[{"data":1,"prerenderedAt":814},["ShallowReactive",2],{"/en-us/blog/a-story-of-runner-scaling":3,"navigation-en-us":43,"banner-en-us":443,"footer-en-us":453,"blog-post-authors-en-us-Darwin Sanoy|Brian Wald":695,"blog-related-posts-en-us-a-story-of-runner-scaling":723,"assessment-promotions-en-us":765,"next-steps-en-us":804},{"id":4,"title":5,"authorSlugs":6,"body":9,"categorySlug":10,"config":11,"content":15,"description":9,"extension":29,"isFeatured":13,"meta":30,"navigation":31,"path":32,"publishedDate":22,"seo":33,"stem":37,"tagSlugs":38,"__hash__":42},"blogPosts/en-us/blog/a-story-of-runner-scaling.yml","A Story Of Runner Scaling",[7,8],"darwin-sanoy","brian-wald",null,"engineering",{"slug":12,"featured":13,"template":14},"a-story-of-runner-scaling",false,"BlogPost",{"title":16,"description":17,"authors":18,"heroImage":21,"date":22,"body":23,"category":10,"tags":24},"An SA story about hyperscaling GitLab Runner workloads using Kubernetes","It is important to have the complete picture of scaled effects in view when designing automation.",[19,20],"Darwin Sanoy","Brian Wald","https://res.cloudinary.com/about-gitlab-com/image/upload/v1749669897/Blog/Hero%20Images/kaleidico-26MJGnCM0Wc-unsplash.jpg","2022-06-29","\n\nThe following *fictional story*\u003Csup>1\u003C/sup> reflects a repeating pattern that Solutions Architects at GitLab encounter frequently. In the analysis of this story we intend to demonstrate three things: (a) Why one should be thoughtful in leveraging Kubernetes for scaling, (b) How unintended consequences of an approach to automation can create a net productivity loss for an organization (reversal of ROI) and (c) How solutions architecture perspectives can help find anti-patterns - retrospectively or when applied during a development process.\n\n### A DevOps transformation story snippet\n\nGild Investment Trust went through a DevOps transformational effort to build efficiency in their development process through automation with GitLab. Dakota, the application development director, knew that their current system handled about 80 pipelines with 600 total tasks and over 30,000 CI minutes so they knew that scaled CI was needed. Since development occurred primarily during European business hours, they were interested in reducing compute costs outside of peak work hours. Cloud compute was also a target due to acquring the pay per use model combined with elastic scaling.\n\nIngrid was the infrastructure engineer for developer productivity who was tasked with building out the shared GitLab Runner fleet to meet the needs of the development teams. At the beginning of the project she made a successful bid to leverage Kubernetes to scale CI and CD to take advantage of the elastic scaling and high availability all with the efficiency of containers. Ingrid had recently achieved the Certified Kubernetes Administrator (CKA) certification and she was eager to put her knowledge to practical use. She did some additional reading around applications running on Kubernetes and noted the strong emphasis on minimizing the resource profile of microservices to achieve efficiency in the form of compute density. She defined runner containers with 2GB of memory and 750millicores (about three quarters of a CPU) had good results from running some test CI pipelines. She also decided to leverage the Kubernetes Cluster Autoscaler which would use the overall cluster utilization and scheduling to automatically add and remove Kubernetes worker nodes for smooth elastic scaling in response to demand.\n\nAbout 3 months into the proof of concept implementation, Sasha, a developer team lead, noted that many of their new job types were failing with strange error messages. The same jobs ran fine on quickly provisioned GitLab shell runners. Since the primary difference between the environments was the liberal allocation of machine resources in a shell runner, Sasha reasoned that the failures were likely due to the constrained CPU and memory resources of the Kubernetes pods.\n\nTo test this hypothesis, Ingrid decided to add a new pod definition. She found it was difficult to discern which of the job types were failing due to CPU constraints, which ones due to memory constraints and which ones due to the combination of both. She knew it could be a lot of her time to discern the answer. She decided to simply define a pod that was more liberal on both CPU and memory and have it be selectable by runner tagging when more resources were needed for certain CI jobs. She created a GitLab Runner pod definition with 4GB of memory and 1750 millicores of CPU to cover the failing job types. Developers could then use these larger containers when the smaller ones failed by adding the ‘large-container’ tag to their GitLab job.\n\nSasha redid the CI testing and was delighted to find that the new resourcing made all the troubling jobs work fine. Sasha created a guide for developers to try to help discern when mysterious error messages and failed CI jobs were probably the fault of resourcing and then how to add a runner tag to the job to expand the resources.\n\nSome weeks later two of the key jobs that were fixed by the new container resourcing started intermittently failing on NPM package creation jobs for just 3 pipelines on 2 different teams. Of course Sasha tried to understand what the differences were and found that these particular pipelines were packaging notably large file sets because they were actually packaging testing data and the NPM format was a convenient way to provide testing data during automated QA testing.\n\nSasha brought this information to Ingrid and together they did testing to figure out that a 6GB container with 2500 millicores would be sufficient for creating an NPM package out of the current test dataset size. They also discussed whether the development team might want to use a dedicated test data management solution, but it turned out that the teams needs were very simple and that their familiarity with NPM packaging meant that bending NPM packaging to suit their purpose was actually more efficient than acquiring, deploying, learning and maintaining a special system for this purpose. So a new pod resourcing profile was defined and could be accessed with the runner tag ‘xlarge’.\n\nSasha updated the guide for finding the optimal container size through failure testing of CI jobs - but they were not happy with how large the document was getting and how imprecise the process was for determining when a CI job failure was, most likely due to container resource constraints. They were concerned that developers would not go through the process and instead simply pick the largest container resourcing profile in order to avoid the effort of optimizing and they shared this concern with Ingrid. In fact, Sasha noted, they were hard pressed themselves to follow their own guidelines and not to simply choose the largest container for all jobs themselves.\n\nThe potential for this cycle to repeat was halted several months later when Dakota, the app dev director, generated a report that showed a 2% increase in developer time spent optimizing CI jobs using failure testing for container size optimization. Dakota considered this work to be a net new increase because when the company was not using container-based CI, the developers did not have to manage this concern at all. Across 298 developers this amounted to around $840,000/yr dollars of total benefits per month\u003Csup>2\u003C/sup>. It was also thought to add about 2 hours (and growing) to developer onboarding training. It was noted that the report did not attempt to account for the opportunity cost tax - what would these people be doing to solve customer problems with that time? It also did not account for the \"critical moments tax\" (when complexity has an outsized frustration effect and business impact on high pressure, high risk situations).\n\n### Solution architecture retrospective: What went wrong?\n\nThis story reflects a classic antipattern we see at GitLab, not only with regard to Kubernetes runner optimization, but also across other areas, such as overly minimalized build containers and the potential for resultant pipeline complexity as was discussed in a previous blog called [When the pursuit of simplicity creates complexity in container-based CI pipelines](/blog/second-law-of-complexity-dynamics/). Frequently this result comes from inadvertent adherance to heuristics of a small part of the problem as though they were applicable to the entirety of the problem (a type of a logical “fallacy of composition”).\n\nThankfully the emergence of the anti-pattern follows a pattern itself :). Let’s apply a little retrospective solution architecture to the \"what happened\" in order to learn what might be done proactively next time to create better iterations on the next automation project.\n\nThere is a certain approach to landscaping shared greenspaces where, rather than shame people into compliance with signs about not cutting across the grass in key locations, the paths that humans naturally take are interpreted as the signal “there should be a path here.” Humans love beauty and detail in the environments they move through, but depending on the space, they can also value the efficiency of the shortest possible route slightly higher than aesthetics. A wise approach to landscaping holds these factors in a balance that reflects the efficiency versus aesthetic appeal balance of the space user. The space stays beautiful without any shaming required.\n\nIn our story Sasha and Ingrid had exactly this kind of cue where the developers were likely to walk across the grass. If that cue is taken to be a signal that reflects efficiency, we can quickly see what can be done to avoid the antipattern when it starts to occur.\n\nThe signal was the observation that developers might simply choose the largest container all the time to avoid the fussy process of optimizing the compute resources being consumed. Some would consider that laziness and not a good signal to heed. However, most human laziness is deeply rooted in efficiency trade-offs. The developers intuitively understand that their time fussing with failure testing to optimize job containers and their time diagnosing intermittent failures due to the varying content of those jobs, is not worth the amount of compute saved. That is especially true given the opportunity cost of not spending that time innovating the core software solution for the revenue generating application.\n\nIngrid and Sasha’s collaboration has initially missed the scaled human toil factor that was introduced to keep container resources at the minimum tolerable levels. They failed to factor in the escalating cost of scaled human toil to have a comprehensive efficiency measurement. They were following a microservices resourcing pattern which assumes the compute is purpose designed around minimal and well known workloads. When taken as a whole in a shared CI cluster, CI compute follows generalized compute patterns where the needs for CPU, Memory, Disk IO and Network IO can vary wildly from one moment to the next.\n\nIn the broadest analysis, the infrastructure team over indexed to the “team local” optimization of compute efficiency and unintentionally created a global de-optimization of scaled human toil for another team.\n\n## How can this antipattern be avoided?\n\nOne way to combat over indexing on a criteria is to have balancing objectives. This need is covered in \"Measure What Matters\" with the concept of counter balancing objectives. There are some counter balancing questions that can be asked of almost any automation effort. When solution architecture is functioning well these counter balancing questions are asked during the iterative process of building out a solution. Here are some applicable ones for this effort:\n\n**Approporiate Rules: Does the primary compute optimization heuristic match the characteristics of the actual compute workload being optimized?**\n\nThe main benefits of container compute for CI are dependency isolation, dependency encapsulation and a clean build environment for every job. None of these benefits has to do with the extreme resource optimizations available to engineer microservices architected applications. As a whole, CI compute reflects generalized compute, not the ultra-specialized compute of a 12 factor architected micro-service.\n\n**Appropriate granularity: Does optimization need to be applied at every level?**\n\nThe fact that the cluster itself has elastic scaling at the Kubernetes node level is a higher order optimization that will generate significant savings. Another possible optimization that would not require continuous fussing by developers is having a node group running on spot compute (as long as the spot compute runners self-identify their compute as spot so pipeline engineers can select appropriate jobs for spot). These optimizations can create huge savings, without creating scaled human toil.\n\n**People and processes counter check: Does the approach to optimization create scaled human toil by its intensity and/or frequency and/or lack of predictability for any people anywhere in the organization?**\n\nAutomation is all about moving human toil into the world of machines. While optimizing machine resources must always be a primary consideration, it is a lower priority objective than creating a net increase in human toil anywhere in your company. Machines can efficiently and elastically scale, while human workforces respond to scaling needs in months or even years.\n\n### Avoid scaled human toil\n\nNotice that neither the story, nor the qualifying questions, imply there is never a valid reason to have specialized runners that developers might need to select using tags. If a given attribute of runners could be selected once and with confidence then the antipattern would not be in play. One example would be selecting spot compute backed runners for workloads that can tolerate termination. It is the potential for repeated needed attention to calibrate container sizing - made worse by the possibility of intermittent failure based on job content - that pushes this specific scenario into the potential realm of “scaled human toil.” The ability to leverage elastic cluster autoscaling is also a huge help to managing compute resources more efficiently.\n\nIf the risk of scaled human toil could be removed then some of this approach may be able to be preserved. For example, having very large minimum pod resourcing and then a super-size for stuff that breaks the standard pod size just once. Caution is still warranted because it is still possible that developers have to fuss a lot to get a two pod approach working in practice.\n\n### Beware of scaled human toil of an individual\n\nOne thing the story did not highlight is that even if we were able to move all the fussing of such a design to the Infrastructure Engineer persona (perhaps by building an AI tuning mechanism that guesses at pod resourcing for a given job), the cumulative taxes on their role are frequently still not worth the expense. This is, in part, because they have a leveraged role - they help with all the automation of the scaled developer workforce and any time they spend on one activity can’t be spent on another. We humans are generally bad at accounting for opportunity costs - what else could that specific engineer be innovating on to make a stronger overall impact to the organization’s productivity or bottom line? Given the very tight IT labor market, a given function may not be able to add headcount, so opportunity costs take on an outsized importance.\n\n### Unlike people’s time, cloud compute does not carry opportunity cost\n\nA long time ago people had to schedule time on shared computing resources. If the time was used for low-value compute activities it could be taking away time from higher value activities. In this model compute time has an opportunity cost - the cost of what it could be using that time for if it wasn’t doing a lower value activity. Cloud compute has changed this because when compute is not being used, it is not being paid for. Additionally, elastic scaling eliminates the costs of over provisioning hardware and completely eliminates the administrative overhead of procuring capacity - if you need lots for a short period of time it is immediately available. In contrast, people time is not elastically scalable nor pay per use. This means that the opportunity cost question “What could this time be used for if it didn’t have to be spent on low value activities?” is still relevant for anything that creates activities for people.\n\n### The first corollary to the Second Law of Complexity Dynamics\n\nThe Second Law of Complexity Dynamics was introduced in an earlier blog. The essence is that complexity is never destroyed - it is only reformed - and primarily it is moved across a boundary line that dictates whether the management of the complexity is in our domain or externalized. For instance, if you write a function for md5 hashing in your code, you are managing the complexity of that code. If you install a dependency package that contains a premade md5 hash function that you simply use, then the complexity is externalized and managed for you by someone else.\n\nIn this story we are introducing the corollary to that “Law” that “**Exchanging Raw Machine Resources for Complexity Management is Generally a Reasonable Trade-off.**” In this case our scaled human toil is created due to the complexity of unending, daily management of optimizing compute efficiency. This does not mean that burning thousands of dollars of inefficient compute is OK because it saved someone 20 minutes of fussing. It is scoped in the following way:\n\n- scoped to “complexity management” (which is creating the “scaled human toil” in our story) - many minutes of toil that increases proportionally or compounds with more of the activity.\n- scoped to “raw machine resources” - meaning that there is not additional logistics nor human toil to gain the resources. In the cloud raw machine resources are generally available via configuration tweaks.\n- scoped to “generally reasonable” - this indicates a disposition of being very cautious about increasing human toil with an automatoin solution - but it still makes sense to use models or calculations to check if the rule actually holds in a given case.\n\nSo if we can externalize complexity management that is great (The Second Law of Complexity Dynamics). If we can trade complexity management for raw computing resource, that is likely still better than managing it ourselves (The First Corollary).\n\n### Iterating SA: Experimental improvements for your next project\n\nThis post contains specifics that can be used to avoid antipatterns in building out a Kubernetes cluster for GitLab CI. However, in the qualifying questions we’ve attempted to kick it up to one meta-level higher to help assess whether any automation effort may have an “overly local” optimization focus which can inadvertently create a net loss of efficiency across the more global “company context.” It is our opinion that automation efforts that create a net loss in human productivity should not be classified as automation at all. While it’s strong medicine to apply to one’s work, we feel that doing so causes appropriate innovation pressure to ensure that individual automation efforts truly deliver on their inherent promise of higher human productivity and efficiency. So simply ask “Does this way of solving a problem cause recurring work for anyone?”\n\n### DevOps transformation and solution architecture perspectives\n\nA technology architecture focus rightfully hones in on the technology choices for a solution build. However, if it is the only lens, it can result in scenarios like our story. Solutions architecture steps back to a broader perspective to sanity-check that solution iterations account for a more complete picture of both the positive and negative impacts across all three of people, processes and technology. As an organizational competency, DevOps emphasis solution architecture perspectives when it is defined as a collaborative and cultural approach to people, processes and technology.\n\nFootnotes:\n\n1. This fictional story was devised specifically for this article and does not knowingly reflect the details of any other published story or an actual situation. The names used in the story are from [GitLab’s list of personas](https://handbook.gitlab.com/handbook/product/personas/).\n2. Across a team of 300 full time developers. 9.6min/workday x 250 workdays / year = 2400mins / 8hrs/workday  = 5 workdays x $560 per day (140K Total Comp/250days) = $2800/dev/year x 300 developers = $840,000/yr\n\nCover image by [Kaleidico](https://unsplash.com/@kaleidico?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText) on [Unsplash](https://unsplash.com/)\n",[25,26,27,28],"CI","CD","performance","solutions 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Architect",{"headshot":703,"linkedin":704,"ctfId":705},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1749659751/Blog/Author%20Headshots/Darwin-Sanoy-headshot-395-square-gitlab-teampage-avatar.png","https://linkedin.com/in/darwinsanoy","DarwinJS",{},"/en-us/blog/authors/darwin-sanoy",{},"en-us/blog/authors/darwin-sanoy","UkMMwmU5o2e6Y-wBltA9E_z96LvHuB-bG6VW9DsLzIY",{"id":712,"title":20,"body":9,"config":713,"content":714,"description":9,"extension":29,"meta":718,"navigation":31,"path":719,"seo":720,"stem":721,"__hash__":722},"blogAuthors/en-us/blog/authors/brian-wald.yml",{"template":699},{"name":20,"config":715},{"headshot":716,"ctfId":717},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1749659488/Blog/Author%20Headshots/gitlab-logo-extra-whitespace.png","78qOxgHKlgDY2IxMrBrgCu",{},"/en-us/blog/authors/brian-wald",{},"en-us/blog/authors/brian-wald","5Q1gvafW_GWqOIMuzuluMCFYFcgFz-Zdg1I0S9LPU6k",[724,737,749],{"content":725,"config":735},{"title":726,"description":727,"authors":728,"heroImage":730,"date":731,"category":10,"tags":732,"body":734},"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.",[729],"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",[265,617,733],"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":736,"featured":13,"template":14},"how-iit-bombay-students-code-future-with-gitlab",{"content":738,"config":747},{"title":739,"description":740,"authors":741,"heroImage":742,"date":743,"category":10,"tags":744,"body":746},"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.",[729],"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",[617,265,745],"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":748,"featured":31,"template":14},"artois-university-elevates-curriculum-with-gitlab-ultimate-for-education",{"content":750,"config":763},{"category":10,"tags":751,"body":754,"date":755,"updatedDate":756,"heroImage":757,"authors":758,"title":761,"description":762},[752,753,112],"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",[759,760],"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":31,"template":14,"slug":764},"migration-from-azure-devops-to-gitlab",{"promotions":766},[767,781,792],{"id":768,"categories":769,"header":771,"text":772,"button":773,"image":778},"ai-modernization",[770],"ai-ml","Is AI achieving its promise at scale?","Quiz will take 5 minutes or less",{"text":774,"config":775},"Get your AI maturity score",{"href":776,"dataGaName":777,"dataGaLocation":247},"/assessments/ai-modernization-assessment/","modernization assessment",{"config":779},{"src":780},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/qix0m7kwnd8x2fh1zq49.png",{"id":782,"categories":783,"header":784,"text":772,"button":785,"image":789},"devops-modernization",[745,563],"Are you just managing tools or shipping innovation?",{"text":786,"config":787},"Get your DevOps maturity score",{"href":788,"dataGaName":777,"dataGaLocation":247},"/assessments/devops-modernization-assessment/",{"config":790},{"src":791},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138785/eg818fmakweyuznttgid.png",{"id":793,"categories":794,"header":796,"text":772,"button":797,"image":801},"security-modernization",[795],"security","Are you trading speed for security?",{"text":798,"config":799},"Get your security maturity score",{"href":800,"dataGaName":777,"dataGaLocation":247},"/assessments/security-modernization-assessment/",{"config":802},{"src":803},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/p4pbqd9nnjejg5ds6mdk.png",{"header":805,"blurb":806,"button":807,"secondaryButton":812},"Start building faster today","See what your team can do with the intelligent orchestration platform for DevSecOps.\n",{"text":808,"config":809},"Get your free trial",{"href":810,"dataGaName":54,"dataGaLocation":811},"https://gitlab.com/-/trial_registrations/new?glm_content=default-saas-trial&glm_source=about.gitlab.com/","feature",{"text":499,"config":813},{"href":58,"dataGaName":59,"dataGaLocation":811},1772652062232]