[{"data":1,"prerenderedAt":820},["ShallowReactive",2],{"/en-us/blog/the-road-to-smarter-code-reviewer-recommendations":3,"navigation-en-us":42,"banner-en-us":452,"footer-en-us":462,"blog-post-authors-en-us-Taylor McCaslin":701,"blog-related-posts-en-us-the-road-to-smarter-code-reviewer-recommendations":716,"blog-promotions-en-us":758,"next-steps-en-us":810},{"id":4,"title":5,"authorSlugs":6,"authors":8,"body":10,"category":11,"categorySlug":11,"config":12,"content":16,"date":20,"description":17,"extension":26,"externalUrl":27,"featured":14,"heroImage":19,"isFeatured":14,"meta":28,"navigation":29,"path":30,"publishedDate":20,"rawbody":31,"seo":32,"slug":13,"stem":36,"tagSlugs":37,"tags":40,"template":15,"updatedDate":27,"__hash__":41},"blogPosts/en-us/blog/the-road-to-smarter-code-reviewer-recommendations.yml","The road to smarter code reviewer recommendations",[7],"taylor-mccaslin",[9],"Taylor McCaslin","\nYou may recall back in June 2021, we [announced the acquisition of UnReview](/press/releases/2021-06-02-gitlab-acquires-unreview-machine-learning-capabilities/), a machine learning (ML) based solution for automatically identifying appropriate expert [code reviewers](/stages-devops-lifecycle/create/) and controlling review workloads and distribution of knowledge.\n\nAt the start of the new year we wanted to provide an update on our integration progress and our wider vision of leveraging machine learning to make GitLab's [DevOps Platform](/solutions/devops-platform/) smarter. You see, the acquisition of UnReview also was the initial staffing of [our new ModelOps stage](https://docs.gitlab.com/user/project/ml/).\n\n### Our Newest DevOps Stage\n\nThis new stage, which we’ve named ModelOps, is focused on enabling and empowering data science workloads on GitLab. GitLab ModelOps aims to bring data science into GitLab both within existing features to make them smarter and more intelligent, but also empowering GitLab customers to build and integrate data science workloads within GitLab.\n\nSo what is ModelOps you may wonder? We view ModelOps as an all encompassing term to cover the entire end to end lifecycle of artificial intelligence models. We wanted to set our vision wide to fully cover everything needed to power data science workloads. DataOps is the processing of data workloads (think traditional ELT: extract, load, transform) and MLOps is the building, training, and deployment of machine learning models. If you’re confused don’t worry, it’s a lot to wrap your head around.\n\n![a look at the stages of MLOps](https://about.gitlab.com/images/blogimages/MLops.png)\n\nToday our DevOps Platform helps plan, build, test, secure, deploy, and monitor traditional software. Now we want to extend our DevOps Platform to include AI and ML workloads. If this is interesting to you, be sure to check out our recent Contribute talk where we dive deeper into plans for our ModelOps stage.\n\n\u003C!-- blank line -->\n\u003Cfigure class=\"video_container\">\n  \u003Ciframe src=\"https://www.youtube.com/embed/C08QVI99JLo\" frameborder=\"0\" allowfullscreen=\"true\"> \u003C/iframe>\n\u003C/figure>\n\u003C!-- blank line -->\n\n### UnReview as our first feature\n\nSo what does this have to do with UnReview? Our acquisition of UnReview is going to be our first [AI Assisted](https://docs.gitlab.com/user/gitlab_duo/) group’s feature: suggested reviewers within [GitLab’s existing reviewers experience](/blog/merge-request-reviewers/). Today, a developer in a merge request has to manually choose a reviewer to look at their code. With UnReview we can leverage the contribution history for a project and recommend someone well-suited for code review of your specific changes.\n\nHere’s an early mockup (and it may differ from our final UI) of how we’re thinking about this integration:\n\n![an early mockup of our UI](https://about.gitlab.com/images/blogimages/codereviewmockup.png)\n\nThe UnReview algorithm looks at a variety of data points from your project’s contribution history to suggest an appropriate reviewer. We’re still in the early days of this integration but our initial internal testing shows great suggestions.\n\n### Customer beta coming soon!\n\nThis leads me to a final question, might you want to be one of our first customers to try this new code review experience? In early 2022, we’ll begin a private customer beta of this new functionality. If interested, [fill out this form to express interest](https://docs.google.com/forms/d/e/1FAIpQLScpmCwpwyBr0GrXxBQ6vE02eokclFAs9lFk_g5dcyuGaHqFuQ/viewform). Do note that we can’t accept everyone and we’ll focus initially on customer profiles that are well suited for the initial version of the suggestion algorithm. Our only ask is we’d like to find customers with active projects that have a healthy number of contributors. The model currently works best on larger repositories with lots of contributors where it may not immediately be clear who is an ideal code reviewer.\n\nWe can’t wait for customers to begin using this new reviewer suggestion experience and will be providing more updates in early 2022.\n","devsecops",{"slug":13,"featured":14,"template":15},"the-road-to-smarter-code-reviewer-recommendations",false,"BlogPost",{"title":5,"description":17,"authors":18,"heroImage":19,"date":20,"body":10,"category":11,"tags":21},"Machine learning is coming to GitLab's code review process. Here's what you need to know, and how you can help!",[9],"https://res.cloudinary.com/about-gitlab-com/image/upload/v1749668426/Blog/Hero%20Images/retrospectivesgitlabpost.jpg","2022-01-04",[22,23,24,25],"DevOps","integrations","workflow","AI/ML","yml",null,{},true,"/en-us/blog/the-road-to-smarter-code-reviewer-recommendations","seo:\n  title: The road to smarter code reviewer recommendations\n  description: >-\n    Machine learning is coming to GitLab's code review process. Here's what you\n    need to know, and how you can help!\n  ogTitle: The road to smarter code reviewer recommendations\n  ogDescription: >-\n    Machine learning is coming to GitLab's code review process. Here's what you\n    need to know, and how you can help!\n  noIndex: false\n  ogImage: >-\n    https://res.cloudinary.com/about-gitlab-com/image/upload/v1749668426/Blog/Hero%20Images/retrospectivesgitlabpost.jpg\n  ogUrl: >-\n    https://about.gitlab.com/blog/the-road-to-smarter-code-reviewer-recommendations\n  ogSiteName: https://about.gitlab.com\n  ogType: article\n  canonicalUrls: >-\n    https://about.gitlab.com/blog/the-road-to-smarter-code-reviewer-recommendations\ncontent:\n  title: The road to smarter code reviewer recommendations\n  description: >-\n    Machine learning is coming to GitLab's code review process. Here's what you\n    need to know, and how you can help!\n  authors:\n    - Taylor McCaslin\n  heroImage: >-\n    https://res.cloudinary.com/about-gitlab-com/image/upload/v1749668426/Blog/Hero%20Images/retrospectivesgitlabpost.jpg\n  date: '2022-01-04'\n  body: >\n\n    You may recall back in June 2021, we [announced the acquisition of\n    UnReview](/press/releases/2021-06-02-gitlab-acquires-unreview-machine-learning-capabilities/),\n    a machine learning (ML) based solution for automatically identifying\n    appropriate expert [code reviewers](/stages-devops-lifecycle/create/) and\n    controlling review workloads and distribution of knowledge.\n\n\n    At the start of the new year we wanted to provide an update on our\n    integration progress and our wider vision of leveraging machine learning to\n    make GitLab's [DevOps Platform](/solutions/devops-platform/) smarter. You\n    see, the acquisition of UnReview also was the initial staffing of [our new\n    ModelOps stage](https://docs.gitlab.com/user/project/ml/).\n\n\n    ### Our Newest DevOps Stage\n\n\n    This new stage, which we’ve named ModelOps, is focused on enabling and\n    empowering data science workloads on GitLab. GitLab ModelOps aims to bring\n    data science into GitLab both within existing features to make them smarter\n    and more intelligent, but also empowering GitLab customers to build and\n    integrate data science workloads within GitLab.\n\n\n    So what is ModelOps you may wonder? We view ModelOps as an all encompassing\n    term to cover the entire end to end lifecycle of artificial intelligence\n    models. We wanted to set our vision wide to fully cover everything needed to\n    power data science workloads. DataOps is the processing of data workloads\n    (think traditional ELT: extract, load, transform) and MLOps is the building,\n    training, and deployment of machine learning models. If you’re confused\n    don’t worry, it’s a lot to wrap your head around.\n\n\n    ![a look at the stages of\n    MLOps](https://about.gitlab.com/images/blogimages/MLops.png)\n\n\n    Today our DevOps Platform helps plan, build, test, secure, deploy, and\n    monitor traditional software. Now we want to extend our DevOps Platform to\n    include AI and ML workloads. If this is interesting to you, be sure to check\n    out our recent Contribute talk where we dive deeper into plans for our\n    ModelOps stage.\n\n\n    \u003C!-- blank line -->\n\n    \u003Cfigure class=\"video_container\">\n      \u003Ciframe src=\"https://www.youtube.com/embed/C08QVI99JLo\" frameborder=\"0\" allowfullscreen=\"true\"> \u003C/iframe>\n    \u003C/figure>\n\n    \u003C!-- blank line -->\n\n\n    ### UnReview as our first feature\n\n\n    So what does this have to do with UnReview? Our acquisition of UnReview is\n    going to be our first [AI Assisted](https://docs.gitlab.com/user/gitlab_duo/) group’s feature:\n    suggested reviewers within [GitLab’s existing reviewers\n    experience](/blog/merge-request-reviewers/). Today, a developer in a merge\n    request has to manually choose a reviewer to look at their code. With\n    UnReview we can leverage the contribution history for a project and\n    recommend someone well-suited for code review of your specific changes.\n\n\n    Here’s an early mockup (and it may differ from our final UI) of how we’re\n    thinking about this integration:\n\n\n    ![an early mockup of our\n    UI](https://about.gitlab.com/images/blogimages/codereviewmockup.png)\n\n\n    The UnReview algorithm looks at a variety of data points from your project’s\n    contribution history to suggest an appropriate reviewer. We’re still in the\n    early days of this integration but our initial internal testing shows great\n    suggestions.\n\n\n    ### Customer beta coming soon!\n\n\n    This leads me to a final question, might you want to be one of our first\n    customers to try this new code review experience? In early 2022, we’ll begin\n    a private customer beta of this new functionality. If interested, [fill out\n    this form to express\n    interest](https://docs.google.com/forms/d/e/1FAIpQLScpmCwpwyBr0GrXxBQ6vE02eokclFAs9lFk_g5dcyuGaHqFuQ/viewform).\n    Do note that we can’t accept everyone and we’ll focus initially on customer\n    profiles that are well suited for the initial version of the suggestion\n    algorithm. Our only ask is we’d like to find customers with active projects\n    that have a healthy number of contributors. The model currently works best\n    on larger repositories with lots of contributors where it may not\n    immediately be clear who is an ideal code reviewer.\n\n\n    We can’t wait for customers to begin using this new reviewer suggestion\n    experience and will be providing more updates in early 2022.\n  category: devsecops\n  tags:\n    - DevOps\n    - integrations\n    - workflow\n    - AI/ML\nconfig:\n  slug: the-road-to-smarter-code-reviewer-recommendations\n  featured: false\n  template: 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statement",{"items":691},[692,695,698],{"text":693,"config":694},"Terms",{"href":522,"dataGaName":523,"dataGaLocation":470},{"text":696,"config":697},"Cookies",{"dataGaName":532,"dataGaLocation":470,"id":533,"isOneTrustButton":29},{"text":699,"config":700},"Privacy",{"href":527,"dataGaName":528,"dataGaLocation":470},[702],{"id":703,"title":704,"body":27,"config":705,"content":707,"description":27,"extension":26,"meta":711,"navigation":29,"path":712,"seo":713,"stem":714,"__hash__":715},"blogAuthors/en-us/blog/authors/taylor-mccaslin.yml","Taylor Mccaslin",{"template":706},"BlogAuthor",{"name":9,"config":708},{"headshot":709,"ctfId":710},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1749667996/Blog/Author%20Headshots/tmccaslin-headshot.png","tmccaslin",{},"/en-us/blog/authors/taylor-mccaslin",{},"en-us/blog/authors/taylor-mccaslin","7SPWjJi2CicE7Or_JC0bA95HjfK2vx1NkcbRlLVcgCk",[717,730,744],{"content":718,"config":728},{"title":719,"description":720,"authors":721,"heroImage":723,"date":724,"body":725,"category":11,"tags":726},"Teaching software development the easy way using GitLab","Learn how University of Washington lecturer Stephen G. Dame uses GitLab for Education to manage student assignments, distribute course materials, and provide inline code feedback at scale.\n",[722],"Rod Burns","https://res.cloudinary.com/about-gitlab-com/image/upload/v1749659537/Blog/Hero%20Images/display-article-image-0679-1800x945-fy26.png","2026-04-29","For instructors teaching software development, one of the biggest logistical challenges is assignment distribution and feedback at scale. How do you give large groups of students access to course materials, keep solution code private, and still deliver meaningful, contextual feedback without lots of administrative overhead?\n\nThe **[GitLab for Education program](https://about.gitlab.com/solutions/education/)** provides qualifying institutions with free access to **GitLab Ultimate**, enabling instructors to build professional-grade workflows that mirror real-world software development environments. In this article, you'll learn how Stephen G. Dame, a lecturer in the Computing and Software Systems department at the University of Washington, Bothell, uses simple workflows in GitLab to manage everything from course materials to student feedback across multiple classes.\n\n## From aerospace to academia: Bringing GitLab to the classroom\n\nDame came to academia with years of experience as a chief software engineer at Boeing Commercial Airplanes, where GitLab was used for aerospace projects. As an adjunct professor, he became an early advocate for GitLab within the university, joining the GitLab for Education program to access the full feature set needed to run structured, scalable course workflows.\n\n> **\"GitLab provides the greatest way to organize multiple classes, student assignments, lectures, and code samples through the use of Groups and Subgroups, which I found to be unique to GitLab compared to other repository platforms.\"**\n>\n> - Stephen G. Dame, University of Washington, Bothell\n\n## Set up groups: Build the right structure before writing a line of code\n\nThe foundation of an effective GitLab-based course is a well-planned group hierarchy. GitLab's **[Groups and Subgroups](https://docs.gitlab.com/tutorials/manage_user/#create-the-organization-parent-group-and-subgroups)** allow instructors to model the natural structure of a university department institution, course, and role with precise, inheritable permissions at every level.\n\nDame's structure places the university at the root (`UWTeaching`), with each course occupying its own subgroup (e.g. `css430`). Within each course sit repositories for `lecture-materials` and `code`, alongside dedicated Subgroups for `students` and `graders`. Instructor materials remain private, while student and grader subgroups are configured with controlled permissions so that assignment briefs and solutions are visible only to the right people.\n\n![Screenshot of GitLab group hierarchy — institution, course subgroup, and per-student subgroups](https://res.cloudinary.com/about-gitlab-com/image/upload/v1777463673/dpxfnitv76pdmvcqtgag.png)\n\nPermissions cascade downward through the hierarchy via **Manage > Members**, allowing Dame to add students to a course's `students` subgroup with `Reporter` access and an expiration date tied to the end of the academic quarter. Students can clone and pull from assignment repositories but cannot push — keeping solution code firmly under instructor control.\n\nStudents are guided to set up SSH keys across all their working environments (local machines, cloud shells, virtual machines) so they can clone repositories and receive weekly updates via `git pull`. They copy relevant code into their own private repositories to manage their own version history.\n\n**Tip for large classes:** For larger cohorts, adding students by hand is impractical. GitLab's REST API lets you automate subgroup creation and membership from a list of usernames. Below is a sample Python script that handles this:\n\n```python\n    import gitlab\n    from datetime import datetime\n\n    # Connect to your GitLab instance\n    gl = gitlab.Gitlab('https://gitlab.com', private_token='YOUR_PRIVATE_TOKEN')\n\n    # Target parent group ID (e.g., the ID for \"css430 > students\")\n    parent_group_id = 12345678\n\n    # Set expiration: typically the beginning of the next month after quarter end\n    expiry_date = '2025-01-01'\n\n    # List of collected student usernames\n    student_list = ['alice_css430', 'bob_css430', 'carol_css430', 'dave_css430', 'eve_css430']\n\n    for username in student_list:\n        try:\n            # 1. Create a personal subgroup for the student\n            subgroup = gl.groups.create({\n                'name': username,\n                'path': username,\n                'parent_id': parent_group_id,\n                'visibility': 'private'\n            })\n\n            # 2. Add student to the new subgroup with Expiration\n            user = gl.users.list(username=username)[0]\n            subgroup.members.create({\n                'user_id': user.id,\n                'access_level': gitlab.const.REPORTER_ACCESS,\n                'expires_at': expiry_date\n            })\n            print(f\"Success: Subgroup created and student added for {username}\")\n        except Exception as e:\n            print(f\"Error processing {username}: {e}\")\n```\nThere is also an [open source project that automates class management](https://gitlab.com/edu-docs/class-management-automation) published by GitLab that provides additional tooling for this workflow.\n## Give feedback where the work actually lives\n\nOnce the structure is in place, the feedback workflow is where GitLab's value becomes most apparent to students. Dame asks students to submit assignments by opening a **[merge request](https://docs.gitlab.com/user/project/merge_requests/)** in their repository. This gives instructors an immediate, clean diff of everything the student has written.\n![A GitLab merge request showing inline code comment function for an instructor](https://res.cloudinary.com/about-gitlab-com/image/upload/v1777467468/icclzyglbkwlvfysggbi.png)\nInstructors can click any line of code and leave an **inline comment** — not just flagging what is wrong, but explaining why, and pointing to what to look at next. Students receive this feedback in direct context with their code, which is far more actionable than a comment at the bottom of a submitted document.\n\n## Join GitLab for Education\n\nSetting up your first GitLab assignment takes some initial effort, but once the structure is in place it largely runs itself. The real payoff goes beyond organization: Students graduate having worked daily in an environment that mirrors professional software development, building habits around [version control](https://about.gitlab.com/topics/version-control/) and [code review](https://docs.gitlab.com/development/code_review/) rather than learning them as abstract concepts.\n\nIf you are just getting started, keep it simple. Begin with a single course group, one assignment template, and a basic pipeline. The structure will grow naturally alongside your confidence with the platform.\n\nMake sure to **[sign up for GitLab for Education](https://about.gitlab.com/solutions/education/join/)** so that you and your students can access all top-tier features, including unlimited reviewers on merge requests, additional compute minutes, and expanded storage.\n\n> [Apply to the GitLab for Education program today](https://about.gitlab.com/solutions/education/join/).",[623,727],"open source",{"featured":14,"template":15,"slug":729},"teaching-software-development-the-easy-way-using-gitlab",{"content":731,"config":742},{"description":732,"authors":733,"heroImage":735,"date":736,"title":737,"body":738,"category":11,"tags":739},"AI-generated code is 34% of development work. Discover how to balance productivity gains with quality, reliability, and security.",[734],"Manav Khurana","https://res.cloudinary.com/about-gitlab-com/image/upload/v1767982271/e9ogyosmuummq7j65zqg.png","2026-01-08","AI is reshaping DevSecOps: Attend GitLab Transcend to see what’s next","AI promises a step change in innovation velocity, but most software teams are hitting a wall. According to our latest [Global DevSecOps Report](https://about.gitlab.com/developer-survey/), AI-generated code now accounts for 34% of all development work. Yet 70% of DevSecOps professionals report that AI is making compliance management more difficult, and 76% say agentic AI will create unprecedented security challenges.\n\nThis is the AI paradox: AI accelerates coding, but software delivery slows down as teams struggle to test, secure, and deploy all that code.\n\n## Productivity gains meet workflow bottlenecks\nThe problem isn't AI itself. It's how software gets built today. The traditional DevSecOps lifecycle contains hundreds of small tasks that developers must navigate manually: updating tickets, running tests, requesting reviews, waiting for approvals, fixing merge conflicts, addressing security findings. These tasks drain an average of seven hours per week from every team member, according to our research.\n\nDevelopment teams are producing code faster than ever, but that code still crawls through fragmented toolchains, manual handoffs, and disconnected processes. In fact, 60% of DevSecOps teams use more than five tools for software development overall, and 49% use more than five AI tools. This fragmentation creates collaboration barriers, with 94% of DevSecOps professionals experiencing factors that limit collaboration in the software development lifecycle.\n\nThe answer isn't more tools. It's intelligent orchestration that brings software teams and their AI agents together across projects and release cycles, with enterprise-grade security, governance, and compliance built in.\n\n## Seeking deeper human-AI partnerships\nDevSecOps professionals don't want AI to take over — they want reliable partnerships. The vast majority (82%) say using agentic AI would increase their job satisfaction, and 43% envision an ideal future with a 50/50 split between human and AI contributions. They're ready to trust AI with 37% of their daily tasks without human review, particularly for documentation, test writing, and code reviews.\n\nWhat we heard resoundingly from DevSecOps professionals is that AI won't replace them; rather, it will fundamentally reshape their roles. 83% of DevSecOps professionals believe AI will significantly change their work within five years, and notably, 76% think this will create more engineering jobs, not fewer. As coding becomes easier with AI, engineers who can architect systems, ensure quality, and apply business context will be in high demand.\n\nCritically, 88% agree there are essential human qualities that AI will never fully replace, including creativity, innovation, collaboration, and strategic vision.\n\nSo how can organizations bridge the gap between AI’s promise and the reality of fragmented workflows?\n\n## Join us at GitLab Transcend: Explore how to drive real value with agentic AI\nOn February 10, 2026, GitLab will be hosting Transcend, where we'll reveal how intelligent orchestration transforms AI-powered software development. You'll get a first look at GitLab's upcoming product roadmap and learn how teams are solving real-world challenges by modernizing development workflows with AI.\n\nOrganizations winning in this new era balance AI adoption with security, compliance, and platform consolidation. AI offers genuine productivity gains when implemented thoughtfully — not by replacing human developers, but by freeing DevSecOps professionals to focus on strategic thinking and creative innovation.\n\n[Register for Transcend today](https://about.gitlab.com/events/transcend/virtual/) to secure your spot and discover how intelligent orchestration can help your software teams stay in flow.",[25,740,741],"DevOps platform","security",{"featured":29,"template":15,"slug":743},"ai-is-reshaping-devsecops-attend-gitlab-transcend-to-see-whats-next",{"content":745,"config":756},{"title":746,"description":747,"authors":748,"heroImage":750,"date":751,"body":752,"category":11,"tags":753},"Atlassian ending Data Center as GitLab maintains deployment choice","As Atlassian transitions Data Center customers to cloud-only, GitLab presents a menu of deployment choices that map to business needs.",[749],"Emilio Salvador","https://res.cloudinary.com/about-gitlab-com/image/upload/v1750098354/Blog/Hero%20Images/Blog/Hero%20Images/blog-image-template-1800x945%20%281%29_5XrohmuWBNuqL89BxVUzWm_1750098354056.png","2025-10-07","Change is never easy, especially when it's not your choice. Atlassian's announcement that [all Data Center products will reach end-of-life by March 28, 2029](https://www.atlassian.com/blog/announcements/atlassian-ascend), means thousands of organizations must now reconsider their DevSecOps deployment and infrastructure. But you don't have to settle for deployment options that don't fit your needs. GitLab maintains your freedom to choose — whether you need self-managed for compliance, cloud for convenience, or hybrid for flexibility — all within a single AI-powered DevSecOps platform that respects your requirements.\n\nWhile other vendors force migrations to cloud-only architectures, GitLab remains committed to supporting the deployment choices that match your business needs. Whether you're managing sensitive government data, operating in air-gapped environments, or simply prefer the control of self-managed deployments, we understand that one size doesn't fit all.\n\n## The cloud isn't the answer for everyone\n\nFor the many companies that invested millions of dollars in Data Center deployments, including those that migrated to Data Center [after its Server products were discontinued](https://about.gitlab.com/blog/atlassian-server-ending-move-to-a-single-devsecops-platform/), this announcement represents more than a product sunset. It signals a fundamental shift away from customer-centric architecture choices, forcing enterprises into difficult positions: accept a deployment model that doesn't fit their needs, or find a vendor that respects their requirements.\n\nMany of the organizations requiring self-managed deployments represent some of the world's most important organizations: healthcare systems protecting patient data, financial institutions managing trillions in assets, government agencies safeguarding national security, and defense contractors operating in air-gapped environments.\n\nThese organizations don't choose self-managed deployments for convenience; they choose them for compliance, security, and sovereignty requirements that cloud-only architectures simply cannot meet. Organizations operating in closed environments with restricted or no internet access aren't exceptions — they represent a significant portion of enterprise customers across various industries.\n\n![GitLab vs. Atlassian comparison table](https://res.cloudinary.com/about-gitlab-com/image/upload/v1759928476/ynl7wwmkh5xyqhszv46m.jpg)\n\n## The real cost of forced cloud migration goes beyond dollars\n\nWhile cloud-only vendors frame mandatory migrations as \"upgrades,\" organizations face substantial challenges beyond simple financial costs:\n\n* **Lost integration capabilities:** Years of custom integrations with legacy systems, carefully crafted workflows, and enterprise-specific automations become obsolete. Organizations with deep integrations to legacy systems often find cloud migration technically infeasible.\n\n* **Regulatory constraints:** For organizations in regulated industries, cloud migration isn't just complex — it's often not permitted. Data residency requirements, air-gapped environments, and strict regulatory frameworks don't bend to vendor preferences. The absence of single-tenant solutions in many cloud-only approaches creates insurmountable compliance barriers.\n\n* **Productivity impacts:** Cloud-only architectures often require juggling multiple products: separate tools for planning, code management, CI/CD, and documentation. Each tool means another context switch, another integration to maintain, another potential point of failure. GitLab research shows [30% of developers spend at least 50% of their job maintaining and/or integrating their DevSecOps toolchain](https://about.gitlab.com/developer-survey/). Fragmented architectures exacerbate this challenge rather than solving it.\n\n## GitLab offers choice, commitment, and consolidation\n\nEnterprise customers deserve a trustworthy technology partner. That's why we've committed to supporting a range of deployment options — whether you need on-premises for compliance, hybrid for flexibility, or cloud for convenience, the choice remains yours. That commitment continues with [GitLab Duo](https://about.gitlab.com/gitlab-duo-agent-platform/), our AI solution that supports developers at every stage of their workflow.\n\nBut we offer more than just deployment flexibility. While other vendors might force you to cobble together their products into a fragmented toolchain, GitLab provides everything in a **comprehensive AI-native DevSecOps platform**. Source code management, CI/CD, security scanning, Agile planning, and documentation are all managed within a single application and a single vendor relationship.\n\nThis isn't theoretical. When Airbus and [Iron Mountain](https://about.gitlab.com/customers/iron-mountain/) evaluated their existing fragmented toolchains, they consistently identified challenges: poor user experience, missing functionalities like built-in security scanning and review apps, and management complexity from plugin troubleshooting. **These aren't minor challenges; they're major blockers for modern software delivery.**\n\n## Your migration path: Simpler than you think\n\nWe've helped thousands of organizations migrate from other vendors, and we've built the tools and expertise to make your transition smooth:\n\n* **Automated migration tools:** Our [Bitbucket Server importer](https://docs.gitlab.com/user/import/bitbucket_server/) brings over repositories, pull requests, comments, and even Large File Storage (LFS) objects. For Jira, our [built-in importer](https://docs.gitlab.com/user/project/import/jira/) handles issues, descriptions, and labels, with professional services available for complex migrations.\n\n* **Proven at scale:** A 500 GiB repository with 13,000 pull requests, 10,000 branches, and 7,000 tags is likely to [take just 8 hours to migrate](https://docs.gitlab.com/user/import/bitbucket_server/) from Bitbucket to GitLab using parallel processing.\n\n* **Immediate ROI:** A [Forrester Consulting Total Economic Impact™ study commissioned by GitLab](https://about.gitlab.com/resources/study-forrester-tei-gitlab-ultimate/) found that investing in GitLab Ultimate confirms these benefits translate to real bottom-line impact, with a three-year 483% ROI, 5x time saved in security related activities, and 25% savings in software toolchain costs.\n\n## Start your journey to a unified DevSecOps platform\n\nForward-thinking organizations aren't waiting for vendor-mandated deadlines. They're evaluating alternatives now, while they have time to migrate thoughtfully to platforms that protect their investments and deliver on promises.\n\nOrganizations invest in self-managed deployments because they need control, compliance, and customization. When vendors deprecate these capabilities, they remove not just features but the fundamental ability to choose environments matching business requirements.\n\nModern DevSecOps platforms should offer complete functionality that respects deployment needs, consolidates toolchains, and accelerates software delivery, without forcing compromises on security or data sovereignty.\n\n[Talk to our sales team](https://about.gitlab.com/sales/) today about your migration options, or explore our [comprehensive migration resources](https://about.gitlab.com/move-to-gitlab-from-atlassian/) to see how thousands of organizations have already made the switch.\n\nYou also can [try GitLab Ultimate with GitLab Duo Enterprise](https://about.gitlab.com/free-trial/devsecops/) for free for 30 days to see what a unified DevSecOps platform can do for your organization.",[574,567,754,755],"product","features",{"featured":29,"template":15,"slug":757},"atlassian-ending-data-center-as-gitlab-maintains-deployment-choice",{"promotions":759},[760,774,785,796],{"id":761,"categories":762,"header":764,"text":765,"button":766,"image":771},"ai-modernization",[763],"ai-ml","Is AI achieving its promise at scale?","Quiz will take 5 minutes or less",{"text":767,"config":768},"Get your AI maturity score",{"href":769,"dataGaName":770,"dataGaLocation":245},"/assessments/ai-modernization-assessment/","modernization assessment",{"config":772},{"src":773},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/qix0m7kwnd8x2fh1zq49.png",{"id":775,"categories":776,"header":777,"text":765,"button":778,"image":782},"devops-modernization",[754,11],"Are you just managing tools or shipping innovation?",{"text":779,"config":780},"Get your DevOps maturity score",{"href":781,"dataGaName":770,"dataGaLocation":245},"/assessments/devops-modernization-assessment/",{"config":783},{"src":784},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138785/eg818fmakweyuznttgid.png",{"id":786,"categories":787,"header":788,"text":765,"button":789,"image":793},"security-modernization",[741],"Are you trading speed for security?",{"text":790,"config":791},"Get your security maturity score",{"href":792,"dataGaName":770,"dataGaLocation":245},"/assessments/security-modernization-assessment/",{"config":794},{"src":795},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/p4pbqd9nnjejg5ds6mdk.png",{"id":797,"paths":798,"header":801,"text":802,"button":803,"image":808},"github-azure-migration",[799,800],"migration-from-azure-devops-to-gitlab","integrating-azure-devops-scm-and-gitlab","Is your team ready for GitHub's Azure move?","GitHub is already rebuilding around Azure. Find out what it means for you.",{"text":804,"config":805},"See how GitLab compares to GitHub",{"href":806,"dataGaName":807,"dataGaLocation":245},"/compare/gitlab-vs-github/github-azure-migration/","github azure migration",{"config":809},{"src":784},{"header":811,"blurb":812,"button":813,"secondaryButton":818},"Start building faster today","See what your team can do with the intelligent orchestration platform for DevSecOps.\n",{"text":814,"config":815},"Get your free trial",{"href":816,"dataGaName":53,"dataGaLocation":817},"https://gitlab.com/-/trial_registrations/new?glm_content=default-saas-trial&glm_source=about.gitlab.com/","feature",{"text":508,"config":819},{"href":57,"dataGaName":58,"dataGaLocation":817},1777493607302]