[{"data":1,"prerenderedAt":806},["ShallowReactive",2],{"/en-us/blog/5-gitlab-premium-features-to-help-your-team-scale":3,"navigation-en-us":37,"banner-en-us":447,"footer-en-us":457,"blog-post-authors-en-us-Julie Griffin":698,"blog-related-posts-en-us-5-gitlab-premium-features-to-help-your-team-scale":712,"blog-promotions-en-us":743,"next-steps-en-us":796},{"id":4,"title":5,"authorSlugs":6,"body":8,"categorySlug":9,"config":10,"content":14,"description":8,"extension":25,"isFeatured":12,"meta":26,"navigation":27,"path":28,"publishedDate":20,"seo":29,"stem":33,"tagSlugs":34,"__hash__":36},"blogPosts/en-us/blog/5-gitlab-premium-features-to-help-your-team-scale.yml","5 Gitlab Premium Features To Help Your Team Scale",[7],"julie-griffin",null,"product",{"slug":11,"featured":12,"template":13},"5-gitlab-premium-features-to-help-your-team-scale",false,"BlogPost",{"title":15,"description":16,"authors":17,"heroImage":19,"date":20,"body":21,"category":9,"tags":22},"5 GitLab Premium features to help your team scale","Explore how GitLab Premium boosts team collaboration and productivity, enabling organizations to scale with streamlined workflows and advanced capabilities.",[18],"Julie Griffin","https://res.cloudinary.com/about-gitlab-com/image/upload/v1749665151/Blog/Hero%20Images/blog-image-template-1800x945__27_.png","2024-12-18","As development teams grow, what once worked for a small team often becomes a bottleneck. Code standards become inconsistent, operational silos develop, and technical debt accumulates faster. What was a well-oiled machine is now dysfunctional as more team members, projects, and tools are added on.\n\nMany teams experience these challenges as they grow, but how you handle and address these growing pains can save you time, energy, and money in the long run. In this article, we’ll explore the common pitfalls growing teams face and how successful organizations address them.\n\n## 1. Consistent code quality\n\nOne of the challenges growing teams face is [maintaining consistent code quality](https://about.gitlab.com/blog/transform-code-quality-and-compliance-with-automated-processes/) as more developers contribute to the codebase. Quality issues that were once caught quickly now take longer to identify and fix.\n\nSuccessful teams address these challenges through automated code analysis throughout their development workflow. Instead of relying solely on manual reviews, they implement systems to identify potential issues and enforce consistent standards before code even reaches human reviewers. This approach helps detect complexity issues early and flags potential security vulnerabilities, allowing reviewers to focus on more strategic aspects of code review.\n\n### Features that maintain consistent code quality\n\n* Start by automating code analysis in your workflow. With GitLab Premium, you can set up [Code Quality Reports](https://docs.gitlab.com/ee/ci/testing/code_quality.html) in your merge requests. This helps catch issues early by analyzing code complexity and quality before review begins. For example, when a developer submits changes that might increase technical debt, the report will flag these issues automatically.\n* Next, establish automated quality standards. Configure Quality Gates to define what \"good code\" means for your team. This could include test coverage requirements, complexity limits, or specific coding patterns. When code doesn't meet these standards, merges are automatically blocked until issues are addressed.\n* Finally, prevent issues before they even reach review. [Push Rules](https://docs.gitlab.com/ee/user/project/repository/push_rules.html) let you enforce standards right at commit time. You might start with simple rules like requiring certain commit message formats, then gradually add more sophisticated checks as your team adapts.\n\n## 2. Improve collaboration and productivity\n\nThe priorities for startups are often budget and speed, but as businesses grow, tracking DevSecOps workflows across a patchwork of tools can actually deter productivity.\n\nDisparate tools cause developers to context switch between platforms, decreasing focus time and development speed. Toolchain sprawl also limits visibility among teams, creating operational silos that lead to miscommunication.\n\nTo address these challenges, teams often turn to Agile solutions to help with project management, align timelines, and improve cross-team collaboration. When combined with a DevSecOps environment, [Agile](https://about.gitlab.com/topics/agile-devsecops/) creates a powerful system for software development that marries the iterative Agile approach with a security-first mindset.\n\n### Features that improve collaboration and productivity\n\n* With GitLab Premium, teams can access enterprise-grade Agile tools within their DevSecOps platforms. You can start by creating [groups and projects](http://%20epics), assigning team members roles, and determining their level of permission.\n* [Milestones](https://docs.gitlab.com/ee/user/project/milestones/) and [epics](https://docs.gitlab.com/ee/user/group/epics/index.html) help teams plan large-scale initiatives across multiple projects to track dependencies, progress, and align on deliverables. This gives everyone clear visibility into the process.\n* Then, dive deeper into each task with [issues](https://docs.gitlab.com/ee/user/project/issue_board.html). With customizable workflows and multi-assignee capabilities, teams can visualize project progress, dynamically adjust priorities, and collaborate on issue resolution.\n\n## 3. Increase deployment velocity\n\nIn theory, teams should be more productive as they scale. However, if tools aren’t updated to accommodate a growing team, the CI/CD pipeline can feel clunky and inefficient.\n\nTeams turn to tools that help them automate and optimize the [CI/CD pipeline](https://about.gitlab.com/topics/ci-cd/cicd-pipeline/). By automating components like code reviews, merge trains, and permissions, teams can streamline the CI/CD pipeline and improve deployment speed.\n\n### Features that increase deployment velocity\n\nGitLab Premium offers advanced features that help you build, maintain, deploy, and monitor complex pipelines. Increase the speed of deployment through the CI/CD pipeline with more control over code reviews and merge request processes.\n\n* You can automate the merging of multiple changes in a controlled sequence with Merge Trains. This reduces integration issues and improves deployment efficiency.\n* Gain visibility into whether your jobs passed or failed with Multi-Project Pipeline Graphs. Access all related jobs for a single commit and the net result of each stage of your pipeline to quickly see what failed and fix it.\n* Team leaders can access comprehensive insights and make data-informed decisions with [Code Review Analytics](https://docs.gitlab.com/ee/user/analytics/code_review_analytics.html) that provide detailed metrics and merge request analytics. This helps teams identify bottlenecks, optimize review cycles, and establish data-driven process improvements.\n\n## 4. Enhance security and compliance controls\n\nWithout rigorous governance policies, inefficient and insecure code may be released. With smaller companies, security reviews are often manual and the reviews often take a backseat to speed. This can lead to teams releasing incorrect or unsafe code to production causing costly delays.\n\nTo [evolve their security practices](https://about.gitlab.com/blog/3-signs-your-team-is-ready-to-uplevel-security-controls-in-gitlab/), teams turn to stricter access controls, a more refined and delineated review process, as well as features that enable teams to review and track changes.\n\n### Features that enhance security and compliance controls\n\n* With stricter access controls, such as [Protected Branches and Protected Environments](https://docs.gitlab.com/ee/user/project/repository/branches/protected.html), you can restrict push and merge access, securing those areas from unwanted changes by unauthorized users.\n* To strengthen security review processes, implement [Multiple Approvers in Merge Requests](https://docs.gitlab.com/ee/user/project/merge_requests/approvals/). This requires team members to review and approve code changes before they’re pushed through.\n* Review who performed a certain action within the repository and at what time with [Audit Events](https://docs.gitlab.com/ee/administration/audit_event_reports.html). By tracking changes, you’re able to stay on top of compliance requirements.\n\n## 5. Avoid downtime and delays\n\nWithout support, teams are left to troubleshoot issues themselves. This can lead to major delays or periods of downtime where the company is unable to deliver value. As companies grow, this downtime becomes more and more detrimental to the business.\n\nIt’s important to evaluate what your company’s threshold is for downtime. When the value of the downtime outweighs the cost of support, it’s time to scale your DevSecOps platform to meet those needs.\n\n### Support services to avoid downtime and delays\n\nWith GitLab Premium, customers of both SaaS and self-managed instances have access to [Priority Support](https://support.gitlab.com/hc/en-us/articles/11626483177756-GitLab-Support#priority-support). GitLab customer support offers Tiered Support response times, ranging from emergency to low-impact services, and can help you resolve issues quickly, minimizing downtime and disruption to your development cycle.\n\nPlus, for self-managed customers moving to Premium, GitLab offers support for any issues that occur after implementation and upgrade assistance to provide a seamless transition.\n\n## Build today, scale for tomorrow with GitLab Premium\n\nInstead of struggling with the challenges that growing teams face, scale your DevSecOps platform with GitLab Premium.\n\nGitLab Premium provides teams with the project management, pipeline tools, security, and support needed to work efficiently and effectively across the software development lifecycle.\n\n> #### Learn more about [why you should upgrade to GitLab Premium](https://about.gitlab.com/pricing/premium/why-upgrade/).",[23,24],"DevSecOps 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18.11: Budget guardrails for GitLab Credits","Learn how new spending caps and per-user credit limits give organizations the budget guardrails to scale GitLab Duo Agent Platform.",[718],"Bryan Rothwell","https://res.cloudinary.com/about-gitlab-com/image/upload/v1776259080/cakqnwo5ecp255lo8lzo.png","2026-04-16","Teams using GitLab Duo Agent Platform with on-demand GitLab Credits are shipping faster, catching bugs earlier, and automating tasks that used to take entire sprints. But as adoption grows, so does oversight from finance, procurement, and platform teams to prove that AI spending is bounded, predictable, and controllable.\n\nOne of the greatest barriers to broader AI adoption isn't skepticism about the technology. It's uncertainty about managing spend. Without budget caps, a busy month could produce unexpected expenses. Without per-user limits, a handful of power users could burn through the team's credits before the month is over. And without either, engineering leaders who want to expand their use of agentic AI for software development have to jump through more hoops for budget approval.\n\nSince its [general availability](https://about.gitlab.com/blog/gitlab-duo-agent-platform-is-generally-available/), GitLab Duo Agent Platform has provided usage governance and visibility. With GitLab 18.11, we're introducing usage controls for [GitLab Credits](https://about.gitlab.com/blog/introducing-gitlab-credits/): spending caps and budget guardrails that give your organization even more control and transparency over how credits are consumed.\n\n## Managing GitLab Credits\n\nGitLab 18.11 adds three layers of control over GitLab Credits consumption: a subscription-level spending cap, per-user credit limits, and visibility into cap status and enforcement.\n\n### Subscription-level spending cap\n\nBilling account managers can now set a hard monthly ceiling for on-demand GitLab Credits consumption for their entire subscription.\n\nHere's how it works:\n\n* **Set a cap** in the `Customers Portal` under your subscription's GitLab Credits settings.  \n* **Enforce spend limits automatically.**  When on-demand usage reaches the cap, DAP access is paused for all users on that subscription until the next monthly period begins.  \n* **Make adjustments as you go.** Raise or disable the cap mid-month to restore access.\n\nThe cap resets each monthly period and your configured limit carries forward unless you change it. Because usage data is synchronized periodically rather than in real time, a small amount of additional usage may occur after the cap is reached before enforcement takes effect. See the [GitLab Credits documentation](https://docs.gitlab.com/subscriptions/gitlab_credits/) for details.\n\n### User-level spending caps\n\nNot every user consumes credits at the same rate, and that's expected. But when one or two power users account for a disproportionate share of the pool, the rest of the team can lose access before the month is over.\n\nPer-user credit caps prevent any single user from consuming more than their fair share:\n\n* **Flat per-user cap.** Set a uniform credit limit that applies equally to every user on the subscription through the GitLab GraphQL API. Unlike the subscription-level cap, the per-user cap applies to a user's total consumption across all credit sources.  \n* **Custom per-user overrides.** For organizations that need differentiated limits, you can set individual credit caps for specific users through the GraphQL API. For example, you could give your staff engineers a higher allocation while applying a standard limit to the broader team.  \n* **Individual enforcement.** When a user reaches their cap, they retain full access to GitLab. Only their Duo Agent Platform credit usage is paused until the next billing cycle. Everyone else keeps working uninterrupted until they hit their own limit or the subscription-level cap is reached, whichever comes first.\n\n### Visibility and notifications\n\nWhen a subscription-level cap is reached, GitLab sends an email notification to billing account managers so they can take action: raise the cap, wait for the next period, or redistribute credits.\n\nWithin GitLab, group owners (GitLab.com) and instance administrators (Self-Managed) can view which users have been blocked due to reaching their per-user cap and restore access by adjusting the cap through the GraphQL API. \n\n## How budget guardrails help organizations scale AI usage\n\nGuardrails are essential as organizations ramp up their AI adoption. Here's why:\n\n### Predictable AI budgets\n\nUsage controls for GitLab Duo Agent Platform turn AI into a bounded, predictable budget item using on-demand GitLab Credits. That makes it easier to deploy agents across the software development lifecycle and get sign-off from finance, justify renewals, and plan quarterly spend.\n\n### Governance and chargeback\n\nLarge organizations often need to align AI consumption with internal budgets, cost centers, or departmental policies. Per-user caps give platform teams a straightforward mechanism to allocate credits fairly and track consumption at the individual level. The API import options make it practical to manage caps at enterprise scale. Combined with per-user usage data from the GitLab Credits dashboard, organizations can track consumption patterns to inform their own internal chargeback or budget allocation processes.\n\n### Confidence to scale\n\nMany customers start GitLab Duo Agent Platform with a small pilot group. Usage controls remove risks associated with expanding that pilot across the organization. You can roll out Duo Agent Platform to hundreds or thousands of developers knowing there's a hard ceiling protecting your budget. If usage grows faster than expected, you'll hit the cap, not an unexpected invoice.\n\n## Addressing the seat-based and visibility conundrum\n\nMany AI coding tools take a seat-based approach to cost management. You buy a fixed number of seats at a flat per-user price, and that's your budget. It's simple, but rigid. You pay the same whether a developer uses the tool ten times a day or never touches it. And as vendors introduce premium models and usage-based overages on top of seat pricing, the cost predictability that seat-based licensing promised starts to erode.\n\n\nGitLab takes a different approach. Usage-based pricing with hard caps and a single governance dashboard. You get the flexibility of paying for what your teams actually use, with the budget predictability of enforced spending limits.\n\n## Real-world usage controls\n\n**One example is a mid-size SaaS customer that wants to protect their monthly budget.** A 200-person engineering organization sets a subscription-level cap equal to their expected on-demand usage. Their VP of Engineering can confidently tell finance that GitLab Duo Agent Platform spend will never exceed the approved amount, even as they onboard new teams. If they approach the cap mid-month, the billing account manager gets a notification and can decide whether to raise the limit or wait for the next period.\n\n**At GitLab, we also work with large enterprises that want to keep usage fair across teams.** A global financial services company with 2,000 developers uses per-user caps to ensure equitable access. Staff engineers working on complex refactoring projects get a higher individual allocation via API, while most developers receive a standard flat cap. No single user can exhaust the pool, and the platform team uses the per-user usage data in the GitLab Credits dashboard to track consumption patterns and inform quarterly budget planning.\n\n## Getting started\n\nUsage controls are available for both GitLab.com and Self-Managed customers running GitLab 18.11. Different controls are configured in different places depending on the scope and your role.\n\n**Subscription-level cap**\n\nBilling account managers set the subscription-level on-demand cap in the Customers Portal:\n\n1. Sign in to the `Customers Portal`.  \n2. On your subscription card, navigate to **GitLab Credits** settings.  \n3. Enable the monthly on-demand credits cap and enter your desired limit.\n\n**Flat per-user cap**\n\nThe flat per-user cap can be set through the GitLab GraphQL API by namespace owners (GitLab.com) or instance administrators (Self-Managed). Check the [GitLab Credits documentation](https://docs.gitlab.com/subscriptions/gitlab_credits/) for the latest on available configuration surfaces.\n\n**Custom per-user overrides**\n\nFor differentiated limits, namespace owners (GitLab.com) and instance administrators (Self-Managed) can set individual caps programmatically. This is useful for automation and infrastructure-as-code workflows.\n\n**Monitor usage and cap status**\n\n* **Customers Portal:** View detailed usage and cap status.  \n* **GitLab.com:** Group owners can view blocked users under **Settings > GitLab Credits**.  \n* **Self-Managed:** Instance administrators can view cap status and blocked users under **Admin > GitLab Credits**.\n\n## GitLab Duo Agent Platform is ready to scale\n\nUsage controls are available now in GitLab 18.11. If you've been waiting for the right guardrails before expanding GitLab Duo Agent Platform across your organization, this is your moment. Set your caps, roll out Duo Agent Platform to more teams, and start shipping faster!\n\n> [Learn more about GitLab Credits and usage controls](https://docs.gitlab.com/subscriptions/gitlab_credits/).",[9,723,724],"AI/ML","news",{"featured":12,"template":13,"slug":726},"gitlab-18-11-budget-guardrails-for-gitlab-credits",{"content":728,"config":731},{"title":729,"heroImage":719,"description":730,"date":720,"category":9},"GitLab 18.11 release","This release includes Agentic SAST Vulnerability Resolution, Data Analyst Foundational Agent, CI Expert Agent, and more.",{"featured":12,"template":13,"externalUrl":732},"https://docs.gitlab.com/releases/18/gitlab-18-11-released/",{"content":734,"config":741},{"title":735,"description":736,"authors":737,"heroImage":719,"date":720,"body":739,"category":9,"tags":740},"GitLab 18.11: CI Expert and Data Analyst AI agents target development gaps","Set up CI and query your software development lifecycle data with two new GitLab Duo Agent Platform foundational agents available in GitLab 18.11.",[738],"Corinne Dent","AI-generated code moves faster than the systems around it can keep up with. More code means more merge requests queued, more pipelines to configure, more questions about delivery that nobody has time to answer — and most of the tooling teams rely on wasn't built for this pace.\n\nIn GitLab 18.11, two new foundational agents for Duo Agent Platform address specific gaps in the development lifecycle that AI has largely left untouched:\n* CI Expert Agent (now in beta) focuses on the gap between writing code and getting it into a running pipeline\n* Data Analyst Agent (now generally available) focuses on the gap between shipping code and being able to answer basic questions about how that delivery is actually going.\n\n\nThese are problem areas that couldn't be solved by a general-purpose assistant. A tool running outside GitLab can generate a YAML file or answer a question, but it has no awareness of how your pipelines have historically performed, where failures cluster, or what your actual MR cycle times look like. That context lives in GitLab. These agents do too.\n## Fast CI setup with CI Expert Agent\n\nAI has made it easier than ever to write code. Getting that code into a running pipeline is still something most teams do days, or weeks, later — if at all. The blank-page problem isn't in the editor anymore. The blank page is now in `.gitlab-ci.yml`.\n\nDevelopers who have never configured CI don't know what language detection looks like in YAML, what their test commands should be, or how to validate the result before pushing. Teams either copy a config from a previous project that may not fit, stitch together examples from documentation, or wait for the one person who's done it before. If that person isn't available, CI becomes the thing you'll \"get to later.\" Later becomes never.\n\nWhen CI never happens, the impact shows up everywhere else. Changes ship without a reliable safety net, regressions surface in production instead of in pipelines, and work piles up in bigger, riskier batches because no one wants to be the person who “breaks the build.” Over time, teams normalize working in the dark, often relying on undocumented institutional knowledge and ad-hoc testing, instead of having a fast, predictable feedback loop baked into every change.\n\nCI Expert Agent, now available in beta, removes that friction. It inspects your repository, identifies your language and framework, and proposes a working build and test pipeline tailored to what's actually there — then explains every decision in plain language. The target: a running pipeline in minutes, with no YAML written by hand.\n\nWhat CI Expert Agent does:\n\n* Repo-aware pipeline generation detects language, framework, and test setup \n* Generates valid, runnable build and test configurations   \n* Guided first-pipeline flow with plain-language explanation of each step in Agentic Chat  \n* Native GitLab CI semantics with no config translation required\n\nBecause it runs inside GitLab and sees real pipeline behavior over time, each improvement can build on how teams actually work, not just on static examples.\n\u003Ciframe src=\"https://player.vimeo.com/video/1183458036?badge=0&autopause=0&player_id=0&app_id=58479\" frameborder=\"0\" allow=\"autoplay; fullscreen; picture-in-picture; clipboard-write; encrypted-media; web-share\" referrerpolicy=\"strict-origin-when-cross-origin\" style=\"position:absolute;top:0;left:0;width:100%;height:100%;\" title=\"CI/CD Expert Agent\">\u003C/iframe>\u003Cscript src=\"https://player.vimeo.com/api/player.js\">\u003C/script>\n\u003Cbr>\u003C/br>\n\nCI Expert Agent is available on GitLab.com, Self-Managed, Dedicated; Free, Premium, Ultimate Editions with Duo Agent Platform enabled.\n\n## Query GitLab data in plain language with Data Analyst Agent\n\nAI has sped up how teams ship. Answering basic questions about how that work is going has gotten harder, not easier.\n\nHow long are MRs sitting in review? Which pipelines are slowing teams down? Are deployment targets actually being hit? These questions used to be answerable by glancing at a dashboard. Now, with more code, more teams, and more complexity, the data exists — it's in GitLab — but accessing it still means waiting on an analytics team, filing a dashboard request, or learning GLQL.\n\nData Analyst Agent targets that gap. Ask a natural-language question and get an instant visualization in Agentic Chat. No query language, no dashboard request, no waiting for the answers to be assembled by someone else.\n\nFor example, the agent can answer questions about the following topics for these roles:\n\n* Engineering managers: MR cycle time, throughput by project, where reviews get stuck  \n* Developers: Contribution patterns, flaky tests blocking their MRs, pipeline speed trends  \n* DevOps and platform engineers: Pipeline success/failure rates, runner utilization, deployment frequency  \n* Engineering leadership: Cross-portfolio deployment frequency, project health metrics, lead time comparisons\n\nNow generally available in 18.11, the agent covers MRs, issues, projects, pipelines, and jobs — full software development lifecycle coverage, expanded from the beta scope. Because Data Analyst Agent queries what's already in GitLab, the context is always current, and there's no pipeline to maintain or third-party tool to keep synchronized. Generated GitLab Query Language queries can be copied and used anywhere GitLab Flavored Markdown is supported, with direct export to work items and dashboards on the roadmap.\n\n\u003Ciframe src=\"https://player.vimeo.com/video/1183094817?badge=0&autopause=0&player_id=0&app_id=58479\" frameborder=\"0\" allow=\"autoplay; fullscreen; picture-in-picture; clipboard-write; encrypted-media; web-share\" referrerpolicy=\"strict-origin-when-cross-origin\" style=\"position:absolute;top:0;left:0;width:100%;height:100%;\" title=\"Data Analyst agent demo\">\u003C/iframe>\u003Cscript src=\"https://player.vimeo.com/api/player.js\">\u003C/script>\n\u003Cbr>\u003C/br>\n\nData Analyst Agent is available on GitLab.com, Self-Managed, Dedicated; Free, Premium and Ultimate Edition with Duo Agent Platform enabled.\n\n## One platform, connected context\n\nBoth agents run inside GitLab, with access to the code, pipelines, issues, and merge requests already there. That's what separates platform-native AI from a disconnected assistant: the context is always current, and it only gets more useful over time. CI Expert Agent and Data Analyst Agent represent two concrete steps toward a platform where AI doesn't just help you write code faster; it helps you understand, ship, and maintain what gets built.\n\n> [Start a free trial of GitLab Duo Agent Platform](https://about.gitlab.com/gitlab-duo/) to experience these foundational AI agents.",[723,24,9],{"featured":27,"template":13,"slug":742},"ci-expert-and-data-analyst-ai-agents-target-development-gaps",{"promotions":744},[745,759,770,782],{"id":746,"categories":747,"header":749,"text":750,"button":751,"image":756},"ai-modernization",[748],"ai-ml","Is AI achieving its promise at scale?","Quiz will take 5 minutes or less",{"text":752,"config":753},"Get your AI maturity score",{"href":754,"dataGaName":755,"dataGaLocation":241},"/assessments/ai-modernization-assessment/","modernization assessment",{"config":757},{"src":758},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/qix0m7kwnd8x2fh1zq49.png",{"id":760,"categories":761,"header":762,"text":750,"button":763,"image":767},"devops-modernization",[9,566],"Are you just managing tools or shipping innovation?",{"text":764,"config":765},"Get your DevOps maturity score",{"href":766,"dataGaName":755,"dataGaLocation":241},"/assessments/devops-modernization-assessment/",{"config":768},{"src":769},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138785/eg818fmakweyuznttgid.png",{"id":771,"categories":772,"header":774,"text":750,"button":775,"image":779},"security-modernization",[773],"security","Are you trading speed for security?",{"text":776,"config":777},"Get your security maturity score",{"href":778,"dataGaName":755,"dataGaLocation":241},"/assessments/security-modernization-assessment/",{"config":780},{"src":781},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/p4pbqd9nnjejg5ds6mdk.png",{"id":783,"paths":784,"header":787,"text":788,"button":789,"image":794},"github-azure-migration",[785,786],"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":790,"config":791},"See how GitLab compares to GitHub",{"href":792,"dataGaName":793,"dataGaLocation":241},"/compare/gitlab-vs-github/github-azure-migration/","github azure migration",{"config":795},{"src":769},{"header":797,"blurb":798,"button":799,"secondaryButton":804},"Start building faster today","See what your team can do with the intelligent orchestration platform for DevSecOps.\n",{"text":800,"config":801},"Get your free trial",{"href":802,"dataGaName":48,"dataGaLocation":803},"https://gitlab.com/-/trial_registrations/new?glm_content=default-saas-trial&glm_source=about.gitlab.com/","feature",{"text":503,"config":805},{"href":52,"dataGaName":53,"dataGaLocation":803},1776444514076]