[{"data":1,"prerenderedAt":818},["ShallowReactive",2],{"/en-us/blog/introducing-product-analytics-in-gitlab":3,"navigation-en-us":39,"banner-en-us":450,"footer-en-us":460,"blog-post-authors-en-us-Sam Kerr":701,"blog-related-posts-en-us-introducing-product-analytics-in-gitlab":715,"blog-promotions-en-us":756,"next-steps-en-us":808},{"id":4,"title":5,"authorSlugs":6,"authors":8,"body":10,"category":11,"categorySlug":11,"config":12,"content":16,"date":20,"description":17,"extension":25,"externalUrl":26,"featured":14,"heroImage":19,"isFeatured":14,"meta":27,"navigation":28,"path":29,"publishedDate":20,"rawbody":30,"seo":31,"slug":13,"stem":35,"tagSlugs":36,"tags":37,"template":15,"updatedDate":26,"__hash__":38},"blogPosts/en-us/blog/introducing-product-analytics-in-gitlab.yml","Product Analytics: A sneak peek at our upcoming feature",[7],"sam-kerr",[9],"Sam Kerr","\n\nProduct analytics are important to understand how your users engage with your application so that you can make data-driven decisions. Identifying features that your users make heavy use of and which they don’t can provide signals to teams on where and how to spend their time most effectively. Without product data, we must use one-off anecdotes or opinions, which can be subject to incorrect assumptions, internal biases, or are missing key details. At the same time, instrumenting applications and processing this data can be challenging, which leads many teams to not do it.\n\nAt GitLab, we view this workflow of instrumenting the app, collecting data, and processing it to gain insights as a key piece of the DevSecOps lifecycle. For this reason, we are working on adding Product Analytics capabilities to our platform so you’ll be able to take advantage of them in your own apps. You will be able to instrument features you have built, see how users engage with them, and make decisions using that data – all within GitLab.\n\nIn this blog, you'll learn more details on what our vision is, what we are working on, our future plans, and how you can contribute and engage with us.\n\n## What is Product Analytics?\n\nWe have a broad vision for what we want to achieve, which we outline in our product direction page. The short version is that we want to enable developers to easily add instrumentation to their applications, provide infrastructure to receive and process it, run experiments, and enable consumers of the data, such as product managers or developers, to use GitLab to gain insights that will help them make even better products.\n\nOur initial focus is on web applications, primarily those built with JavaScript and Ruby on Rails. Longer term, we want to add functionality like experiments and support for other web frameworks and additional tech stacks.\n\n\u003C!-- blank line -->\n\u003Cfigure class=\"video_container\">\n  \u003Ciframe src=\"https://www.youtube.com/embed/jG42hesT030\" frameborder=\"0\" allowfullscreen=\"true\"> \u003C/iframe>\n\u003C/figure>\n\u003C!-- blank line -->\n\nWe plan to use several open-source technologies to make this happen: [Snowplow](http://www.snowplow.io), [ClickHouse](https://clickhouse.com/), [Cube.dev](https://cube.dev/), and [ECharts](https://echarts.apache.org/en/index.html) for instrumentation, data storage, and data visualization, respectively. Each of these projects is great at what they do and we are excited to build with them.\n\n## How to configure Product Analytics\n\nOnce publicly available, Product Analytics will need access to a Kubernetes cluster running these applications. GitLab will be able to create and manage this cluster for you or you will be able to provide your own cluster. That cluster will then process, store, and transform your data and display it in relevant GitLab screens. You will then instrument your application’s features with one of our [client-side SDKs](https://gitlab.com/gitlab-org/analytics-section/product-analytics/gl-application-sdk-js).\n\nWith your app instrumented and your Product Analytics cluster set up, you will be able to access reports and dashboards within GitLab to explore the data that is reported. You'll be able to identify usage trends and better understand your users so that you can make improvements in future versions of your product.\n\nWe anticipate that one of the unique differentiators GitLab will have with Product Analytics is that all of the dashboard and visualization configurations will be driven by files in your GitLab project. You will be able to collaborate with your team using [merge requests](https://docs.gitlab.com/user/project/merge_requests/), look at previous versions to understand changes, and set up controls over who can make changes – just like you can with code.\n\n![Product Analytics dashboard](https://about.gitlab.com/images/blogimages/productanalyticsingitlab/productanalytics2.png)\n\n## We are customer zero\nOne of GitLab’s values is [dogfooding](https://handbook.gitlab.com/handbook/values/#dogfooding) and using our own product. This helps us better understand our users’ pain points and to find where we should make improvements more quickly. We are already dogfooding what we have built in Product Analytics so far.\n\nWe added Product Analytics to our internal handbook several months ago and have learned a lot about Product Analytics and how team members use the internal handbook.\n\nInstrumenting the internal handbook helped us work through the user experience for Product Analytics. We built out a workflow in GitLab to configure the cluster, view the Product Analytics dashboards, and view the content on them. This showed us what it would be like to instrument a real application. The steps we had difficulty doing showed what users would also likely have difficulty doing and, therefore, were an indication to focus on fixing those.\n\nOnce we had instrumented the handbook, we learned a few things we expected, such as people use the internal handbook primarily on weekdays and we see a massive dropoff in usage on weekends. One thing we didn’t expect was understanding which pages were the most viewed from the handbook. For example, we have various meetings to review [performance indicators](https://handbook.gitlab.com/handbook/product/#product-performance-indicators) and we saw large spikes in usage for relevant pages when those meetings occurred.\n\n![Screenshot showing spikes](https://about.gitlab.com/images/blogimages/productanalyticsingitlab/productanalytics1.png)\n\n## Our continued commitment to user privacy\n\nWe know that analytics offerings raise questions about user privacy and how data is being managed. Your data is your data. We want to build Product Analytics from the beginning so that you can respect the privacy of users. We are taking a few steps to accomplish this.\n\n* Product Analytics was designed to honor commonly recognized opt-out signals. That means users browsing the app will not have their activity recorded or analyzed by Product Analytics when an opt-out signal is received. Opt-out signals are becoming more common as a way of respecting privacy and we are excited to use them.\n* We are designing Product Analytics from the beginning to give you full control over the data you collect, rather than requiring it to be sent to a third-party service. Recall how you will have to provide a Kubernetes cluster with Snowplow, ClickHouse, and Cube.dev – you can provide your own cluster and GitLab can connect to it or we can host the cluster for you. In all cases, the data is yours – GitLab will not use this data beyond Product Analytics features and will not sell nor examine it.\n\n## What’s next?\n\nWe’re excited for the future of Product Analytics and to provide a way for you to learn even more about your users. Our near-term plans are to take what we have built so far and learn what improvements are needed to make it production ready for you to use. We are also working to give you a variety of options on how to best display Product Analytics data within GitLab so that it is easy for you to get started and to explore your data.\n\n## We’d love to hear from you\n\nAs we move forward with Product Analytics, we would love to hear your thoughts, comments, and questions. We have created [this Product Analytics feedback issue](https://gitlab.com/gitlab-org/gitlab/-/issues/391970) if you want to start a discussion there.\n\nWe plan to release Product Analytics iteratively and will start with a small group of existing customers. If you are interested in previewing Product Analytics before it is generally available, please fill out [our contact form](https://forms.gle/3Q3srimfqpM4WCKM8).\n\n*Disclaimer: This blog contains information related to upcoming products, features, and functionality. It is important to note that the information in this blog post is for informational purposes only. Please do not rely on this information for purchasing or planning purposes. As with all projects, the items mentioned in this blog and linked pages are subject to change or delay. The development, release, and timing of any products, features, or functionality remain at the sole discretion of GitLab.*\n","devsecops",{"slug":13,"featured":14,"template":15},"introducing-product-analytics-in-gitlab",false,"BlogPost",{"title":5,"description":17,"authors":18,"heroImage":19,"date":20,"body":10,"category":11,"tags":21},"Our journey to add Product Analytics into the DevSecOps platform.",[9],"https://res.cloudinary.com/about-gitlab-com/image/upload/v1749667086/Blog/Hero%20Images/blog-compliance.jpg","2023-03-27",[22,23,24],"news","product","features","yml",null,{},true,"/en-us/blog/introducing-product-analytics-in-gitlab","seo:\n  title: 'Product Analytics: A sneak peek at our upcoming feature'\n  description: Our journey to add Product Analytics into the DevSecOps platform.\n  ogTitle: 'Product Analytics: A sneak peek at our upcoming feature'\n  ogDescription: Our journey to add Product Analytics into the DevSecOps platform.\n  noIndex: false\n  ogImage: >-\n    https://res.cloudinary.com/about-gitlab-com/image/upload/v1749667086/Blog/Hero%20Images/blog-compliance.jpg\n  ogUrl: https://about.gitlab.com/blog/introducing-product-analytics-in-gitlab\n  ogSiteName: https://about.gitlab.com\n  ogType: article\n  canonicalUrls: https://about.gitlab.com/blog/introducing-product-analytics-in-gitlab\ncontent:\n  title: 'Product Analytics: A sneak peek at our upcoming feature'\n  description: Our journey to add Product Analytics into the DevSecOps platform.\n  authors:\n    - Sam Kerr\n  heroImage: >-\n    https://res.cloudinary.com/about-gitlab-com/image/upload/v1749667086/Blog/Hero%20Images/blog-compliance.jpg\n  date: '2023-03-27'\n  body: \"\n\n\n    Product analytics are important to understand how your users engage with\n    your application so that you can make data-driven decisions. Identifying\n    features that your users make heavy use of and which they don’t can provide\n    signals to teams on where and how to spend their time most effectively.\n    Without product data, we must use one-off anecdotes or opinions, which can\n    be subject to incorrect assumptions, internal biases, or are missing key\n    details. At the same time, instrumenting applications and processing this\n    data can be challenging, which leads many teams to not do it.\n\n\n    At GitLab, we view this workflow of instrumenting the app, collecting data,\n    and processing it to gain insights as a key piece of the DevSecOps\n    lifecycle. For this reason, we are working on adding Product Analytics\n    capabilities to our platform so you’ll be able to take advantage of them in\n    your own apps. You will be able to instrument features you have built, see\n    how users engage with them, and make decisions using that data – all within\n    GitLab.\n\n\n    In this blog, you'll learn more details on what our vision is, what we are\n    working on, our future plans, and how you can contribute and engage with us.\n\n\n    ## What is Product Analytics?\n\n\n    We have a broad vision for what we want to achieve, which we outline in our\n    product direction page. The\n    short version is that we want to enable developers to easily add\n    instrumentation to their applications, provide infrastructure to receive and\n    process it, run experiments, and enable consumers of the data, such as\n    product managers or developers, to use GitLab to gain insights that will\n    help them make even better products.\n\n\n    Our initial focus is on web applications, primarily those built with\n    JavaScript and Ruby on Rails. Longer term, we want to add functionality like\n    experiments and support for other web frameworks and additional tech stacks.\n\n\n    \u003C!-- blank line -->\n\n    \u003Cfigure class=\\\"video_container\\\">\n\n    \\  \u003Ciframe src=\\\"https://www.youtube.com/embed/jG42hesT030\\\"\n    frameborder=\\\"0\\\" allowfullscreen=\\\"true\\\"> \u003C/iframe>\n\n    \u003C/figure>\n\n    \u003C!-- blank line -->\n\n\n    We plan to use several open-source technologies to make this happen:\n    [Snowplow](http://www.snowplow.io), [ClickHouse](https://clickhouse.com/),\n    [Cube.dev](https://cube.dev/), and\n    [ECharts](https://echarts.apache.org/en/index.html) for instrumentation,\n    data storage, and data visualization, respectively. Each of these projects\n    is great at what they do and we are excited to build with them.\n\n\n    ## How to configure Product Analytics\n\n\n    Once publicly available, Product Analytics will need access to a Kubernetes\n    cluster running these applications. GitLab will be able to create and manage\n    this cluster for you or you will be able to provide your own cluster. That\n    cluster will then process, store, and transform your data and display it in\n    relevant GitLab screens. You will then instrument your application’s\n    features with one of our [client-side\n    SDKs](https://gitlab.com/gitlab-org/analytics-section/product-analytics/gl-\\\n    application-sdk-js).\n\n\n    With your app instrumented and your Product Analytics cluster set up, you\n    will be able to access reports and dashboards within GitLab to explore the\n    data that is reported. You'll be able to identify usage trends and better\n    understand your users so that you can make improvements in future versions\n    of your product.\n\n\n    We anticipate that one of the unique differentiators GitLab will have with\n    Product Analytics is that all of the dashboard and visualization\n    configurations will be driven by files in your GitLab project. You will be\n    able to collaborate with your team using [merge\n    requests](https://docs.gitlab.com/user/project/merge_requests/), look at\n    previous versions to understand changes, and set up controls over who can\n    make changes – just like you can with code.\n\n\n    ![Product Analytics\n    dashboard](https://about.gitlab.com/images/blogimages/productanalyticsingit\\\n    lab/productanalytics2.png)\n\n\n    ## We are customer zero\\\n\n\n    One of GitLab’s values is\n    [dogfooding](https://handbook.gitlab.com/handbook/values/#dogfooding) and\n    using our own product. This helps us better understand our users’ pain\n    points and to find where we should make improvements more quickly. We are\n    already dogfooding what we have built in Product Analytics so far.\n\n\n    We added Product Analytics to our internal handbook several months ago and\n    have learned a lot about Product Analytics and how team members use the\n    internal handbook.\n\n\n    Instrumenting the internal handbook helped us work through the user\n    experience for Product Analytics. We built out a workflow in GitLab to\n    configure the cluster, view the Product Analytics dashboards, and view the\n    content on them. This showed us what it would be like to instrument a real\n    application. The steps we had difficulty doing showed what users would also\n    likely have difficulty doing and, therefore, were an indication to focus on\n    fixing those.\n\n\n    Once we had instrumented the handbook, we learned a few things we expected,\n    such as people use the internal handbook primarily on weekdays and we see a\n    massive dropoff in usage on weekends. One thing we didn’t expect was\n    understanding which pages were the most viewed from the handbook. For\n    example, we have various meetings to review [performance\n    indicators](https://handbook.gitlab.com/handbook/product/#product-performan\\\n    ce-indicators) and we saw large spikes in usage for relevant pages when\n    those meetings occurred.\n\n\n    ![Screenshot showing\n    spikes](https://about.gitlab.com/images/blogimages/productanalyticsingitlab\\\n    /productanalytics1.png)\n\n\n    ## Our continued commitment to user privacy\n\n\n    We know that analytics offerings raise questions about user privacy and how\n    data is being managed. Your data is your data. We want to build Product\n    Analytics from the beginning so that you can respect the privacy of users.\n    We are taking a few steps to accomplish this.\n\n\n    * Product Analytics was designed to honor commonly recognized opt-out\n    signals. That means users browsing the app will not have their activity\n    recorded or analyzed by Product Analytics when an opt-out signal is\n    received. Opt-out signals are becoming more common as a way of respecting\n    privacy and we are excited to use them.\n\n    * We are designing Product Analytics from the beginning to give you full\n    control over the data you collect, rather than requiring it to be sent to a\n    third-party service. Recall how you will have to provide a Kubernetes\n    cluster with Snowplow, ClickHouse, and Cube.dev – you can provide your own\n    cluster and GitLab can connect to it or we can host the cluster for you. In\n    all cases, the data is yours – GitLab will not use this data beyond Product\n    Analytics features and will not sell nor examine it.\n\n\n    ## What’s next?\n\n\n    We’re excited for the future of Product Analytics and to provide a way for\n    you to learn even more about your users. Our near-term plans are to take\n    what we have built so far and learn what improvements are needed to make it\n    production ready for you to use. We are also working to give you a variety\n    of options on how to best display Product Analytics data within GitLab so\n    that it is easy for you to get started and to explore your data.\n\n\n    ## We’d love to hear from you\n\n\n    As we move forward with Product Analytics, we would love to hear your\n    thoughts, comments, and questions. We have created [this Product Analytics\n    feedback issue](https://gitlab.com/gitlab-org/gitlab/-/issues/391970) if you\n    want to start a discussion there.\n\n\n    We plan to release Product Analytics iteratively and will start with a small\n    group of existing customers. If you are interested in previewing Product\n    Analytics before it is generally available, please fill out [our contact\n    form](https://forms.gle/3Q3srimfqpM4WCKM8).\n\n\n    *Disclaimer: This blog contains information related to upcoming products, features, and functionality. It is important to note that the information in this blog post is for informational purposes only. Please do not rely on this information for purchasing or planning purposes. As with all projects, the items mentioned in this blog and linked pages are subject to change or delay. 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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",[721],"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/).",[623,726],"open source",{"featured":14,"template":15,"slug":728},"teaching-software-development-the-easy-way-using-gitlab",{"content":730,"config":742},{"description":731,"authors":732,"heroImage":734,"date":735,"title":736,"body":737,"category":11,"tags":738},"AI-generated code is 34% of development work. Discover how to balance productivity gains with quality, reliability, and security.",[733],"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.",[739,740,741],"AI/ML","DevOps platform","security",{"featured":28,"template":15,"slug":743},"ai-is-reshaping-devsecops-attend-gitlab-transcend-to-see-whats-next",{"content":745,"config":754},{"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.",[574,567,23,24],{"featured":28,"template":15,"slug":755},"atlassian-ending-data-center-as-gitlab-maintains-deployment-choice",{"promotions":757},[758,772,783,794],{"id":759,"categories":760,"header":762,"text":763,"button":764,"image":769},"ai-modernization",[761],"ai-ml","Is AI achieving its promise at scale?","Quiz will take 5 minutes or less",{"text":765,"config":766},"Get your AI maturity score",{"href":767,"dataGaName":768,"dataGaLocation":243},"/assessments/ai-modernization-assessment/","modernization assessment",{"config":770},{"src":771},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/qix0m7kwnd8x2fh1zq49.png",{"id":773,"categories":774,"header":775,"text":763,"button":776,"image":780},"devops-modernization",[23,11],"Are you just managing tools or shipping innovation?",{"text":777,"config":778},"Get your DevOps maturity score",{"href":779,"dataGaName":768,"dataGaLocation":243},"/assessments/devops-modernization-assessment/",{"config":781},{"src":782},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138785/eg818fmakweyuznttgid.png",{"id":784,"categories":785,"header":786,"text":763,"button":787,"image":791},"security-modernization",[741],"Are you trading speed for security?",{"text":788,"config":789},"Get your security maturity score",{"href":790,"dataGaName":768,"dataGaLocation":243},"/assessments/security-modernization-assessment/",{"config":792},{"src":793},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/p4pbqd9nnjejg5ds6mdk.png",{"id":795,"paths":796,"header":799,"text":800,"button":801,"image":806},"github-azure-migration",[797,798],"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":802,"config":803},"See how GitLab compares to GitHub",{"href":804,"dataGaName":805,"dataGaLocation":243},"/compare/gitlab-vs-github/github-azure-migration/","github azure migration",{"config":807},{"src":782},{"header":809,"blurb":810,"button":811,"secondaryButton":816},"Start building faster today","See what your team can do with the intelligent orchestration platform for DevSecOps.\n",{"text":812,"config":813},"Get your free trial",{"href":814,"dataGaName":50,"dataGaLocation":815},"https://gitlab.com/-/trial_registrations/new?glm_content=default-saas-trial&glm_source=about.gitlab.com/","feature",{"text":506,"config":817},{"href":54,"dataGaName":55,"dataGaLocation":815},1777493642249]