[{"data":1,"prerenderedAt":827},["ShallowReactive",2],{"/en-us/blog/monitor-application-performance-with-distributed-tracing":3,"navigation-en-us":42,"banner-en-us":463,"footer-en-us":473,"blog-post-authors-en-us-Sacha Guyon":714,"blog-related-posts-en-us-monitor-application-performance-with-distributed-tracing":729,"blog-promotions-en-us":764,"next-steps-en-us":817},{"id":4,"title":5,"authorSlugs":6,"authors":8,"body":10,"category":11,"categorySlug":11,"config":12,"content":16,"date":26,"description":17,"extension":27,"externalUrl":28,"featured":15,"heroImage":19,"isFeatured":15,"meta":29,"navigation":15,"path":30,"publishedDate":26,"rawbody":31,"seo":32,"slug":14,"stem":37,"tagSlugs":38,"tags":40,"template":13,"updatedDate":28,"__hash__":41},"blogPosts/en-us/blog/monitor-application-performance-with-distributed-tracing.md","Monitor application performance with Distributed Tracing",[7],"sacha-guyon",[9],"Sacha Guyon","Downtime due to application defects or performance issues can have devastating financial consequences for businesses. An hour of downtime is estimated to cost firms $301,000 or more, according to [Information Technology Intelligence Consulting's 2022 Global Server Hardware and Server OS Reliability Survey](https://itic-corp.com/server-and-application-by-the-numbers-understanding-the-nines/). These issues often originate from human-introduced changes, such as code or configuration changes.\n\nResolving such incidents requires development and operations teams to collaborate closely, investigating the various components of the system to find the root cause change, and promptly restore the system back to normal operation. However, these teams commonly use separate tools to build, manage, and monitor their application services and infrastructure. This approach leads to siloed data, fragmented communication, and inefficient context switching, increasing the time spent to detect and resolve incidents.\n\nGitLab aims to address this challenge by combining software delivery and monitoring functionalities within the same platform. Last year, we released [Error Tracking](https://docs.gitlab.com/operations/error_tracking/) as a general availability feature in [GitLab 16.0](https://docs.gitlab.com/releases/16/gitlab-16-0-released/#error-tracking-is-now-generally-available). Now, we're excited to announce the [Beta release of Distributed Tracing](https://docs.gitlab.com/operations/tracing/), the next step toward a comprehensive observability offering seamlessly integrated into the GitLab DevSecOps platform.\n\n## A new era of efficiency: GitLab Observability\n\nGitLab Observability empowers development and operations teams to visualize and analyze errors, traces, logs, and metrics from their applications and infrastructure. By integrating application performance monitoring into existing software delivery workflows, context switching is minimized and productivity is increased, keeping teams focused and collaborative on a unified platform.\n\nAdditionally, GitLab Observability bridges the gap between development and operations by providing insights into application performance in production. This enhances transparency, information sharing, and communication between teams. Consequently, they can detect and resolve bugs and performance issues arising from new code or configuration changes sooner and more effectively, preventing those issues from escalating into major incidents that could negatively impact the business.\n\n## What is Distributed Tracing?\n\nWith Distributed Tracing, engineers can identify the source of application performance issues. A trace represents a single user request that moves through different services and systems. Engineers are able to analyze the timing of each operation and any errors as they occur.\n\nEach trace is composed of one or more spans, which represent individual operations or units of work. Spans contain metadata like the name, timestamps, status, and relevant tags or logs. By examining the relationships between spans, developers can understand the request flow, identify performance bottlenecks, and pinpoint issues.\n\nDistributed Tracing is especially valuable for [microservices architecture](https://about.gitlab.com/topics/microservices/), where a single request may involve numerous service calls across a complex system. Tracing provides visibility into this interaction, empowering teams to quickly diagnose and resolve problems.\n\n![tracing example](https://res.cloudinary.com/about-gitlab-com/image/upload/v1750098009/Blog/Content%20Images/Blog/Content%20Images/image4_aHR0cHM6_1750098009139.png)\n\nFor example, this trace illustrates a how a user request flows through difference services to fetch product recommendations on a e-commerce website:\n\n- `User Action`: This indicates the user's initial action, such as clicking a button to request product recommendations on a product page.\n-  `Web front-end`: The web front-end sends a request to the recommendation service to retrieve product recommendations.\n- `Recommendation service`: The request from the web front-end is handled by the recommendation service, which processes the request to generate a list of recommended products.\n- `Catalog service`: The recommendation service calls the catalog service to fetch details of the recommended products. An alert icon suggests an issue or delay at this stage, such as a slow response or error in fetching product details.\n- `Database`: The catalog service queries the database to retrieve the actual product details. This span shows the SQL query in the database.\n\nBy visualizing this end-to-end trace, developers can identify performance issues – here, an error in the Catalog service – and quickly diagnose and resolve issues across the distributed system.\n\n![End-to-end trace](https://res.cloudinary.com/about-gitlab-com/image/upload/v1750098009/Blog/Content%20Images/Blog/Content%20Images/image1_aHR0cHM6_1750098009140.png)\n\n## How Distributed Tracing works\n\nHere is a breakdown of how Distributed Tracing works.\n\n### Collect data from any application with OpenTelemetry\n\nTraces and spans can be collected using [OpenTelemetry](https://opentelemetry.io/docs/what-is-opentelemetry/), an open-source observability framework that supports a wide array of SDKs and libraries across [major programming languages and frameworks](https://opentelemetry.io/docs/languages/). This framework offers a vendor-neutral approach for collecting and exporting telemetry data, enabling developers to avoid vendor lock-in and choose the tools that best fit their needs.\n\nThis means that if you are already using OpenTelemetry with another vendor, you can send data to us simply by adding our endpoint to your configuration file, making it very easy to try out our features!\n\n![Distributed tracing workflow diagram](https://res.cloudinary.com/about-gitlab-com/image/upload/v1750098009/Blog/Content%20Images/Blog/Content%20Images/image5_aHR0cHM6_1750098009141.png)\n\n### Ingest and retain data at scale with fast, real-time queries\n\nObservability requires the storage and querying of vast amounts of data while maintaining low latency for real-time analytics. To meet these needs, we developed a horizontally scalable, long-term storage solution using ClickHouse and Kubernetes, based on our [acquisition of Opstrace](https://about.gitlab.com/press/releases/2021-12-14-gitlab-acquires-opstrace-to-expand-its-devops-platform-with-open-source-observability-solution/). This [open-source platform](https://gitlab.com/gitlab-org/opstrace/opstrace) ensures rapid query performance and enterprise-grade scalability, all while minimizing costs.\n\n### Explore and analyze traces effortlessly\nAn advanced, native-level user interface is crucial for effective data exploration. We built such an interface from the ground up, starting with our Trace Explorer, which allows users to examine traces and understand their application's performance:\n- __Advanced filtering:__ Filter by services, operation names, status, and time range. Autocomplete helps simplify querying.\n- __Error highlighting:__ Easily identify error spans in search results.\n- __RED metrics:__ Visualize the Requests rate, Errors rate, and average Duration as a time-series chart for any search in real-time.\n- __Timeline view:__ Individual traces are displayed as a waterfall diagram, providing a complete view of a request distributed across different services and operations.\n- __Historical data:__ Users can query traces up to 30 days in the past.\n\n![Distributed Tracing - image 5](https://res.cloudinary.com/about-gitlab-com/image/upload/v1750098009/Blog/Content%20Images/Blog/Content%20Images/image3_aHR0cHM6_1750098009141.png)\n\n## How we use Distributed Tracing at GitLab\n[Dogfooding](https://handbook.gitlab.com/handbook/values/#dogfooding) is a core value and practice at GitLab. We've been already using early versions of Distributed Tracing for our engineering and operations needs. Here are a couple example use cases from our teams:\n\n### 1. Debug errors and performance Issues in GitLab Agent for Kubernetes\n\nThe [Environments group](https://handbook.gitlab.com/handbook/engineering/development/ops/deploy/environments/) has been using Distributed Tracing to troubleshoot and resolve issues with the [GitLab Agent for Kubernetes](https://gitlab.com/gitlab-org/cluster-integration/gitlab-agent), such as timeouts or high latency issues. The Trace List and Trace Timeline views offer valuable insights for the team to address these concerns efficiently. These traces are shared and discussed in the [related GitLab issues](https://gitlab.com/gitlab-org/cluster-integration/gitlab-agent/-/issues/386#note_1576431796), where the team collaborates on resolution.\n\n\u003Ccenter>\u003Cem>\"The Distributed Tracing feature has been invaluable in pinpointing where latency issues are occurring, allowing us to focus on the root cause and resolve it faster.\" - Mikhail, GitLab Engineer\u003C/em>\u003C/center>\u003Cp>\n\n### 2. Optimize GitLab’s build pipeline duration by identifying performance bottlenecks\n\nSlow deployments of GitLab source code can significantly impact the productivity of the whole company, as well as our compute spending. Our main repository runs [over 100,000 pipelines every month](https://gitlab.com/gitlab-org/gitlab/-/pipelines/charts). If the time it takes for these pipelines to run changes by just one minute, it can add or remove more than 2,000 hours of work time. That's 87 extra days!\n\nTo optimize pipeline execution time, GitLab's [platform engineering teams](https://handbook.gitlab.com/handbook/engineering/infrastructure/) utilize a [custom-built tool](https://gitlab.com/gitlab-com/gl-infra/gitlab-pipeline-trace) that converts GitLab deployment pipelines into traces.\n\nThe Trace Timeline view allows them to visualize the detailed execution timeline of complex pipelines and pinpoint which jobs are part of the critical path and slowing down the entire process. By identifying these bottlenecks, they can optimize job execution – for example, making the job fail faster, or running more jobs in parallel – to improve overall pipeline efficiency.\n\n![Distributed Tracing - image 6](https://res.cloudinary.com/about-gitlab-com/image/upload/v1750098009/Blog/Content%20Images/Blog/Content%20Images/image2_aHR0cHM6_1750098009143.gif)\n\n[The script is freely available](https://gitlab.com/gitlab-com/gl-infra/gitlab-pipeline-trace), so you can adapt it for your own pipelines.\n\n\u003Ccenter>\u003Cem>\"Using Distributed Tracing for our deployment pipelines has been a game-changer. It's helped us quickly identify and eliminate bottlenecks, significantly reducing our deployment times.\"- Reuben, GitLab Engineer\u003C/em>\u003C/center>\u003Cp>\n\n## What's coming next?\n\nThis release is just the start: In the next few months, we'll continue to expand our observability and monitoring features with the upcoming Metrics and Logging releases. Check out [our Observability direction page](https://docs.gitlab.com/operations/) for more info, and keep an eye out for updates!\n\n## Join the private Beta\n\nInterested in being part of this exciting journey? [Sign up to enroll in the private Beta](https://docs.gitlab.com/operations/observability/) and try out our features. Your contribution can help shape the future of observability within GitLab, ensuring our tools are perfectly aligned with your needs and challenges.\n\n> Help shape the future of GitLab Observability. [Join the Distributed Tracing Beta.](https://docs.gitlab.com/operations/observability/)","product",{"template":13,"slug":14,"featured":15},"BlogPost","monitor-application-performance-with-distributed-tracing",true,{"title":5,"description":17,"authors":18,"heroImage":19,"tags":20,"category":11,"date":26,"body":10},"Learn how Distributed Tracing helps troubleshoot application performance issues by providing end-to-end visibility and seamless collaboration across your organization.",[9],"https://res.cloudinary.com/about-gitlab-com/image/upload/v1750098000/Blog/Hero%20Images/Blog/Hero%20Images/blog-image-template-1800x945%20%288%29_5x6kH5vwjz8cwKgSBh1w11_1750098000511.png",[21,22,23,24,25],"performance","features","news","DevSecOps platform","collaboration","2024-06-13","md",null,{},"/en-us/blog/monitor-application-performance-with-distributed-tracing","---\nseo:\n  title: Monitor application performance with Distributed Tracing\n  description: >-\n    Learn how Distributed Tracing helps troubleshoot application performance\n    issues by providing end-to-end visibility and seamless collaboration across\n    your organization.\n  ogTitle: Monitor application performance with Distributed Tracing\n  ogDescription: >-\n    Learn how Distributed Tracing helps troubleshoot application performance\n    issues by providing end-to-end visibility and seamless collaboration across\n    your organization.\n  noIndex: false\n  ogImage: >-\n    https://res.cloudinary.com/about-gitlab-com/image/upload/v1750098000/Blog/Hero%20Images/Blog/Hero%20Images/blog-image-template-1800x945%20%288%29_5x6kH5vwjz8cwKgSBh1w11_1750098000511.png\n  ogUrl: >-\n    https://about.gitlab.com/blog/monitor-application-performance-with-distributed-tracing\n  ogSiteName: https://about.gitlab.com\n  ogType: article\n  canonicalUrls: >-\n    https://about.gitlab.com/blog/monitor-application-performance-with-distributed-tracing\ntitle: Monitor application performance with Distributed Tracing\ndescription: Learn how Distributed Tracing helps troubleshoot application performance issues by providing end-to-end visibility and seamless collaboration across your organization.\nauthors:\n  - Sacha Guyon\nheroImage: https://res.cloudinary.com/about-gitlab-com/image/upload/v1750098000/Blog/Hero%20Images/Blog/Hero%20Images/blog-image-template-1800x945%20%288%29_5x6kH5vwjz8cwKgSBh1w11_1750098000511.png\ntags:\n  - performance\n  - features\n  - news\n  - DevSecOps platform\n  - collaboration\ncategory: product\ndate: '2024-06-13'\nslug: monitor-application-performance-with-distributed-tracing\nfeatured: true\ntemplate: BlogPost\n---\n\nDowntime due to application defects or performance issues can have devastating financial consequences for businesses. An hour of downtime is estimated to cost firms $301,000 or more, according to [Information Technology Intelligence Consulting's 2022 Global Server Hardware and Server OS Reliability Survey](https://itic-corp.com/server-and-application-by-the-numbers-understanding-the-nines/). These issues often originate from human-introduced changes, such as code or configuration changes.\n\nResolving such incidents requires development and operations teams to collaborate closely, investigating the various components of the system to find the root cause change, and promptly restore the system back to normal operation. However, these teams commonly use separate tools to build, manage, and monitor their application services and infrastructure. This approach leads to siloed data, fragmented communication, and inefficient context switching, increasing the time spent to detect and resolve incidents.\n\nGitLab aims to address this challenge by combining software delivery and monitoring functionalities within the same platform. Last year, we released [Error Tracking](https://docs.gitlab.com/operations/error_tracking/) as a general availability feature in [GitLab 16.0](https://docs.gitlab.com/releases/16/gitlab-16-0-released/#error-tracking-is-now-generally-available). Now, we're excited to announce the [Beta release of Distributed Tracing](https://docs.gitlab.com/operations/tracing/), the next step toward a comprehensive observability offering seamlessly integrated into the GitLab DevSecOps platform.\n\n## A new era of efficiency: GitLab Observability\n\nGitLab Observability empowers development and operations teams to visualize and analyze errors, traces, logs, and metrics from their applications and infrastructure. By integrating application performance monitoring into existing software delivery workflows, context switching is minimized and productivity is increased, keeping teams focused and collaborative on a unified platform.\n\nAdditionally, GitLab Observability bridges the gap between development and operations by providing insights into application performance in production. This enhances transparency, information sharing, and communication between teams. Consequently, they can detect and resolve bugs and performance issues arising from new code or configuration changes sooner and more effectively, preventing those issues from escalating into major incidents that could negatively impact the business.\n\n## What is Distributed Tracing?\n\nWith Distributed Tracing, engineers can identify the source of application performance issues. A trace represents a single user request that moves through different services and systems. Engineers are able to analyze the timing of each operation and any errors as they occur.\n\nEach trace is composed of one or more spans, which represent individual operations or units of work. Spans contain metadata like the name, timestamps, status, and relevant tags or logs. By examining the relationships between spans, developers can understand the request flow, identify performance bottlenecks, and pinpoint issues.\n\nDistributed Tracing is especially valuable for [microservices architecture](https://about.gitlab.com/topics/microservices/), where a single request may involve numerous service calls across a complex system. Tracing provides visibility into this interaction, empowering teams to quickly diagnose and resolve problems.\n\n![tracing example](https://res.cloudinary.com/about-gitlab-com/image/upload/v1750098009/Blog/Content%20Images/Blog/Content%20Images/image4_aHR0cHM6_1750098009139.png)\n\nFor example, this trace illustrates a how a user request flows through difference services to fetch product recommendations on a e-commerce website:\n\n- `User Action`: This indicates the user's initial action, such as clicking a button to request product recommendations on a product page.\n-  `Web front-end`: The web front-end sends a request to the recommendation service to retrieve product recommendations.\n- `Recommendation service`: The request from the web front-end is handled by the recommendation service, which processes the request to generate a list of recommended products.\n- `Catalog service`: The recommendation service calls the catalog service to fetch details of the recommended products. An alert icon suggests an issue or delay at this stage, such as a slow response or error in fetching product details.\n- `Database`: The catalog service queries the database to retrieve the actual product details. This span shows the SQL query in the database.\n\nBy visualizing this end-to-end trace, developers can identify performance issues – here, an error in the Catalog service – and quickly diagnose and resolve issues across the distributed system.\n\n![End-to-end trace](https://res.cloudinary.com/about-gitlab-com/image/upload/v1750098009/Blog/Content%20Images/Blog/Content%20Images/image1_aHR0cHM6_1750098009140.png)\n\n## How Distributed Tracing works\n\nHere is a breakdown of how Distributed Tracing works.\n\n### Collect data from any application with OpenTelemetry\n\nTraces and spans can be collected using [OpenTelemetry](https://opentelemetry.io/docs/what-is-opentelemetry/), an open-source observability framework that supports a wide array of SDKs and libraries across [major programming languages and frameworks](https://opentelemetry.io/docs/languages/). This framework offers a vendor-neutral approach for collecting and exporting telemetry data, enabling developers to avoid vendor lock-in and choose the tools that best fit their needs.\n\nThis means that if you are already using OpenTelemetry with another vendor, you can send data to us simply by adding our endpoint to your configuration file, making it very easy to try out our features!\n\n![Distributed tracing workflow diagram](https://res.cloudinary.com/about-gitlab-com/image/upload/v1750098009/Blog/Content%20Images/Blog/Content%20Images/image5_aHR0cHM6_1750098009141.png)\n\n### Ingest and retain data at scale with fast, real-time queries\n\nObservability requires the storage and querying of vast amounts of data while maintaining low latency for real-time analytics. To meet these needs, we developed a horizontally scalable, long-term storage solution using ClickHouse and Kubernetes, based on our [acquisition of Opstrace](https://about.gitlab.com/press/releases/2021-12-14-gitlab-acquires-opstrace-to-expand-its-devops-platform-with-open-source-observability-solution/). This [open-source platform](https://gitlab.com/gitlab-org/opstrace/opstrace) ensures rapid query performance and enterprise-grade scalability, all while minimizing costs.\n\n### Explore and analyze traces effortlessly\nAn advanced, native-level user interface is crucial for effective data exploration. We built such an interface from the ground up, starting with our Trace Explorer, which allows users to examine traces and understand their application's performance:\n- __Advanced filtering:__ Filter by services, operation names, status, and time range. Autocomplete helps simplify querying.\n- __Error highlighting:__ Easily identify error spans in search results.\n- __RED metrics:__ Visualize the Requests rate, Errors rate, and average Duration as a time-series chart for any search in real-time.\n- __Timeline view:__ Individual traces are displayed as a waterfall diagram, providing a complete view of a request distributed across different services and operations.\n- __Historical data:__ Users can query traces up to 30 days in the past.\n\n![Distributed Tracing - image 5](https://res.cloudinary.com/about-gitlab-com/image/upload/v1750098009/Blog/Content%20Images/Blog/Content%20Images/image3_aHR0cHM6_1750098009141.png)\n\n## How we use Distributed Tracing at GitLab\n[Dogfooding](https://handbook.gitlab.com/handbook/values/#dogfooding) is a core value and practice at GitLab. We've been already using early versions of Distributed Tracing for our engineering and operations needs. Here are a couple example use cases from our teams:\n\n### 1. Debug errors and performance Issues in GitLab Agent for Kubernetes\n\nThe [Environments group](https://handbook.gitlab.com/handbook/engineering/development/ops/deploy/environments/) has been using Distributed Tracing to troubleshoot and resolve issues with the [GitLab Agent for Kubernetes](https://gitlab.com/gitlab-org/cluster-integration/gitlab-agent), such as timeouts or high latency issues. The Trace List and Trace Timeline views offer valuable insights for the team to address these concerns efficiently. These traces are shared and discussed in the [related GitLab issues](https://gitlab.com/gitlab-org/cluster-integration/gitlab-agent/-/issues/386#note_1576431796), where the team collaborates on resolution.\n\n\u003Ccenter>\u003Cem>\"The Distributed Tracing feature has been invaluable in pinpointing where latency issues are occurring, allowing us to focus on the root cause and resolve it faster.\" - Mikhail, GitLab Engineer\u003C/em>\u003C/center>\u003Cp>\n\n### 2. Optimize GitLab’s build pipeline duration by identifying performance bottlenecks\n\nSlow deployments of GitLab source code can significantly impact the productivity of the whole company, as well as our compute spending. Our main repository runs [over 100,000 pipelines every month](https://gitlab.com/gitlab-org/gitlab/-/pipelines/charts). If the time it takes for these pipelines to run changes by just one minute, it can add or remove more than 2,000 hours of work time. That's 87 extra days!\n\nTo optimize pipeline execution time, GitLab's [platform engineering teams](https://handbook.gitlab.com/handbook/engineering/infrastructure/) utilize a [custom-built tool](https://gitlab.com/gitlab-com/gl-infra/gitlab-pipeline-trace) that converts GitLab deployment pipelines into traces.\n\nThe Trace Timeline view allows them to visualize the detailed execution timeline of complex pipelines and pinpoint which jobs are part of the critical path and slowing down the entire process. By identifying these bottlenecks, they can optimize job execution – for example, making the job fail faster, or running more jobs in parallel – to improve overall pipeline efficiency.\n\n![Distributed Tracing - image 6](https://res.cloudinary.com/about-gitlab-com/image/upload/v1750098009/Blog/Content%20Images/Blog/Content%20Images/image2_aHR0cHM6_1750098009143.gif)\n\n[The script is freely available](https://gitlab.com/gitlab-com/gl-infra/gitlab-pipeline-trace), so you can adapt it for your own pipelines.\n\n\u003Ccenter>\u003Cem>\"Using Distributed Tracing for our deployment pipelines has been a game-changer. It's helped us quickly identify and eliminate bottlenecks, significantly reducing our deployment times.\"- Reuben, GitLab Engineer\u003C/em>\u003C/center>\u003Cp>\n\n## What's coming next?\n\nThis release is just the start: In the next few months, we'll continue to expand our observability and monitoring features with the upcoming Metrics and Logging releases. Check out [our Observability direction page](https://docs.gitlab.com/operations/) for more info, and keep an eye out for updates!\n\n## Join the private Beta\n\nInterested in being part of this exciting journey? [Sign up to enroll in the private Beta](https://docs.gitlab.com/operations/observability/) and try out our features. Your contribution can help shape the future of observability within GitLab, ensuring our tools are perfectly aligned with your needs and challenges.\n\n> Help shape the future of GitLab Observability. 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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. You can also [visit our AWS partner page](https://about.gitlab.com/partners/technology-partners/aws/) to learn more.",[290,755,756],"AWS","AI/ML","2026-04-21",[759,760],"Joe Mann","Mark Kriaf","https://res.cloudinary.com/about-gitlab-com/image/upload/v1776362275/ozbwn9tk0dditpnfddlz.png",{"featured":15,"template":13,"slug":763},"gitlab-amazon-platform-orchestration-on-a-trusted-ai-foundation",{"promotions":765},[766,780,791,803],{"id":767,"categories":768,"header":770,"text":771,"button":772,"image":777},"ai-modernization",[769],"ai-ml","Is AI achieving its promise at scale?","Quiz will take 5 minutes or less",{"text":773,"config":774},"Get your AI maturity score",{"href":775,"dataGaName":776,"dataGaLocation":253},"/assessments/ai-modernization-assessment/","modernization assessment",{"config":778},{"src":779},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/qix0m7kwnd8x2fh1zq49.png",{"id":781,"categories":782,"header":783,"text":771,"button":784,"image":788},"devops-modernization",[11,582],"Are you just managing tools or shipping innovation?",{"text":785,"config":786},"Get your DevOps maturity score",{"href":787,"dataGaName":776,"dataGaLocation":253},"/assessments/devops-modernization-assessment/",{"config":789},{"src":790},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138785/eg818fmakweyuznttgid.png",{"id":792,"categories":793,"header":795,"text":771,"button":796,"image":800},"security-modernization",[794],"security","Are you trading speed for security?",{"text":797,"config":798},"Get your security maturity score",{"href":799,"dataGaName":776,"dataGaLocation":253},"/assessments/security-modernization-assessment/",{"config":801},{"src":802},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/p4pbqd9nnjejg5ds6mdk.png",{"id":804,"paths":805,"header":808,"text":809,"button":810,"image":815},"github-azure-migration",[806,807],"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":811,"config":812},"See how GitLab compares to GitHub",{"href":813,"dataGaName":814,"dataGaLocation":253},"/compare/gitlab-vs-github/github-azure-migration/","github azure migration",{"config":816},{"src":790},{"header":818,"blurb":819,"button":820,"secondaryButton":825},"Start building faster today","See what your team can do with the intelligent orchestration platform for DevSecOps.\n",{"text":821,"config":822},"Get your free trial",{"href":823,"dataGaName":52,"dataGaLocation":824},"https://gitlab.com/-/trial_registrations/new?glm_content=default-saas-trial&glm_source=about.gitlab.com/","feature",{"text":519,"config":826},{"href":56,"dataGaName":57,"dataGaLocation":824},1777934944231]