[{"data":1,"prerenderedAt":822},["ShallowReactive",2],{"/en-us/blog/how-we-supercharged-gitlab-ci-statuses-with-websockets":3,"navigation-en-us":38,"banner-en-us":458,"footer-en-us":468,"blog-post-authors-en-us-Payton Burdette":709,"blog-related-posts-en-us-how-we-supercharged-gitlab-ci-statuses-with-websockets":724,"blog-promotions-en-us":759,"next-steps-en-us":812},{"id":4,"title":5,"authorSlugs":6,"authors":8,"body":10,"category":11,"categorySlug":11,"config":12,"content":16,"date":23,"description":17,"extension":24,"externalUrl":25,"featured":15,"heroImage":19,"isFeatured":15,"meta":26,"navigation":27,"path":28,"publishedDate":23,"rawbody":29,"seo":30,"slug":14,"stem":33,"tagSlugs":34,"tags":36,"template":13,"updatedDate":25,"__hash__":37},"blogPosts/en-us/blog/how-we-supercharged-gitlab-ci-statuses-with-websockets.md","How we supercharged GitLab CI statuses with WebSockets",[7],"payton-burdette",[9],"Payton Burdette","We just reduced API calls by 92.56% on GitLab's CI job status updates – from 45 million to 3.4 million calls per day. Instead of needing to wait up to half a minute, users now see job status changes instantly. Here's how we did it.\n\n## The problem: Polling in 2025\nIt's 2025, WebSockets are in and polling is out. Polling is more of a legacy method of getting \"real-time\" updates for software. It's time-driven, meaning clients make network calls to a server on an interval usually between 5 and 30 seconds. Even if the data hasn't changed, those network requests are made to try and get the most accurate data served to the client.\nWebSockets are event-driven, so you only make network requests to the server when the data has actually changed, i.e., a status in a database column changes from `pending` to `running`. Unlike traditional HTTP requests where the client repeatedly asks the server for updates (polling), WebSockets establish a persistent, two-way connection between the client and server. This means the server can instantly push updates to the client the moment something changes, eliminating unnecessary network traffic and reducing latency. For monitoring job statuses or real-time data, this is far more efficient than having clients poll the server every few seconds just to check if anything is different. \n## The transformation\nPreviously, the job header on the job log view was utilizing polling to get the most recent status for a single job. That component made a network request every 30 seconds no matter what to try and get the true state of the job.\n![Job header on job log view](https://res.cloudinary.com/about-gitlab-com/image/upload/v1757932872/b4zsw0zaasxnu5mm7szu.png)\n\nOur metrics showed that:\n* **547,145** network calls happened per 15 minutes\n* **45,729,530** network calls happened per 24 hours\nUsers experienced frustrating delays seeing status updates, and we were hammering our database.\n## Enter GraphQL subscriptions\n\nIn comes GraphQL subscriptions with WebSockets. GraphQL subscriptions are a feature that extends GraphQL beyond simple request-response queries and mutations, allowing clients to maintain a real-time connection to the server. While regular GraphQL queries fetch data once and return it, subscriptions let you say 'notify me whenever this specific data changes.' Under the hood, GraphQL subscriptions typically use WebSockets to maintain that persistent connection. Here's what we did:\n1. First, we refactored the job header component to use GraphQL for its data\n2. Then we implemented a GraphQL subscription to serve real-time updates with ActionCable (Rails' WebSocket framework).\n## The results\nAfter this implementation, our users now get truly real-time accurate job status – updates appear instantly when jobs change state. The performance gains are remarkable:\n* **92.56% reduction** in API calls for this component\n* Now averaging **39,670** network calls per 15 minutes (down from 547,145)\n* Only **3,403,395** network calls per 24 hours (down from 45,729,530)\nWe also monitored CPU utilization and operation rate per command over the last week and have not seen any significant increase on our services. Win-win for the software and the team.\n## What's next\nThis is just the beginning. We're working on making every CI status in the GitLab product real-time. Currently, many parts of GitLab's UI still rely on polling to check for updates. Our goal is to systematically replace these polling mechanisms with GraphQL subscriptions, giving users instant feedback across the entire CI/CD workflow.\nWant to see this capability in action? Check out any job log view and watch those status updates fly. Not a GitLab user yet? [Try GitLab Ultimate with GitLab Duo Enterprise](https://about.gitlab.com/free-trial/devsecops/) for free for 30 days.","product",{"template":13,"slug":14,"featured":15},"BlogPost","how-we-supercharged-gitlab-ci-statuses-with-websockets",false,{"title":5,"description":17,"authors":18,"heroImage":19,"tags":20,"category":11,"date":23,"body":10},"Learn how we reduced API calls on GitLab's CI job status updates from 45 million to 3.4 million calls per day. Users now see job status changes instantly instead of waiting up to 30 seconds.",[9],"https://res.cloudinary.com/about-gitlab-com/image/upload/v1749663000/Blog/Hero%20Images/tanukilifecycle.png",[11,21,22],"features","CI/CD","2025-09-15","md",null,{},true,"/en-us/blog/how-we-supercharged-gitlab-ci-statuses-with-websockets","---\nseo:\n  config:\n    noIndex: false\n  ogTitle: How we supercharged GitLab CI statuses with WebSockets\n  ogDescription: \"Learn how we reduced API calls on GitLab's CI job status updates\n    from 45 million to 3.4 million calls per day.\"\n  title: How we supercharged GitLab CI statuses with WebSockets\n  description: Learn how we reduced API calls on GitLab's CI job status updates\n    from 45 million to 3.4 million calls per day.\ntitle: How we supercharged GitLab CI statuses with WebSockets\ndescription: Learn how we reduced API calls on GitLab's CI job status updates from 45 million to 3.4 million calls per day. Users now see job status changes instantly instead of waiting up to 30 seconds.\nauthors:\n  - Payton Burdette\nheroImage: https://res.cloudinary.com/about-gitlab-com/image/upload/v1749663000/Blog/Hero%20Images/tanukilifecycle.png\ntags:\n  - product\n  - features\n  - CI/CD\ncategory: product\ndate: '2025-09-15'\nslug: how-we-supercharged-gitlab-ci-statuses-with-websockets\nfeatured: false\ntemplate: BlogPost\n---\n\nWe just reduced API calls by 92.56% on GitLab's CI job status updates – from 45 million to 3.4 million calls per day. Instead of needing to wait up to half a minute, users now see job status changes instantly. Here's how we did it.\n\n## The problem: Polling in 2025\nIt's 2025, WebSockets are in and polling is out. Polling is more of a legacy method of getting \"real-time\" updates for software. It's time-driven, meaning clients make network calls to a server on an interval usually between 5 and 30 seconds. Even if the data hasn't changed, those network requests are made to try and get the most accurate data served to the client.\nWebSockets are event-driven, so you only make network requests to the server when the data has actually changed, i.e., a status in a database column changes from `pending` to `running`. Unlike traditional HTTP requests where the client repeatedly asks the server for updates (polling), WebSockets establish a persistent, two-way connection between the client and server. This means the server can instantly push updates to the client the moment something changes, eliminating unnecessary network traffic and reducing latency. For monitoring job statuses or real-time data, this is far more efficient than having clients poll the server every few seconds just to check if anything is different. \n## The transformation\nPreviously, the job header on the job log view was utilizing polling to get the most recent status for a single job. 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First, we refactored the job header component to use GraphQL for its data\n2. Then we implemented a GraphQL subscription to serve real-time updates with ActionCable (Rails' WebSocket framework).\n## The results\nAfter this implementation, our users now get truly real-time accurate job status – updates appear instantly when jobs change state. The performance gains are remarkable:\n* **92.56% reduction** in API calls for this component\n* Now averaging **39,670** network calls per 15 minutes (down from 547,145)\n* Only **3,403,395** network calls per 24 hours (down from 45,729,530)\nWe also monitored CPU utilization and operation rate per command over the last week and have not seen any significant increase on our services. Win-win for the software and the team.\n## What's next\nThis is just the beginning. We're working on making every CI status in the GitLab product real-time. Currently, many parts of GitLab's UI still rely on polling to check for updates. 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Patch Release: 18.11.2, 18.10.5","Learn about this 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","2026-04-29",[732],"patch releases",{"featured":15,"template":13,"externalUrl":734},"https://docs.gitlab.com/releases/patches/patch-release-gitlab-18-11-2-released/",{"content":736,"config":742},{"title":737,"description":738,"heroImage":729,"date":739,"category":11,"tags":740},"GitLab Patch Release: 18.11.1, 18.10.4, 18.9.6","Discover what's in this latest patch release.","2026-04-22",[732,741],"security releases",{"featured":15,"template":13,"externalUrl":743},"https://docs.gitlab.com/releases/patches/patch-release-gitlab-18-11-1-released/",{"content":745,"config":757},{"title":746,"description":747,"body":748,"category":11,"tags":749,"date":752,"authors":753,"heroImage":756},"GitLab + Amazon: Platform orchestration on a trusted AI 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.",[285,750,751],"AWS","AI/ML","2026-04-21",[754,755],"Joe Mann","Mark Kriaf","https://res.cloudinary.com/about-gitlab-com/image/upload/v1776362275/ozbwn9tk0dditpnfddlz.png",{"featured":27,"template":13,"slug":758},"gitlab-amazon-platform-orchestration-on-a-trusted-ai-foundation",{"promotions":760},[761,775,786,798],{"id":762,"categories":763,"header":765,"text":766,"button":767,"image":772},"ai-modernization",[764],"ai-ml","Is AI achieving its promise at scale?","Quiz will take 5 minutes or less",{"text":768,"config":769},"Get your AI maturity score",{"href":770,"dataGaName":771,"dataGaLocation":248},"/assessments/ai-modernization-assessment/","modernization assessment",{"config":773},{"src":774},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/qix0m7kwnd8x2fh1zq49.png",{"id":776,"categories":777,"header":778,"text":766,"button":779,"image":783},"devops-modernization",[11,577],"Are you just managing tools or shipping innovation?",{"text":780,"config":781},"Get your DevOps maturity score",{"href":782,"dataGaName":771,"dataGaLocation":248},"/assessments/devops-modernization-assessment/",{"config":784},{"src":785},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138785/eg818fmakweyuznttgid.png",{"id":787,"categories":788,"header":790,"text":766,"button":791,"image":795},"security-modernization",[789],"security","Are you trading speed for security?",{"text":792,"config":793},"Get your security maturity score",{"href":794,"dataGaName":771,"dataGaLocation":248},"/assessments/security-modernization-assessment/",{"config":796},{"src":797},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/p4pbqd9nnjejg5ds6mdk.png",{"id":799,"paths":800,"header":803,"text":804,"button":805,"image":810},"github-azure-migration",[801,802],"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. 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