[{"data":1,"prerenderedAt":825},["ShallowReactive",2],{"/en-us/blog/more-granular-product-usage-insights-for-gitlab-self-managed-and-dedicated":3,"navigation-en-us":40,"banner-en-us":461,"footer-en-us":471,"blog-post-authors-en-us-Tanuja Jayarama Raju":712,"blog-related-posts-en-us-more-granular-product-usage-insights-for-gitlab-self-managed-and-dedicated":727,"blog-promotions-en-us":762,"next-steps-en-us":815},{"id":4,"title":5,"authorSlugs":6,"authors":8,"body":10,"category":11,"categorySlug":11,"config":12,"content":16,"date":23,"description":17,"extension":25,"externalUrl":26,"featured":15,"heroImage":19,"isFeatured":15,"meta":27,"navigation":15,"path":28,"publishedDate":23,"rawbody":29,"seo":30,"slug":14,"stem":35,"tagSlugs":36,"tags":38,"template":13,"updatedDate":24,"__hash__":39},"blogPosts/en-us/blog/more-granular-product-usage-insights-for-gitlab-self-managed-and-dedicated.md","More granular product usage insights for GitLab Self-Managed and Dedicated",[7],"tanuja-jayarama-raju",[9],"Tanuja Jayarama Raju","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. The development, release, and timing of any products, features, or functionality remain at the sole discretion of GitLab Inc.*","product",{"template":13,"slug":14,"featured":15},"BlogPost","more-granular-product-usage-insights-for-gitlab-self-managed-and-dedicated",true,{"title":5,"description":17,"authors":18,"heroImage":19,"tags":20,"category":11,"date":23,"updatedDate":24,"body":10},"Learn how event-level data helps GitLab improve the DevSecOps platform. 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Opt-out option is always available.\nauthors:\n  - Tanuja Jayarama Raju\nheroImage: 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\ntags:\n  - product\n  - DevSecOps platform\n  - features\ncategory: product\ndate: '2025-03-26'\nupdatedDate: '2025-05-14'\nslug: more-granular-product-usage-insights-for-gitlab-self-managed-and-dedicated\nfeatured: true\ntemplate: BlogPost\n---\n\nIn 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. 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foundation","Pair GitLab Duo Agent Platform with Amazon Bedrock for agentic software development and orchestration.","If your team runs GitLab and has a strong AWS practice, a new combination of Duo Agent Platform and Amazon Bedrock is just for you. The model is simple: GitLab acts as your orchestration layer to help accelerate your entire software lifecycle with agentic AI, and Bedrock is designed to provide a secure, compliant foundation model layer with AI inference behind the scenes.\n\nGitLab Duo Agent Platform enables you to handle planning, merge pipelines, security scanning, vulnerability remediation, and more as part of your GitLab workflows, while the GitLab AI Gateway routes model calls to Bedrock (or GitLab-managed Bedrock-backed endpoints, depending on your setup). That means you can build on the identity and access management (IAM) policies, virtual private cloud (VPC) boundaries, regional controls, and cloud spend commitments you already have in AWS.\n\nIf you already use Amazon Bedrock and want AI to help inside the work you already do in GitLab, not in yet another standalone chat tool, this is the pairing for you.\n\n\nIn this article, we look at the real problem many teams face today: AI is fragmented, data paths are fuzzy, and Bedrock investment gets underused when AI sits outside the software development lifecycle. Then we break down your deployment options for GitLab Duo Agent Platform:\n\n* Integrated with self-hosted models on Amazon Bedrock for GitLab Self-Managed deployments and self-hosted AI gateway   \n* Integrated with GitLab-operated models on Amazon Bedrock (with GitLab-owned keys) for GitLab Self-Managed deployments and GitLab-hosted AI gateway  \n* Integrated with GitLab-operated models on Amazon Bedrock (with GitLab-owned keys) for GitLab.com instances and GitLab-hosted AI gateway\n\nWe wrap with a summary on how this approach helps avoid shadow AI and point-tool sprawl without creating a parallel tech stack for AI tooling.\n\n## AI everywhere, control nowhere\n\nSomewhere in your company right now, software teams might be using an AI tool that your security team hasn't approved. Prompt data might be leaving your environment through a path no one has fully mapped. And your organization’s Amazon Bedrock investment might be underused while individual teams expense separate AI tools, pulling workloads and cloud spend away from the platforms you’ve already committed to.\n\nInstead of being a people problem, this might be an architecture problem. And it surfaces the same three constraints in nearly every enterprise:\n\n**Operational fragmentation.** Each team, or sometimes even an individual developer, picks their own development toolset, including AI tooling and model selection. That fragmentation makes end-to-end governance within the software development lifecycle nearly impossible.\n\n**Security and sovereignty.** Where does prompt and code data actually flow? Who owns the logs?\n\n**Cloud spend optimization.** Commitments to key cloud providers like AWS are diluted as workloads and AI usage drift to point tools outside of customers’ existing agreements.\n\nGitLab Duo Agent Platform and Amazon Bedrock help solve this together. The division of labor is straightforward: Duo Agent Platform owns the workflow orchestration with agentic AI for software development, Bedrock owns the inference layer and hosts approved foundational models, and your organization has full control over the data and policy boundaries you already defined in AWS. Three jobs, three owners, no fragmentation.\n\n## GitLab Duo Agent Platform: The agentic control plane\n\nGitLab Duo Agent Platform is GitLab's agentic AI layer: a framework of specialized agents and flows that operate simultaneously and in-parallel, going beyond the traditional stage-based handoffs  and helping automate work across the entire software lifecycle. Rather than a single assistant responding to prompts, Duo Agent Platform enables teams to orchestrate many AI agents asynchronously using unified data and project context, including issues, merge requests, pipelines, and security findings. Linear workflows are turned into coordinated, continuous collaboration between software teams and their AI agents, at scale.\n\nWith that control plane in place, the natural next question is which AI foundation should power these agents. For customers who run GitLab Self-Managed on AWS and need inference traffic, prompt data, and logs to also stay within their AWS environment along with their software lifecycle data, Amazon Bedrock acting as the AI inference layer is the natural fit. \n\n## Amazon Bedrock: The trusted AI foundation\n\nAmazon Bedrock is a fully managed, serverless foundation model layer that runs entirely within your AWS environment. Customer data stays in the customer's AWS account: inputs and outputs are encrypted in transit and at rest, never shared with model providers, and never used to train base models. Bedrock carries compliance certifications across GDPR, HIPAA, and FedRAMP High, covering many regulated industry requirements out of the box. Teams can also bring fine-tuned models from elsewhere via Custom Model Import and deploy them alongside native Bedrock models through the same infrastructure, without managing separate deployment pipelines. Bedrock Guardrails adds configurable safeguards across all models for content filtering, hallucination detection, and sensitive data protection.\n\nTogether, GitLab Duo Agent Platform and Bedrock consolidate DevSecOps orchestration and AI model governance, helping eliminate the fragmentation that happens when teams roll out AI tools independently.\n\n## Choosing your deployment path\n\nThe integration delivers the same core GitLab Duo Agent Platform capabilities regardless of how it is deployed. What varies is who runs GitLab, who operates the AI Gateway, and whose Bedrock account the inference runs through. The right pattern depends on where your organization already operates.\n\nAt a high level, the integration has three main components:\n\n* **GitLab Duo Agent Platform:** agentic workflows embedded across the software development lifecycle  \n* **AI Gateway (GitLab-managed or self-hosted):** the abstraction layer between Duo Agent Platform and the foundational model backend   \n* **Amazon Bedrock:** the AI model and inference substrate\n\n![Deployment of GitLab and AWS Bedrock](https://res.cloudinary.com/about-gitlab-com/image/upload/v1776362365/udmvmv2efpmwtkxgydch.png)\n\nChoosing a deployment pattern is informed by where an organization wants to place the levers of control. The patterns below are designed to meet teams where they already are, whether that's SaaS-first, self-managed for compliance, or all-in on AWS with existing Bedrock investments.\n\n| Deployment Model | GitLab.com instance with GitLab-hosted AI Gateway with GitLab-operated Bedrock models   | GitLab Self-Managed with GitLab-hosted AI Gateway with GitLab-operated Bedrock models | GitLab Self-Managed  with self-hosted AI Gateway and customer-operated Bedrock models |\n| :---- | :---- | :---- | :---- |\n| **Ideal if you:** | Are primarily on GitLab.com and don’t want to self-host AI gateway and Bedrock models  | Need GitLab Self-Managed for compliance and operational reasons but don’t want to manage AI layer | Are AWS-centric with existing Bedrock usage and strict data/control needs  |\n| **Key Benefits** | Fastest, turnkey way to get Duo Agent Platform workflows: GitLab runs GitLab.com, the AI Gateway, integrated with Bedrock AI models. | Keep GitLab deployed in your own environment while consuming Bedrock models via a GitLab-managed AI Gateway, combining deployment control with simplified AI operations. | Run GitLab and AI Gateway in your AWS account, reuse existing IAM/VPC/regions, keep logs and data in your environment, and draw Bedrock usage from your existing AWS spend commitments. |\n\n## How customers use GitLab Duo Agent Platform with Amazon Bedrock\n\nPlatform teams can use GitLab Duo Agent Platform with Amazon Bedrock to standardize which models handle code suggestions, security analysis, and pipeline remediation. This helps enforce guardrails and logging centrally rather than letting individual teams adopt separate tools independently.\n\nSecurity workflows see particular benefit. GitLab Duo Agent Platform agents can propose and validate fixes for security findings within GitLab, helping reduce the manual triage work developers would otherwise handle outside the platform.\n\nFor enterprises already committed to AWS, routing AI workloads through Bedrock from within GitLab enables you to keep developer AI usage aligned with existing cloud agreements rather than generating separate, unplanned spend.\n\n## Closing the loop\n\nThe constraints that slow enterprise AI adoption are often not technical. They are organizational: fragmented tooling, ungoverned data flows, and cloud spend that never consolidates. Those are the problems that can stall AI programs even after the pilots succeed.\n\nGitLab Duo Agent Platform and Amazon Bedrock help address each one directly. Platform teams get consistent governance, auditability, and standardized paths for AI usage across the software development lifecycle. Development teams get streamlined, agentic workflows that feel native to GitLab. And AWS-centric organizations get to extend their existing Bedrock investment rather than build parallel AI infrastructure alongside it.\n\nThe result is an AI program that scales without fragmenting. Governance and velocity on the same stack, serving the same teams, under policies the organization already owns.\n\n\n> To explore which deployment pattern is right for your organization and how to align GitLab Duo Agent Platform and Amazon Bedrock with your existing AWS strategy, [contact the GitLab sales team](https://about.gitlab.com/sales/) and we’ll help you design and implement the best architecture for your environment. 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