[{"data":1,"prerenderedAt":780},["ShallowReactive",2],{"/en-us/blog/taming-tool-sprawl-how-to-boost-university-it-productivity":3,"navigation-en-us":33,"banner-en-us":433,"footer-en-us":443,"blog-post-authors-en-us-Elisabeth Burrows":684,"blog-related-posts-en-us-taming-tool-sprawl-how-to-boost-university-it-productivity":698,"assessment-promotions-en-us":731,"next-steps-en-us":770},{"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":20,"seo":27,"stem":30,"tagSlugs":31,"__hash__":32},"blogPosts/en-us/blog/taming-tool-sprawl-how-to-boost-university-it-productivity.yml","Taming Tool Sprawl How To Boost University It Productivity",[7],"elisabeth-burrows",null,"product",{"featured":11,"template":12,"slug":13},true,"BlogPost","taming-tool-sprawl-how-to-boost-university-it-productivity",{"title":15,"description":16,"authors":17,"heroImage":19,"date":20,"body":21,"category":9,"tags":22},"Taming tool sprawl: How to boost university IT productivity","Discover how a unified DevSecOps platform drives user growth, automates compliance, and ensures responsible AI adoption in higher education.",[18],"Elisabeth Burrows","https://res.cloudinary.com/about-gitlab-com/image/upload/v1756989645/fojzxakmfdea6jfqjkrl.png","2025-12-15","When Dr. James Quilty began developing engineering project management courses at Victoria University of Wellington's School of Engineering and Computer Science, he didn't find an organized system for delivering course content. Instead, he was faced with chaos.\n\nThe problem was that back in 2015 learning materials were scattered across a dozen different tools, like the Blackboard platform, customized Wiki pages, personal websites, and shared Google Docs. On top of that, students were left to choose their own tools for coursework. All of this led to a constant state of confusion. As if that weren’t enough, few of these disparate systems provided proper version history or reliable issue tracking.\n\nThis all-too-familiar lack of standardization was creating massive headaches for both lecturers and students.\n\n\"Information was fragmented across multiple files, multiple formats — sitting often on file systems, not necessarily under good version control,\" says Quilty, who is now program director for engineering at the New Zealand university.\n\nAfter consolidating on GitLab Self-Managed Ultimate in 2017, [Victoria University](https://about.gitlab.com/customers/victoria-university/) saw 483% growth in student users by 2021. They also added 35 GitLab-enabled courses and now host more than 8,000 projects. The university also deployed GitLab as a unified DevSecOps platform for academic coursework, replacing what had become a fragmented and complicated toolchain. More importantly, they've redirected faculty time from administration to actual education.\n\nThis pattern isn't unique. Across higher education, IT teams struggle with the same tool sprawl — multiple tools and incompatible systems that lead to hours lost to context switching and administering disparate and costly tools. Simplifying how teams build and deliver software is the answer to this widespread problem.\n\nThe teams making real progress are reducing complexity instead of creating it.\n\n### Facing the complexity problem\n\nHigher education IT teams are faced with managing aging infrastructure, legacy systems, and resource constraints that force difficult tradeoffs with every technology decision they have to make.\n\nDevelopment workflows exist in silos since many departments use different version control systems, CI/CD tools, and security scanners. That means teams struggle to collaborate on cross-functional projects because they're working with incompatible toolchains and a lack of shared visibility.\n\nLegacy technology compounds these problems. Many institutions run development environments that are outdated and incompatible with modern DevSecOps practices. But replacing them isn't realistic when budgets are tight and IT staff are already stretched thin.\n\nTo take on these problems, institutions need to modernize, but because of administrative processes, budget constraints, and the reality of managing critical systems, they have to do it in phases, not overnight. For instance, some workloads may move to the cloud while others remain on-premises. [A research department](https://edtechmagazine.com/higher/article/2024/09/how-approach-higher-eds-hybrid-cloud-migration), for instance, might shift large datasets off-site while central IT functions stay in-house.\n\nOrganizations need the flexibility to be able to do that, and that’s what they get with GitLab Ultimate, the enterprise-ready DevSecOps platform that delivers the same capabilities whether you deploy on GitLab.com or self-host on your own infrastructure: on-premises servers, data centers, or cloud providers, including AWS, GCP, Azure, or even multi-cloud. Self-hosted deployments include all features, including air-gapped support for sensitive environments.\n\nThis means, with [GitLab Ultimate](https://about.gitlab.com/pricing/ultimate/), institutions can modernize on their own timeline without abandoning governance requirements or forcing wholesale infrastructure changes.\n\n### Moving from manual compliance to automated enforcement\n\nIT teams also have to work with regulatory mandates and that adds another layer of complexity. Student privacy requirements, research grant stipulations, and institutional security policies all demand audit trails and governance controls. For institutions supporting U.S. Department of Defense research or contractors, [CMMC 2.0 compliance requirements](https://www.meritalk.com/articles/dod-begins-rollout-of-cmmc-on-nov-10-heres-what-you-need-to-know/) add stringent cybersecurity controls based on NIST SP 800-171. Meeting these obligations while modernizing traditionally meant manually documenting everything — a process that didn't scale easily.\n\nIn conversations with team members from educational institutions at events like EDUCAUSE we've learned it's all too common for dedicated compliance staff to spend the majority of their time gathering evidence for audits, instead of actually improving security. Not building better software. Just proving that policies were followed. This administrative burden extends to development teams, as well. According to Forrester Consulting’s study [The Total Economic Impact™ of GitLab Ultimate](https://about.gitlab.com/resources/study-forrester-tei-gitlab-ultimate/), which was commissioned by GitLab, software development team members save 90% of the time previously spent on annual auditing and compliance efforts after adopting GitLab's end-to-end platform.\n\nGitLab saves all of that time and effort by enabling automation through [custom compliance frameworks](https://docs.gitlab.com/user/compliance/compliance_frameworks/) that map multiple, overlapping controls from different standards and regulations into a single, unified structure. They then cascade automatically from the instance level to all subgroups and projects, ensuring consistent enforcement without manual configuration.\n\n[Pipeline execution policies](https://docs.gitlab.com/user/application_security/policies/pipeline_execution_policies/) enforce compliance directly in CI/CD pipelines where development work happens. Rather than operating disparate governance, risk, and compliance tools, compliance validation occurs automatically as code moves through the pipeline. To make all of this easier, GitLab’s [Compliance Center](https://docs.gitlab.com/user/compliance/compliance_center/) provides oversight through dashboards that show where projects fail to meet framework requirements — whether due to failed security scans or other control gaps.\n\nComplete [audit trails](https://docs.gitlab.com/user/compliance/audit_events/) also capture every code change with timestamps and attribution. And [policy-as-code](https://handbook.gitlab.com/handbook/security/security-assurance/security-compliance/policy-as-code/) enforces security rules that can't be bypassed. When an auditor asks who changed what code and when, you have the answer instantly — without weeks spent manually gathering evidence. Every pipeline execution automatically generates compliance documentation, enabling teams to instantly prove adherence to requirements and quickly identify any control gaps.\n\n### AI: Governance over guesswork\n\nThis visibility across the entire security posture matters now more than ever. Artificial intelligence (AI) is changing how software gets built with many teams testing AI code generation tools to enable them to move faster. But higher education institutions are uniquely positioned to lead on a critical question: How do you adopt AI responsibly?\n\n[Cornell University](https://edtechmagazine.com/higher/article/2025/10/ai-playbook-comprehensive-strategy-higher-education-perfcon) and [Cal State Fullerton](https://www.fullerton.edu/it/ai/ethical-principles-ai-framework.html) already are developing ethical frameworks for AI use, asking essential questions about transparency, explainability, and bias. The [University of California San Diego](https://edtechmagazine.com/higher/article/2025/05/effective-ai-requires-effective-data-governance) is adapting its existing data governance framework — originally built for analytics platforms — to secure its on-premises AI assistants, ensuring the same access controls and approval workflows that protect institutional data now extend to AI-driven tools. Educational institutions understand that AI adoption requires more than just enabling new tools — it requires proper oversight and protection.\n\nThe problem isn't AI itself. It's AI without guardrails integrated into development workflows. Most organizations haven't considered what secure AI development looks like — what governance is needed for AI-generated code, how to maintain visibility into what gets committed to repositories, or how to ensure the same rigor applies whether code comes from a human or AI.\n\nThis is exactly where platform-level AI integration becomes essential. [GitLab Duo Agent Platform](https://docs.gitlab.com/user/duo_agent_platform/) goes beyond fragmented AI tools and coding assistants alone to provide an orchestration layer that integrates AI across the entire software development lifecycle.\n\nAI agents handle planning, testing, security remediation, and deployment tasks, while working alongside developers rather than just generating code on command. When security scans identify vulnerabilities, for example, AI agents explain findings, assess risks, and prioritize issues to reduce noise and accelerate mean time to recovery (MTTR). This platform approach ensures AI accelerates development without compromising the security standards and governance controls institutions require.\n\nThe benefits extend beyond technical capabilities. Through GitLab's [AI Transparency Center](https://about.gitlab.com/ai-transparency-center/), institutions get clear documentation of data privacy protections, AI ethics principles, and vendor selection processes. This means schools can adopt AI tools while maintaining the governance standards they're developing institution-wide.\n\nAI will change how we build software. The question is whether institutions can do it with the same responsible approach they're bringing to AI adoption across campus.\n\n## See results in your education environment\n\nThe universities making real progress aren't adding more tools to manage complexity. They're consolidating onto platforms that prevent problems rather than just detecting them, creating visibility and automation across their development workflows.\n\nForrester's [The Total Economic Impact™ of GitLab Ultimate](https://about.gitlab.com/resources/study-forrester-tei-gitlab-ultimate/) study found that a composite organization representative of interviewed customers reclaimed up to 305 hours per developer year through automated testing within a single interface, eliminating constant context switching between tools. New hires ramped to full productivity 75% faster — in 1.5 weeks instead of 1.5 months. Teams spend their time building rather than maintaining fragmented toolchains.\n\n**Your institution can achieve similar results.** Learn more about how GitLab Ultimate can help your institution deliver secure software faster while meeting compliance requirements. 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GitLab metrics and registry features help reduce CI/CD bottlenecks","See how CI/CD Job Performance Metrics and Container Virtual Registry, currently in beta, help platform teams quickly spot slow jobs and simplify multi-registry container pulls.",[704],"Talia Armato-Helle","https://res.cloudinary.com/about-gitlab-com/image/upload/v1771438388/t6sts5qw4z8561gtlxiq.png","Platform and DevOps engineers spend too much time piecing together visibility across fragmented tools and managing infrastructure that should just work.\n\nTwo new GitLab features currently in beta tackle this from different angles but share the same goal: giving practitioners direct control over the CI/CD infrastructure they depend on, without adding another third-party tool. One surfaces job-level performance data right where you monitor pipelines. The other simplifies how you pull container images from multiple registries with built-in caching.\n\nBoth features are open for feedback now. Your input will help shape what ships next.\n\n## CI/CD Job Performance Metrics\n\n* **Available tiers:** GitLab Premium, GitLab Ultimate\n* **Status:** Limited-availability beta on GitLab.com; available on GitLab Self-Managed and GitLab Dedicated when ClickHouse is configured\n\nToday, there’s no simple way to see when a particular job’s duration starts increasing or which jobs are quietly dragging down your pipeline runtimes. Most teams either build custom dashboards or manually dig through logs to answer basic questions like:\n\n* Which jobs are slowest?  \n* Where are failure rates climbing?  \n* Which stage is the real bottleneck?\n\nCI/CD Job Performance Metrics changes that by adding a new job-focused panel to the CI/CD analytics page at the project level.\n\nFor each job in your pipelines, you can see:\n\n* Typical (P50, median) and worst‑case (P95) job duration, so you can quickly view normal versus slowest runs  \n* Failure rate, so you can spot fragile or flaky jobs  \n* Job name and stage, covering the last 30 days by default\n\nThe table is sortable, searchable by job name, and paginated, so platform teams get a single view to answer questions that previously required separate tools or custom reporting.\n\n**Try it now**\n\n* Navigate to your project and select **Analyze \\> CI/CD analytics**.  \n* Look for the CI/CD job performance metrics panel and sort by duration or failure rate to find your slowest or least reliable jobs.\n\n**Documentation**\n\n* [CI/CD analytics – CI/CD job performance metrics](https://docs.gitlab.com/user/analytics/ci_cd_analytics/#cicd-job-performance-metrics)\n\n**What’s coming next**\n\nWe’re working on stage-level grouping, so you can view aggregated metrics across your build, test, and deploy stages, and quickly understand where to focus optimization work.\n\n**Share your feedback:**\n\n* [CI/CD job performance metrics epic](https://gitlab.com/groups/gitlab-org/-/work_items/18548)\n\n## Container Virtual Registry\n\n**Tier:** GitLab Premium, GitLab Ultimate\n**Status:** Beta, API-ready in 18.9\n\nMost organizations pulling container images into CI/CD pipelines rely on multiple registries: Docker Hub, Harbor, Quay, and internal registries, to name a few. Managing authentication, availability, and caching across all of them is operational overhead that slows pipelines down and introduces fragility.\n\nThe Container Virtual Registry lets you create a single GitLab endpoint that pulls from multiple upstream container sources with built-in caching.\n\nInstead of configuring credentials and availability for each registry individually in your pipeline configuration, you can:\n\n* Point your pipelines at one GitLab virtual registry endpoint  \n* Configure multiple upstream registries (Docker Hub, Harbor, Quay, and others using long-lived token authentication)  \n* Let GitLab resolve image pulls automatically, with pull-through caching to reduce bandwidth costs and improve reliability\n\nFor teams evaluating GitLab as a container registry replacement, this closes a critical capability gap. For teams already managing multi-registry container workflows, it centralizes image management into GitLab and cuts down on repeated pulls.\n\n**What the beta supports today**\n\n* Upstream registries using long-lived token authentication: Docker Hub, Harbor, Quay, and other compatible registries  \n* Pull-through caching so commonly used images are served from GitLab after the first pull  \n* API-first configuration, with UI management in progress++\n\nCloud provider registries requiring IAM authentication (such as Amazon Elastic Container Registry, Google Artifact Registry, and Azure Container Registry) are being considered for future iterations.\n\n**Test it today**\n\n* The Container Virtual Registry is API-ready in 18.9.  \n* SaaS (GitLab.com): Request access through your CSM or by commenting on the feedback issue below to have the feature flag enabled for your group.  \n* Self-managed: Enable the feature flag and configure the virtual registry using the API.\n\n**Documentation**\n\n* [Container Virtual Registry API](https://docs.gitlab.com/api/container_virtual_registries/)  \n* [Pull container images from the virtual registry](https://docs.gitlab.com/user/packages/virtual_registry/container/#pull-container-images-from-the-virtual-registry)\n\n\n Watch this walkthrough of the Container Virtual Registry Beta:\n   \n\n  \u003Ciframe src=\"https://player.vimeo.com/video/1167512082?title=0&byline=0&portrait=0&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=\"20260223_Container Virtual Registry Beta_V1\">\u003C/iframe>\u003C\u003Cscript src=\"https://player.vimeo.com/api/player.js\">\u003C/script>\n\n  \u003Cbr>\u003C/br>\n\n\n\n**Share your feedback:**\n\n* [Container virtual registry feedback issue](https://gitlab.com/gitlab-org/gitlab/-/issues/589630)\n\n## Help us build what matters\n\nEveryone in the GitLab community is a contributor. We built these betas based on community requests.\n\n* **CI/CD Job Performance Metrics** came from teams who had no easy way to see when build times started trending in the wrong direction, or which jobs were hurting pipeline reliability.  \n* **Container Virtual Registry** came from enterprise customers managing multiple registries and looking to reduce tool sprawl and bandwidth costs while evaluating GitLab as a central registry.\n\nYour feedback shapes what we create next. Try one or both of these betas, and share your experience in the linked feedback issues.\n\nThis is the first in a series of Core DevOps betas we plan to highlight. 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