-runner stop\ngitlab-runner --debug start\n```\n\nThe output should throw an error, which will be helpful in determining which configuration is causing the issue.\n\nIf your configuration creates too many machines, and you wish to remove them all at the same time, you can run this command to destroy them all:\n\n```bash\ndocker-machine rm $(docker-machine ls -q)\n```\nFor more troubleshooting steps and additional configuration options, you can refer to [GitLab’s documentation](https://docs.gitlab.com/runner/).\n\n## Conclusion\n\nYou've successfully set up an automated CI/CD pipeline using GitLab Runner and Docker. From here, you could configure higher levels of caching with Docker Registry to optimize performance or explore the use of tagging code builds to specific GitLab code runners.\n\nFor more on GitLab Runner, [see the detailed documentation](https://docs.gitlab.com/runner/), or to learn more, you can read [GitLab’s series of blog posts](https://docs.gitlab.com/ee/ci/) on how to make the most of your continuous integration pipeline.\n\n[This post was originally published by DigitalOcean](https://www.digitalocean.com/community/tutorials/how-to-autoscale-gitlab-continuous-deployment-with-gitlab-runner-on-digitalocean) and is licensed under [CC BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/).\n",[23,24],"CI","integrations","yml",{},true,"/en-us/blog/autoscale-continuous-deployment-gitlab-runner-digital-ocean",{"title":15,"description":16,"ogTitle":15,"ogDescription":16,"noIndex":12,"ogImage":19,"ogUrl":30,"ogSiteName":31,"ogType":32,"canonicalUrls":30},"https://about.gitlab.com/blog/autoscale-continuous-deployment-gitlab-runner-digital-ocean","https://about.gitlab.com","article","en-us/blog/autoscale-continuous-deployment-gitlab-runner-digital-ocean",[35,24],"ci","vdKwTeQxBWZw8SON4BZ5j9qodFSgK27oy5MaQtGssWE",{"data":38},{"logo":39,"freeTrial":44,"sales":49,"login":54,"items":59,"search":366,"minimal":397,"duo":416,"pricingDeployment":426},{"config":40},{"href":41,"dataGaName":42,"dataGaLocation":43},"/","gitlab logo","header",{"text":45,"config":46},"Get free 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statement",{"items":678},[679,682,685],{"text":680,"config":681},"Terms",{"href":506,"dataGaName":507,"dataGaLocation":454},{"text":683,"config":684},"Cookies",{"dataGaName":516,"dataGaLocation":454,"id":517,"isOneTrustButton":27},{"text":686,"config":687},"Privacy",{"href":511,"dataGaName":512,"dataGaLocation":454},[689],{"id":690,"title":18,"body":8,"config":691,"content":693,"description":8,"extension":25,"meta":697,"navigation":27,"path":698,"seo":699,"stem":700,"__hash__":701},"blogAuthors/en-us/blog/authors/owen-williams.yml",{"template":692},"BlogAuthor",{"name":18,"config":694},{"headshot":695,"ctfId":696},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1749659488/Blog/Author%20Headshots/gitlab-logo-extra-whitespace.png","Owen-Williams",{},"/en-us/blog/authors/owen-williams",{},"en-us/blog/authors/owen-williams","13I935syOPTojvVRkZ-Xc4gV6wLKORSEFbKGKmYQw7s",[703,716,728],{"content":704,"config":714},{"title":705,"description":706,"authors":707,"heroImage":709,"date":710,"category":9,"tags":711,"body":713},"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.",[708],"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",[258,610,712],"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":715,"featured":12,"template":13},"how-iit-bombay-students-code-future-with-gitlab",{"content":717,"config":726},{"title":718,"description":719,"authors":720,"heroImage":721,"date":722,"category":9,"tags":723,"body":725},"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.",[708],"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",[610,258,724],"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":727,"featured":27,"template":13},"artois-university-elevates-curriculum-with-gitlab-ultimate-for-education",{"content":729,"config":742},{"category":9,"tags":730,"body":733,"date":734,"updatedDate":735,"heroImage":736,"authors":737,"title":740,"description":741},[731,732,106],"tutorial","git","\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. Self-managed GitLab may need Elasticsearch/OpenSearch for advanced features.                                                                   |\n","2025-12-03","2026-01-16","https://res.cloudinary.com/about-gitlab-com/image/upload/v1749658924/Blog/Hero%20Images/securitylifecycle-light.png",[738,739],"Evgeny Rudinsky","Michael Leopard","Guide: Migrate from Azure DevOps to GitLab","Learn how to carry out the full migration from Azure DevOps to GitLab using GitLab Professional Services migration tools — from planning and execution to post-migration follow-up tasks.",{"featured":27,"template":13,"slug":743},"migration-from-azure-devops-to-gitlab",{"promotions":745},[746,760,771],{"id":747,"categories":748,"header":750,"text":751,"button":752,"image":757},"ai-modernization",[749],"ai-ml","Is AI achieving its promise at scale?","Quiz will take 5 minutes or less",{"text":753,"config":754},"Get your AI maturity score",{"href":755,"dataGaName":756,"dataGaLocation":240},"/assessments/ai-modernization-assessment/","modernization assessment",{"config":758},{"src":759},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/qix0m7kwnd8x2fh1zq49.png",{"id":761,"categories":762,"header":763,"text":751,"button":764,"image":768},"devops-modernization",[724,556],"Are you just managing tools or shipping innovation?",{"text":765,"config":766},"Get your DevOps maturity score",{"href":767,"dataGaName":756,"dataGaLocation":240},"/assessments/devops-modernization-assessment/",{"config":769},{"src":770},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138785/eg818fmakweyuznttgid.png",{"id":772,"categories":773,"header":775,"text":751,"button":776,"image":780},"security-modernization",[774],"security","Are you trading speed for security?",{"text":777,"config":778},"Get your security maturity score",{"href":779,"dataGaName":756,"dataGaLocation":240},"/assessments/security-modernization-assessment/",{"config":781},{"src":782},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/p4pbqd9nnjejg5ds6mdk.png",{"header":784,"blurb":785,"button":786,"secondaryButton":791},"Start building faster today","See what your team can do with the intelligent orchestration platform for DevSecOps.\n",{"text":787,"config":788},"Get your free trial",{"href":789,"dataGaName":48,"dataGaLocation":790},"https://gitlab.com/-/trial_registrations/new?glm_content=default-saas-trial&glm_source=about.gitlab.com/","feature",{"text":492,"config":792},{"href":52,"dataGaName":53,"dataGaLocation":790},1772652065878]