[{"data":1,"prerenderedAt":784},["ShallowReactive",2],{"/en-us/blog/more-granular-product-usage-insights-for-gitlab-self-managed-and-dedicated":3,"navigation-en-us":38,"banner-en-us":438,"footer-en-us":448,"blog-post-authors-en-us-Tanuja Jayarama Raju":689,"blog-related-posts-en-us-more-granular-product-usage-insights-for-gitlab-self-managed-and-dedicated":703,"assessment-promotions-en-us":735,"next-steps-en-us":774},{"id":4,"title":5,"authorSlugs":6,"body":8,"categorySlug":9,"config":10,"content":14,"description":8,"extension":26,"isFeatured":12,"meta":27,"navigation":12,"path":28,"publishedDate":20,"seo":29,"stem":34,"tagSlugs":35,"__hash__":37},"blogPosts/en-us/blog/more-granular-product-usage-insights-for-gitlab-self-managed-and-dedicated.yml","More Granular Product Usage Insights For Gitlab Self Managed And Dedicated",[7],"tanuja-jayarama-raju",null,"product",{"slug":11,"featured":12,"template":13},"more-granular-product-usage-insights-for-gitlab-self-managed-and-dedicated",true,"BlogPost",{"title":15,"description":16,"authors":17,"heroImage":19,"date":20,"body":21,"category":9,"tags":22,"updatedDate":25},"More granular product usage insights for GitLab Self-Managed and Dedicated","Learn how event-level data helps GitLab improve the DevSecOps platform. Opt-out option is always available.",[18],"Tanuja Jayarama Raju","https://res.cloudinary.com/about-gitlab-com/image/upload/v1750099221/Blog/Hero%20Images/Blog/Hero%20Images/blog-image-template-1800x945%20%2811%29_78Dav6FR9EGjhebHWuBVan_1750099221690.png","2025-03-26","In GitLab 18.0, we plan to enable event-level product usage data collection from GitLab Self-Managed and GitLab Dedicated instances – while ensuring privacy, transparency, and customer control every step of the way.\n\nWe know data powers valuable insights to help you understand the performance of your DevSecOps practices. Similarly, platform usage data enables us to prioritize the investments and product improvements that drive more impact for you.\t\n\nHistorically, we’ve collected both event and aggregate product usage data from GitLab.com. However, for GitLab Self-Managed and Dedicated instances, the absence of event data has required the GitLab Customer Success team to rely on manual data extraction methods to gather key insights, including job runtimes, runner usage for cost optimization, pipeline success rates, and deployment frequency for assessing DevSecOps maturity. Access to event-level data reduces the need for workarounds and enables more efficient reporting and optimizations.\n**Note: Throughout this blog, when we discuss event collection, we are exclusively referring to the collection of events for all features except those included in GitLab Duo. For more details, please refer to our [Customer Product Usage Information page](https://handbook.gitlab.com/handbook/legal/privacy/customer-product-usage-information/).**\n\n## Understanding event-level data\n\nEvent-level data tracks product usage interactions within the GitLab platform, such as initiating CI/CD pipelines, merging a merge request, triggering a webhook, or creating a new issue. User identifiers are pseudonymized to protect privacy, and GitLab does not undertake any processes to re-identify or associate the metrics with individual users. Importantly, event-level data does not include source code or other customer-created content stored within GitLab. To learn more, visit our [Customer Product Usage Information page](https://handbook.gitlab.com/handbook/legal/privacy/customer-product-usage-information/) and [event data documentation](https://docs.gitlab.com/administration/settings/event_data/).\n\nHere is an example of a data sample we collect:\n\n![event-level data - code example](https://res.cloudinary.com/about-gitlab-com/image/upload/v1750099231/Blog/Content%20Images/Blog/Content%20Images/image2_aHR0cHM6_1750099230972.png)\n\n## How event-level data collection benefits you\n\nEvent-level data offers a wealth of insights beyond what aggregated data can provide. It enables slicing and aggregating pseudonymized system instrumentation to identify trends, highlight unused or underused areas, and signal product improvements. By analyzing usage patterns in context, we can understand which features are used, how, and in what sequence. This visibility uncovers bottlenecks and optimization opportunities that aggregated data would miss.\n\n* **In-depth feature usage analysis**    Rather than just knowing which features are used weekly or monthly, event-level data provides a clearer picture of how users experience GitLab and the frequency of their usage. This enables us to gain a deeper understanding of user behavior and highlights areas for improvement.  * **Trend discovery**    Event-level data helps identify trends in GitLab adoption that can’t be seen with rolling aggregates. With these insights, the GitLab Customer Success team can help customers make more informed decisions on feature adoption and usage, improving overall efficiency.  * **Smarter product improvements**    Event-level data gives GitLab’s Product team a clearer picture of real-world customer needs. By analyzing usage patterns, product improvements can be aligned with customer priorities, leading to continuous enhancements that make GitLab more powerful, efficient, and user-friendly.  * **Custom insights for your use case**    Event-level data will enable GitLab Customer Success to provide tailored insights based on your organization's overall product usage without identifying individual users. This flexibility helps our teams provide recommendations that address your unique needs and challenges.\n\n## You stay in control of your data\n\n![event-level data - screen of choices](https://res.cloudinary.com/about-gitlab-com/image/upload/v1750099231/Blog/Content%20Images/Blog/Content%20Images/Screenshot_2025-04-02_at_12.14.12_PM_aHR0cHM6_1750099230972.png)\n\nWe’re committed to rolling this out with a strong focus on privacy. Here’s what we’re doing to ensure transparency and choice:\n\n✅ **Pre-deployment early opt-out** – Data sharing can be disabled by instance admins in the 17.11 release before event collection begins in 18.0. The pre-deployment early opt-out option will remain available after 18.0; just upgrade to 17.11 first and disable data sharing.\n\n✅ **Proactive communication** – Updates on the progress of this initiative shared via blog posts, emails to GitLab admins, and updates through your GitLab account team.\n\n ✅ **No third-party collectors** - GitLab’s event-level instrumentation will not use any third-party collectors; it’s built and operated by GitLab, and events are sent directly to GitLab-managed environments, similar to [Service Ping](https://handbook.gitlab.com/handbook/legal/privacy/customer-product-usage-information/#service-ping-formerly-known-as-usage-ping).\n\n✅ **Detailed documentation** – Detailed documentation is available [here](https://docs.gitlab.com/administration/settings/event_data/), and a list of FAQs is available [here](http://handbook.gitlab.com/handbook/legal/privacy/product-usage-events-faq/).\n\n✅ **De-identification approach** – We will continue to apply aggregation and/or pseudonymization to any event-level data collected from Self-Managed and Dedicated.\n\n## What’s next\n\n* **Product enhancements (coming up!)** - Improvements to GitLab user experiences and adoption insights made possible by event-level data.\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 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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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.",[709],"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. More are coming throughout the year, and we hope you’ll help us make them as useful as possible.\n","2026-02-25",[107,9,24],{"featured":12,"template":13,"slug":715},"new-gitlab-metrics-and-registry-features-help-reduce-ci-cd-bottlenecks",{"content":717,"config":724},{"title":718,"description":719,"heroImage":720,"date":712,"category":9,"tags":721},"GitLab Patch Release: 18.9.1, 18.8.5, 18.7.5","Learn more about this patch release for GitLab Community Edition and Enterprise Edition.","https://res.cloudinary.com/about-gitlab-com/image/upload/v1749661926/Blog/Hero%20Images/security-patch-blog-image-r2-0506-700x400-fy25_2x.jpg",[722,723],"patch releases","security releases",{"featured":30,"template":13,"externalUrl":725},"https://about.gitlab.com/releases/2026/02/25/patch-release-gitlab-18-9-1-released/",{"content":727,"config":733},{"title":728,"description":729,"heroImage":710,"date":730,"tags":731,"category":9},"GitLab 18.9 released","Read about GitLab Duo Agent Platform self-hosted models now available for cloud licenses, vulnerability resolution with GitLab Duo Agent Platform, and more.","2026-02-19",[9,732],"releases",{"featured":30,"template":13,"externalUrl":734},"https://about.gitlab.com/releases/2026/02/19/gitlab-18-9-released/",{"promotions":736},[737,751,762],{"id":738,"categories":739,"header":741,"text":742,"button":743,"image":748},"ai-modernization",[740],"ai-ml","Is AI achieving its promise at scale?","Quiz will take 5 minutes or less",{"text":744,"config":745},"Get your AI maturity score",{"href":746,"dataGaName":747,"dataGaLocation":242},"/assessments/ai-modernization-assessment/","modernization assessment",{"config":749},{"src":750},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/qix0m7kwnd8x2fh1zq49.png",{"id":752,"categories":753,"header":754,"text":742,"button":755,"image":759},"devops-modernization",[9,557],"Are you just managing tools or shipping innovation?",{"text":756,"config":757},"Get your DevOps maturity score",{"href":758,"dataGaName":747,"dataGaLocation":242},"/assessments/devops-modernization-assessment/",{"config":760},{"src":761},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138785/eg818fmakweyuznttgid.png",{"id":763,"categories":764,"header":766,"text":742,"button":767,"image":771},"security-modernization",[765],"security","Are you trading speed for security?",{"text":768,"config":769},"Get your security maturity score",{"href":770,"dataGaName":747,"dataGaLocation":242},"/assessments/security-modernization-assessment/",{"config":772},{"src":773},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/p4pbqd9nnjejg5ds6mdk.png",{"header":775,"blurb":776,"button":777,"secondaryButton":782},"Start building faster today","See what your team can do with the intelligent orchestration platform for DevSecOps.\n",{"text":778,"config":779},"Get your free trial",{"href":780,"dataGaName":49,"dataGaLocation":781},"https://gitlab.com/-/trial_registrations/new?glm_content=default-saas-trial&glm_source=about.gitlab.com/","feature",{"text":494,"config":783},{"href":53,"dataGaName":54,"dataGaLocation":781},1772652077502]