[{"data":1,"prerenderedAt":803},["ShallowReactive",2],{"/en-us/blog/streamline-enterprise-artifact-management-with-gitlab":3,"navigation-en-us":33,"banner-en-us":443,"footer-en-us":453,"blog-post-authors-en-us-Tim Rizzi":695,"blog-related-posts-en-us-streamline-enterprise-artifact-management-with-gitlab":709,"blog-promotions-en-us":740,"next-steps-en-us":793},{"id":4,"title":5,"authorSlugs":6,"body":8,"categorySlug":9,"config":10,"content":14,"description":8,"extension":24,"isFeatured":11,"meta":25,"navigation":11,"path":26,"publishedDate":22,"seo":27,"stem":30,"tagSlugs":31,"__hash__":32},"blogPosts/en-us/blog/streamline-enterprise-artifact-management-with-gitlab.yml","Streamline Enterprise Artifact Management With Gitlab",[7],"tim-rizzi",null,"product",{"featured":11,"template":12,"slug":13},true,"BlogPost","streamline-enterprise-artifact-management-with-gitlab",{"title":15,"description":16,"heroImage":17,"authors":18,"category":9,"tags":20,"date":22,"body":23},"Streamline enterprise artifact management with GitLab","Platform teams can spend $200K+ annually managing fragmented artifact systems. Learn about GitLab's strategic approach to consolidation.","https://res.cloudinary.com/about-gitlab-com/image/upload/v1756500636/wmey6kqzzuhirk88w2de.png",[19],"Tim Rizzi",[9,21],"features","2025-10-08","For the past six years, I've worked on artifact management at GitLab and have had hundreds of conversations with platform engineers trying to solve the same challenge: managing artifacts when they've become a sprawling, expensive mess. What started as simple Docker registries and Maven repositories has evolved into a complex web of tools, policies, and operational overhead that's consuming more time and budget than anyone anticipated.\n\nI recently spoke with a platform engineer at a Fortune 500 company who told me, \"I spend more time managing artifact repositories than I do on actual platform improvements.\" That conversation reminded me why we need an honest discussion about the real costs of fragmented artifact management — and what platform teams can realistically do about it. This article will help you better understand the problem and how GitLab can help you solve it through strategic consolidation.\n\n## Real-world impact: The numbers\n\nBased on data from our customers and industry research, fragmented artifact management typically results in the following costs for a midsize organization (500+ developers):\n\n* **Licensing:** $50,000-200,000 annually across multiple tools  \n* **Operational overhead:** 2-3 FTE's equivalent time spent on artifact management tasks  \n* **Storage inefficiency:** 20%-30% higher storage costs due to duplication and poor lifecycle management  \n* **Developer productivity loss:** 15-20 minutes daily per developer due to artifact-related friction\n\nFor large enterprises, these numbers multiply significantly. One customer calculated they were spending over $500,000 annually just on the operational overhead of managing seven different artifact storage systems.\n\nThe hidden costs compound daily:\n\n**Time multiplication:** Every lifecycle policy, security rule, or access control change must be implemented across multiple systems. What should be a 15-minute configuration becomes hours of work.\n\n**Security gap risks:** Managing security policies across disparate systems creates blind spots. Vulnerability scanning, access controls, and audit trails become fragmented.\n\n**Context switching tax:** Developers lose productivity when they can't find artifacts or need to remember which system stores what.\n\n## The multiplication problem\n\nThe artifact management landscape has exploded. Where teams once managed a single Maven repository, today's platform engineers juggle:\n\n* Container registries (Docker Hub, ECR, GCR, Azure ACR)  \n* Package repositories (JFrog Artifactory, Sonatype Nexus)  \n* Language-specific registries (npm, PyPI, NuGet, Conan)  \n* Infrastructure artifacts (Terraform modules, Helm charts)  \n* ML model registries (MLflow, Weights & Biases)\n\nEach tool comes with its own authentication system, lifecycle policies, security scanning, and operational requirements. For organizations with hundreds or thousands of projects, this creates an exponential management burden.\n\n## GitLab's strategic approach: Depth over breadth\n\nWhen we started building GitLab's artifact management capabilities six years ago, we faced a classic product decision: support every artifact format imaginable or go deep on the formats that matter most to enterprise teams. We chose depth, and that decision has shaped everything we've built since.\n\n### Our core focus areas\n\nInstead of building shallow support for 20+ formats, we committed to delivering enterprise-grade capabilities for a strategic set:\n\n* **Maven** (Java ecosystem)  \n* **npm** (JavaScript/Node.js)  \n* **Docker/OCI** (container images)  \n* **PyPI** (Python packages)  \n* **NuGet** (C#/.NET packages)  \n* **Generic packages** (any binary artifact)  \n* **Terraform modules** (infrastructure as code)\n\nThese seven formats account for approximately 80% of artifact usage in enterprise environments, based on our customer data.\n\n### What 'enterprise-grade' actually means\n\nBy focusing on fewer formats, we can deliver capabilities that work in production environments with hundreds of developers, terabytes of artifacts, and strict compliance requirements:\n\n**[Virtual registries](https://docs.gitlab.com/user/packages/virtual_registry/):** Proxy and cache upstream dependencies for reliable builds and supply chain control. Currently production-ready for Maven, with npm and Docker coming in early 2026.\n\n**Lifecycle management**: Automated cleanup policies that prevent storage costs from spiraling while preserving artifacts for compliance. Available at the project level today, organization-level policies planned for mid-2026.\n\n**[Security integration](https://docs.gitlab.com/user/application_security/):** Built-in vulnerability scanning, dependency analysis, and policy enforcement. Our upcoming Dependency Firewall (planned for late 2026) will provide supply chain security control across all formats.\n\n**[Deep CI/CD integration](https://docs.gitlab.com/ci/):** Complete traceability from source commit to deployed artifact, with build provenance and security scan results embedded in artifact metadata.\n\n## Current capabilities: Battle-tested features\n\n**Maven virtual registries:** Our flagship enterprise capability, proven with 15+ enterprise customers. Most complete [Maven virtual registry](https://about.gitlab.com/blog/tutorial-secure-and-optimize-your-maven-repository-in-gitlab/) setup within two months, with minimal GitLab support required.\n\n**Locally-hosted repositories:** All seven supported formats offer complete upload, download, versioning, and access control capabilities supporting critical workloads at organizations with thousands of developers.\n\n**Protected artifacts:** Comprehensive protection preventing unauthorized modifications, supporting fine-grained access controls across all formats.\n\n**Project-level lifecycle policies:** Automated cleanup and retention policies for storage cost control and compliance.\n\n### Performance and scale characteristics\n\nBased on current production deployments:\n\n* **Throughput:** 10,000+ artifact downloads per minute/per instance  \n* **Storage:** Customers successfully managing 50+ TB of artifacts  \n* **Concurrent users:** 1,000+ developers accessing artifacts simultaneously  \n* **Availability:** 99.99% uptime for [GitLab.com](http://GitLab.com) for more than 2 years\n\n## Strategic roadmap: Next 18 months\n\n### Q1 2026\n\n* **npm virtual registries:** Enterprise proxy/cache for JavaScript packages  \n* **Docker virtual registries:** Container registry proxy capabilities\n\n### Q2 2026\n\n* **Organization-level lifecycle policies (Beta):** Centralized cleanup policies with project overrides  \n* **NuGet virtual registries (Beta):** .NET package proxy support  \n* **PyPI virtual registries (Beta):** Completing virtual registry support for Python\n\n### Q3 2026\n\n* **Advanced Analytics Dashboard:** Storage optimization and usage insights\n\n### Q4 2026\n\n* **Dependency Firewall (Beta):** Supply chain security control for all artifact types\n\n## When to choose GitLab: Decision framework\n\n**GitLab is likely the right choice if:**\n\n* 80%+ of your artifacts are in our seven supported formats  \n* You're already using GitLab for source code or CI/CD  \n* You value integrated workflows over standalone feature richness  \n* You want to reduce the operational complexity of managing multiple systems  \n* You need complete traceability from source to deployment  \n\n\n### Migration considerations\n\n**Typical timeline:** 2-4 months for complete migration from Artifactory/Nexus \n\n**Common challenges:** Virtual registry configuration, access control mapping, and developer workflow changes \n\n**Success factors:** Phased approach, comprehensive testing, and developer training\n\nMost successful migrations follow this pattern:\n\n1. **Assessment** (2-4 weeks): Catalog current artifacts and usage patterns  \n2. **Pilot** (4-6 weeks): Migrate one team/project end-to-end  \n3. **Rollout** (6-12 weeks): Gradual migration with parallel systems  \n4. **Optimization** (ongoing): Implement advanced features and policies\n\n## Better artifact management can start today\n\nGitLab's artifact management isn't trying to be everything to everyone. We've made strategic trade-offs: deep capabilities for core enterprise formats rather than shallow support for everything.\n\nIf your artifact needs align with our supported formats and you value integrated workflows, we can significantly reduce your operational overhead while improving developer experience. \n\nOur goal is to help you make informed decisions about your artifact management strategy with a clear understanding of capabilities and our roadmap.\n\nPlease reach out to me at [trizzi@gitlab.com](mailto:trizzi@gitlab.com) to learn more about GitLab artifact management. I can discuss specific requirements and connect you with our technical team for a deeper evaluation.\n\n*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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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.",[715],"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/).",[9,720,721],"AI/ML","news",{"featured":29,"template":12,"slug":723},"gitlab-18-11-budget-guardrails-for-gitlab-credits",{"content":725,"config":728},{"title":726,"heroImage":716,"description":727,"date":717,"category":9},"GitLab 18.11 release","This release includes Agentic SAST Vulnerability Resolution, Data Analyst Foundational Agent, CI Expert Agent, and more.",{"featured":29,"template":12,"externalUrl":729},"https://docs.gitlab.com/releases/18/gitlab-18-11-released/",{"content":731,"config":738},{"title":732,"description":733,"authors":734,"heroImage":716,"date":717,"body":736,"category":9,"tags":737},"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.",[735],"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.",[720,21,9],{"featured":11,"template":12,"slug":739},"ci-expert-and-data-analyst-ai-agents-target-development-gaps",{"promotions":741},[742,756,767,779],{"id":743,"categories":744,"header":746,"text":747,"button":748,"image":753},"ai-modernization",[745],"ai-ml","Is AI achieving its promise at scale?","Quiz will take 5 minutes or less",{"text":749,"config":750},"Get your AI maturity score",{"href":751,"dataGaName":752,"dataGaLocation":237},"/assessments/ai-modernization-assessment/","modernization assessment",{"config":754},{"src":755},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/qix0m7kwnd8x2fh1zq49.png",{"id":757,"categories":758,"header":759,"text":747,"button":760,"image":764},"devops-modernization",[9,563],"Are you just managing tools or shipping innovation?",{"text":761,"config":762},"Get your DevOps maturity score",{"href":763,"dataGaName":752,"dataGaLocation":237},"/assessments/devops-modernization-assessment/",{"config":765},{"src":766},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138785/eg818fmakweyuznttgid.png",{"id":768,"categories":769,"header":771,"text":747,"button":772,"image":776},"security-modernization",[770],"security","Are you trading speed for security?",{"text":773,"config":774},"Get your security maturity score",{"href":775,"dataGaName":752,"dataGaLocation":237},"/assessments/security-modernization-assessment/",{"config":777},{"src":778},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/p4pbqd9nnjejg5ds6mdk.png",{"id":780,"paths":781,"header":784,"text":785,"button":786,"image":791},"github-azure-migration",[782,783],"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":787,"config":788},"See how GitLab compares to GitHub",{"href":789,"dataGaName":790,"dataGaLocation":237},"/compare/gitlab-vs-github/github-azure-migration/","github azure migration",{"config":792},{"src":766},{"header":794,"blurb":795,"button":796,"secondaryButton":801},"Start building faster today","See what your team can do with the intelligent orchestration platform for DevSecOps.\n",{"text":797,"config":798},"Get your free trial",{"href":799,"dataGaName":44,"dataGaLocation":800},"https://gitlab.com/-/trial_registrations/new?glm_content=default-saas-trial&glm_source=about.gitlab.com/","feature",{"text":499,"config":802},{"href":48,"dataGaName":49,"dataGaLocation":800},1776444499214]