[{"data":1,"prerenderedAt":799},["ShallowReactive",2],{"/en-us/blog/gitlab-com-stability-post-gcp-migration":3,"navigation-en-us":42,"banner-en-us":442,"footer-en-us":452,"blog-post-authors-en-us-Andrew Newdigate":694,"blog-related-posts-en-us-gitlab-com-stability-post-gcp-migration":708,"assessment-promotions-en-us":750,"next-steps-en-us":789},{"id":4,"title":5,"authorSlugs":6,"body":8,"categorySlug":9,"config":10,"content":14,"description":8,"extension":29,"isFeatured":12,"meta":30,"navigation":31,"path":32,"publishedDate":20,"seo":33,"stem":37,"tagSlugs":38,"__hash__":41},"blogPosts/en-us/blog/gitlab-com-stability-post-gcp-migration.yml","Gitlab Com Stability Post Gcp Migration",[7],"andrew-newdigate",null,"engineering",{"slug":11,"featured":12,"template":13},"gitlab-com-stability-post-gcp-migration",false,"BlogPost",{"title":15,"description":16,"authors":17,"heroImage":19,"date":20,"body":21,"category":9,"tags":22},"What's up with GitLab.com? Check out the latest data on its stability","Let's take a look at the data on the stability of GitLab.com from before and after our recent migration from Azure to GCP, and dive into why things are looking up.",[18],"Andrew Newdigate","https://res.cloudinary.com/about-gitlab-com/image/upload/v1749671280/Blog/Hero%20Images/gitlab-gke-integration-cover.png","2018-10-11","This post is inspired by [this comment on\nReddit](https://www.reddit.com/r/gitlab/comments/9f71nq/thanks_gitlab_team_for_improving_the_stability_of/), thanking us for improving the stability of GitLab.com. Thanks, hardwaresofton! Making GitLab.com ready for your mission-critical workloads has been top of mind for us for some time, and it's great to hear that users are noticing a difference.\n\n_Please note that the numbers in this post differ slightly from the Reddit post as the data has changed since that post._\n\nWe will continue to work hard on improving the availability and stability of the platform. Our current goal is to achieve 99.95 percent availability on GitLab.com – look out for an upcoming post about how we're planning to get there.\n\n## GitLab.com stability before and after the migration\n\nAccording to [Pingdom](http://stats.pingdom.com/81vpf8jyr1h9), GitLab.com's availability for the year to date, up until the migration was **[99.68 percent](https://docs.google.com/spreadsheets/d/1uJ_zacNvJTsvJUfNpi1D_aPBg-vNJC1xJzsSwGKKt8g/edit#gid=527563485&range=F2)**, which equates to about 32 minutes of downtime per week on average.\n\nSince the migration, our availability has improved greatly, although we have much less data to compare with than in Azure.\n\n![Availability Chart](https://docs.google.com/spreadsheets/d/e/2PACX-1vQg_tdtdZYoC870W3u2R2icSK0Rd9qoOtDJqYHALaQlzhxXOmfY63X1NMMyFVEypQs7NngR4UUIZx5R/pubchart?oid=458170195&format=image)\n\nUsing data publicly available from Pingdom, here are some stats about our availability for the year to date:\n\n| Period | Mean-time between outage events |\n| --- | --- |\n| Pre-migration (Azure) | **1.3 days** |\n| Post-migration (GCP) | **7.3 days** |\n| Post-migration (GCP) excluding 1st day | **12 days** |\n\nThis is great news: we're experiencing outages less frequently. What does this mean for our availability, and are we on track to achieve our goal of 99.95 percent?\n\n| Period | Availability | Downtime per week |\n| --- | --- | --- |\n| Pre-migration (Azure) | **[99.68%](https://docs.google.com/spreadsheets/d/1uJ_zacNvJTsvJUfNpi1D_aPBg-vNJC1xJzsSwGKKt8g/edit#gid=527563485&range=F2)** | **32 minutes** |\n| Post-migration (GCP) | **[99.88%](https://docs.google.com/spreadsheets/d/1uJ_zacNvJTsvJUfNpi1D_aPBg-vNJC1xJzsSwGKKt8g/edit#gid=527563485&range=B3)** | **13 minutes** |\n| Target - not yet achieved | **99.95%** | **5 minutes** |\n\nDropping from 32 minutes per week average downtime to 13 minutes per week means we've experienced a **61 percent improvement** in our availability following our migration to Google Cloud Platform.\n\n## Performance\n\nWhat about the performance of GitLab.com since the migration?\n\nPerformance can be tricky to measure. In particular, averages are a terrible way of measuring performance, since they neglect outlying values. One of the better ways to measure performance is with a latency histogram chart. To do this, we imported the GitLab.com access logs for July (for Azure) and\nSeptember (for Google Cloud Platform) into [Google\nBigQuery](https://cloud.google.com/bigquery/), then selected the 100 most popular endpoints for each month and categorised these as either API, web, git, long-polling, or static endpoints. Comparing these histograms side-by-side allows us to study how the performance of GitLab.com has changed since the migration.\n\n![GitLab.com Latency\nHistogram](https://about.gitlab.com/images/blogimages/whats-up-with-gitlab-com/azure_v_gcp_latencies.gif)\n\nIn this histogram, higher values on the left indicate better performance.\nThe right of the graph is the \"_tail_\", and the \"_fatter the tail_\", the worse the user experience.\n\nThis graph shows us that with the move to GCP, more requests are completing within a satisfactory amount of time.\n\nHere's two more graphs showing the difference for API and Git requests respectively.\n\n![API Latency\nHistogram](https://about.gitlab.com/images/blogimages/whats-up-with-gitlab-com/api-performance-histogram.png)\n\n![Git Latency\nHistogram](https://about.gitlab.com/images/blogimages/whats-up-with-gitlab-com/git-performance-histogram.png)\n\n## Why these improvements?\n\nWe chose Google Cloud Platform because we believe that Google offer the most reliable cloud platform for our workload, particularly as we move towards running GitLab.com in [Kubernetes](/solutions/kubernetes/).\n\nHowever, there are many other reasons unrelated to our change in cloud provider for these improvements to stability and performance.\n\n> #### _“We chose Google Cloud Platform because we believe that Google offer\nthe most reliable cloud platform for our workload”_\n\nLike any large SaaS site, GitLab.com is a large, complicated system, and attributing availability changes to individual changes is extremely difficult, but here are a few factors which may be effecting our availability and performance:\n\n### Reason #1: Our Gitaly Fleet on GCP is much more powerful than before\n\nGitaly is responsible for all Git access in the GitLab application. Before\nGitaly, Git access occurred directly from within Rails workers. Because of the scale we run at, we require many servers serving the web application, and therefore, in order to share git data between all workers, we relied on\nNFS volumes. Unfortunately this approach doesn't scale well, which led to us building Gitaly, a dedicated Git service.\n\n> #### _“We've opted to give our fleet of 24 Gitaly servers a serious\nupgrade”_\n\n#### Our upgraded Gitaly fleet\n\nAs part of the migration, we've opted to give our fleet of 24 [Gitaly](/blog/the-road-to-gitaly-1-0/) servers a serious upgrade. If the old fleet was the equivalent of a nice family sedan, the new fleet are like a pack of snarling musclecars, ready to serve your Git objects.\n\n| Environment | Processor | Number of cores per instance | RAM per instance |\n| --- | --- | --- | --- |\n| Azure | Intel Xeon Ivy Bridge @ 2.40GHz | 8 | 55GB |\n| GCP | Intel Xeon Haswell @ 2.30GHz | **32** | **118GB** |\n\nOur new Gitaly fleet is much more powerful. This means that Gitaly can respond to requests more quickly, and deal better with unexpected traffic surges.\n\n#### IO performance\n\nAs you can probably imagine, serving [225TB of Git data](https://dashboards.gitlab.com/d/ZwfWfY2iz/vanity-metrics-dashboard?orgId=1)\nto roughly half-a-million active users a week is a fairly IO-heavy operation. Any performance improvements we can make to this will have a big impact on the overall performance of GitLab.com.\n\nFor this reason, we've focused on improving performance here too.\n\n| Environment | RAID | Volumes | Media | filesystem | Performance |\n| --- | --- | --- | --- | --- | --- |\n| Azure | RAID 5 (lvm) | 16 | magnetic | xfs | 5k IOPS, 200MB/s (_per disk_) / 32k IOPS **1280MB/s** (_volume group_) |\n| GCP | No raid | 1 | **SSD** | ext4 | **60k read IOPs**, 30k write IOPs, 800MB/s read 200MB/s write |\n\nHow does this translate into real-world performance? Here are average read and write times across our Gitaly fleet:\n\n##### IO performance is much higher\n\nHere are some comparative figures for our Gitaly fleet from Azure and GCP.\nIn each case, the performance in GCP is much better than in Azure, although this is what we would expect given the more powerful fleet.\n\n[![Disk read time graph](https://docs.google.com/spreadsheets/d/e/2PACX-1vQg_tdtdZYoC870W3u2R2icSK0Rd9qoOtDJqYHALaQlzhxXOmfY63X1NMMyFVEypQs7NngR4UUIZx5R/pubchart?oid=458168633&format=image)](https://docs.google.com/spreadsheets/d/1uJ_zacNvJTsvJUfNpi1D_aPBg-vNJC1xJzsSwGKKt8g/edit#gid=1002437172)\n[![Disk write time graph](https://docs.google.com/spreadsheets/d/e/2PACX-1vQg_tdtdZYoC870W3u2R2icSK0Rd9qoOtDJqYHALaQlzhxXOmfY63X1NMMyFVEypQs7NngR4UUIZx5R/pubchart?oid=884528549&format=image)](https://docs.google.com/spreadsheets/d/1uJ_zacNvJTsvJUfNpi1D_aPBg-vNJC1xJzsSwGKKt8g/edit#gid=1002437172)\n[![Disk Queue length graph](https://docs.google.com/spreadsheets/d/e/2PACX-1vQg_tdtdZYoC870W3u2R2icSK0Rd9qoOtDJqYHALaQlzhxXOmfY63X1NMMyFVEypQs7NngR4UUIZx5R/pubchart?oid=2135164979&format=image)](https://docs.google.com/spreadsheets/d/1uJ_zacNvJTsvJUfNpi1D_aPBg-vNJC1xJzsSwGKKt8g/edit#gid=1002437172)\n\nNote: For reference: for Azure, this uses the average times for the week leading up to the failover. For GCP, it's an average for the week up to\nOctober 2, 2018.\n\nThese stats clearly illustrate that our new fleet has far better IO performance than our old cluster. Gitaly performance is highly dependent on\nIO performance, so this is great news and goes a long way to explaining the performance improvements we're seeing.\n\n### Reason #2: Fewer \"unicorn worker saturation\" errors\n\n![HTTP 503 Status\nGitLab](https://about.gitlab.com/images/blogimages/whats-up-with-gitlab-com/facepalm-503.png)\n\nUnicorn worker saturation sounds like it'd be a good thing, but it's really not!\n\nWe ([currently](https://gitlab.com/gitlab-org/gitlab-ce/merge_requests/1899))\nrely on [unicorn](https://bogomips.org/unicorn/), a Ruby/Rack http server, for serving much of the application. Unicorn uses a single-threaded model, which uses a fixed pool of workers processes. Each worker can handle only one request at a time. If the worker gives no response within 60 seconds, it is terminated and another process is spawned to replace it.\n\n> #### _“Unicorn worker saturation sounds like it'd be a good thing, but\nit's really not!”_\n\nAdd to this the lack of autoscaling technologies to ramp the fleet up when we experience high load volumes, and this means that GitLab.com has a relatively static-sized pool of workers to handle incoming requests.\n\nIf a Gitaly server experiences load problems, even fast [RPCs](https://en.wikipedia.org/wiki/Remote_procedure_call) that would normally only take milliseconds, could take up to several seconds to respond\n– thousands of times slower than usual. Requests to the unicorn fleet that communicate with the slow server will take hundreds of times longer than expected. Eventually, most of the fleet is handling requests to that affected backend server. This leads to a queue which affects all incoming traffic, a bit like a tailback on a busy highway caused by a traffic jam on a single offramp.\n\nIf the request gets queued for too long – after about 60 seconds – the request will be cancelled, leading to a 503 error. This is indiscriminate – all requests, whether they interact with the affected server or not, will get cancelled. This is what I call unicorn worker saturation, and it's a very bad thing.\n\nBetween February and August this year we frequently experienced this phenomenon.\n\nThere are several approaches we've taken to dealing with this:\n\n- **Fail fast with aggressive timeouts and circuitbreakers**: Timeouts mean\nthat when a Gitaly request is expected to take a few milliseconds, they time out after a second, rather than waiting for the request to time out after 60 seconds. While some requests will still be affected, the cluster will remain generally healthy. Gitaly currently doesn't use circuitbreakers, but we plan to add this, possibly using [Istio](https://istio.io/docs/tasks/traffic-management/circuit-breaking/)\nonce we've moved to Kubernetes.\n\n- **Better abuse detection and limits**: More often than not, server load\nspikes are driven by users going against our fair usage policies. We built tools to better detect this and over the past few months, an abuse team has been established to deal with this. Sometimes, load is driven through huge repositories, and we're working on reinstating fair-usage limits which prevent 100GB Git repositories from affecting our entire fleet.\n\n- **Concurrency controls and rate limits**: For limiting the blast radius,\nrate limiters (mostly in HAProxy) and concurrency limiters (in Gitaly) slow overzealous users down to protect the fleet as a whole.\n\n### Reason #3: GitLab.com no longer uses NFS for any Git access\n\nIn early September we disabled Git NFS mounts across our worker fleet. This was possible because Gitaly had reached v1.0: the point at which it's sufficiently complete. You can read more about how we got to this stage in our [Road to Gitaly blog post](/blog/the-road-to-gitaly-1-0/).\n\n### Reason #4: Migration as a chance to reduce debt\n\nThe migration was a fantastic opportunity for us to improve our infrastructure, simplify some components, and otherwise make GitLab.com more stable and more observable, for example, we've rolled out new **structured logging infrastructure**.\n\nAs part of the migration, we took the opportunity to move much of our logging across to structured logs. We use [fluentd](https://www.fluentd.org/), [Google\nPub/Sub](https://cloud.google.com/pubsub/docs/overview), [Pubsubbeat](https://github.com/GoogleCloudPlatform/pubsubbeat), storing our logs in [Elastic Cloud](https://www.elastic.co/cloud) and [Google\nStackdriver Logging](https://cloud.google.com/logging/). Having reliable, indexed logs has allowed us to reduce our mean-time to detection of incidents, and in particular detect abuse. This new logging infrastructure has also been invaluable in detecting and resolving several security incidents.\n\n> #### _“This new logging infrastructure has also been invaluable in\ndetecting and resolving several security incidents”_\n\nWe've also focused on making our staging environment much more similar to our production environment. This allows us to test more changes, more accurately, in staging before rolling them out to production. Previously the team was maintaining a limited scaled-down staging environment and many changes were not adequately tested before being rolled out. Our environments now share a common configuration and we're working to automate all [terraform](https://gitlab.com/gitlab-com/gl-infra/infrastructure/issues/5079)\nand [chef](https://gitlab.com/gitlab-com/gl-infra/infrastructure/issues/5078)\nrollouts.\n\n### Reason #5: Process changes\n\nUnfortunately many of the worst outages we've experienced over the past few years have been self-inflicted. We've always been transparent about these — and will continue to be so — but as we rapidly grow, it's important that our processes scale alongside our systems and team.\n\n> #### _“It's important that our processes scale alongside our systems and\nteam”_\n\nIn order to address this, over the past few months, we've formalized our change and incident management processes. These processes respectively help us to avoid outages and resolve them quicker when they do occur.\n\nIf you're interested in finding out more about the approach we've taken to these two vital disciplines, they're published in our handbook:\n\n- [GitLab.com's Change Management\nProcess](https://handbook.gitlab.com/handbook/engineering/infrastructure/change-management/)\n\n- [GitLab.com's Incident Management\nProcess](https://handbook.gitlab.com/handbook/engineering/infrastructure/incident-management/)\n\n### Reason #6: Application improvement\n\nEvery GitLab release includes [performance and stability improvements](https://gitlab.com/gitlab-org/gitlab-ce/issues?scope=all&state=opened&label_name%5B%5D=performance); some of these have had a big impact on GitLab's stability and performance, particularly n+1 issues.\n\nTake Gitaly for example: like other distributed systems, Gitaly can suffer from a class of performance degradations known as \"n+1\" problems. This happens when an endpoint needs to make many queries (_\"n\"_) to fulfill a single request.\n\n> Consider an imaginary endpoint which queried Gitaly for all tags on a\nrepository, and then issued an additional query for each tag to obtain more information. This would result in n + 1 Gitaly queries: one for the initial tag, and then n for the tags. This approach would work fine for a project with 10 tags – issuing 11 requests, but a project with 1000 tags, this would result in 1001 Gitaly calls, each with a round-trip time, and issued in sequence.\n\n![Latency drop in Gitaly endpoints](https://about.gitlab.com/images/blogimages/whats-up-with-gitlab-com/drop-off.png)\n\nUsing data from Pingdom, this chart shows long-term performance trends since the start of the year. It's clear that latency improved a great deal on May 7, 2018. This date happens to coincide with the RC1 release of GitLab 10.8, and its deployment on GitLab.com.\n\nIt turns out that this was due to a [single fix on n+1 on the merge request page being resolved](https://gitlab.com/gitlab-org/gitlab-ce/issues/44052).\n\nWhen running in development or test mode, GitLab now detects n+1 situations and we have compiled [a list of known n+1s](https://gitlab.com/gitlab-org/gitlab-ce/issues?scope=all&utf8=%E2%9C%93&state=opened&label_name[]=performance&label_name[]=Gitaly&label_name[]=technical%20debt).\nAs these are resolved we expect even more performance improvements.\n\n![GitLab Summit - South Africa - 2018](https://about.gitlab.com/images/summits/2018_south-africa_team.jpg)\n\n### Reason #7: Infrastructure team growth and reorganization\n\nAt the start of May 2018, the Infrastructure team responsible for GitLab.com consisted of five engineers.\n\nSince then, we've had a new director join the Infrastructure team, two new managers, a specialist [Postgres\nDBRE](https://gitlab.com/gitlab-com/www-gitlab-com/merge_requests/13778), and four new [SREs](https://handbook.gitlab.com/job-families/engineering/infrastructure/site-reliability-engineer/).\nThe database team has been reorganized to be an embedded part of infrastructure group. We've also brought in [Ongres](https://www.ongres.com/), a specialist Postgres consultancy, to work alongside the team.\n\nHaving enough people in the team has allowed us to be able to split time between on-call, tactical improvements, and longer-term strategic work.\n\nOh, and we're still hiring! If you're interested, check out [our open positions](/jobs/) and choose the Infrastructure Team 😀\n\n## TL;DR: Conclusion\n\n1. GitLab.com is more stable: availability has improved 61 percent since we\nmigrated to GCP\n\n1. GitLab.com is faster: latency has improved since the migration\n\n1. We are totally focused on continuing these improvements, and we're\nbuilding a great team to do it\n\nOne last thing: our Grafana dashboards are open, so if you're interested in digging into our metrics in more detail, visit [dashboards.gitlab.com](https://dashboards.gitlab.com) and explore!\n",[23,24,25,26,27,28],"GKE","google","inside 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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.",[714],"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",[264,616,718],"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":721,"featured":12,"template":13},"how-iit-bombay-students-code-future-with-gitlab",{"content":723,"config":732},{"title":724,"description":725,"authors":726,"heroImage":727,"date":728,"category":9,"tags":729,"body":731},"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.",[714],"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",[616,264,730],"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":733,"featured":31,"template":13},"artois-university-elevates-curriculum-with-gitlab-ultimate-for-education",{"content":735,"config":748},{"category":9,"tags":736,"body":739,"date":740,"updatedDate":741,"heroImage":742,"authors":743,"title":746,"description":747},[737,738,111],"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",[744,745],"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":13,"slug":749},"migration-from-azure-devops-to-gitlab",{"promotions":751},[752,766,777],{"id":753,"categories":754,"header":756,"text":757,"button":758,"image":763},"ai-modernization",[755],"ai-ml","Is AI achieving its promise at scale?","Quiz will take 5 minutes or less",{"text":759,"config":760},"Get your AI maturity score",{"href":761,"dataGaName":762,"dataGaLocation":246},"/assessments/ai-modernization-assessment/","modernization assessment",{"config":764},{"src":765},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/qix0m7kwnd8x2fh1zq49.png",{"id":767,"categories":768,"header":769,"text":757,"button":770,"image":774},"devops-modernization",[730,562],"Are you just managing tools or shipping innovation?",{"text":771,"config":772},"Get your DevOps maturity score",{"href":773,"dataGaName":762,"dataGaLocation":246},"/assessments/devops-modernization-assessment/",{"config":775},{"src":776},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138785/eg818fmakweyuznttgid.png",{"id":778,"categories":779,"header":781,"text":757,"button":782,"image":786},"security-modernization",[780],"security","Are you trading speed for security?",{"text":783,"config":784},"Get your security maturity score",{"href":785,"dataGaName":762,"dataGaLocation":246},"/assessments/security-modernization-assessment/",{"config":787},{"src":788},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/p4pbqd9nnjejg5ds6mdk.png",{"header":790,"blurb":791,"button":792,"secondaryButton":797},"Start building faster today","See what your team can do with the intelligent orchestration platform for DevSecOps.\n",{"text":793,"config":794},"Get your free trial",{"href":795,"dataGaName":53,"dataGaLocation":796},"https://gitlab.com/-/trial_registrations/new?glm_content=default-saas-trial&glm_source=about.gitlab.com/","feature",{"text":498,"config":798},{"href":57,"dataGaName":58,"dataGaLocation":796},1772652069472]