[{"data":1,"prerenderedAt":814},["ShallowReactive",2],{"/en-us/blog/automate-remediation-with-ready-to-merge-ai-code-fixes":3,"navigation-en-us":39,"banner-en-us":450,"footer-en-us":460,"blog-post-authors-en-us-Alisa Ho":702,"blog-related-posts-en-us-automate-remediation-with-ready-to-merge-ai-code-fixes":715,"blog-promotions-en-us":752,"next-steps-en-us":804},{"id":4,"title":5,"authorSlugs":6,"authors":8,"body":10,"category":11,"categorySlug":11,"config":12,"content":16,"date":20,"description":17,"extension":25,"externalUrl":26,"featured":13,"heroImage":19,"isFeatured":13,"meta":27,"navigation":28,"path":29,"publishedDate":20,"rawbody":30,"seo":31,"slug":15,"stem":34,"tagSlugs":35,"tags":37,"template":14,"updatedDate":26,"__hash__":38},"blogPosts/en-us/blog/automate-remediation-with-ready-to-merge-ai-code-fixes.yml","GitLab 18.11: Automate remediation with ready-to-merge AI code fixes",[7],"alisa-ho",[9],"Alisa Ho","AI is writing code faster than any security team can review it. What used to be a manageable backlog of static application security testing (SAST) vulnerabilities is now an overwhelming list  that has become difficult to parse. Expecting developers to manually research and fix each one isn't a process, it's a bottleneck. The answer isn't more human effort. It's an autonomous pipeline. [Agentic SAST Vulnerability Resolution](https://docs.gitlab.com/user/application_security/vulnerabilities/agentic_vulnerability_resolution/) within GitLab Duo Agent Platform is built for that exact problem.\n\nNow generally available, Agentic SAST Vulnerability Resolution automatically generates ready-to-merge code fixes to remediate SAST vulnerabilities. With this capability:\n\n* Developers stay in flow  \n* Vulnerabilities get resolved before they reach production  \n* AppSec teams spend less time on triage and chasing down developers to close the loop \n\nAgentic SAST Vulnerability Resolution is the future of application security. GitLab 18.11 also delivers faster SAST scanning, smarter prioritization, and tighter governance across the platform.\n\n## Auto-remediation without breaking your flow\n\nWhen AI is generating code at scale, the math changes. A security backlog that once grew linearly now compounds with every model-assisted commit. There is no version of this problem that gets solved by asking developers to context-switch more and continue manually remediating vulnerabilities. According to [GitLab's 2025 DevSecOps Report,](https://about.gitlab.com/resources/developer-survey/) developers already spend 11 hours per month remediating vulnerabilities post-release — that is, fixing issues that are already exploitable in production instead of shipping new work.\n\nAgentic SAST Vulnerability Resolution changes the economics of that cycle. When a SAST scan completes, findings automatically kick off the [SAST false positive detection](https://docs.gitlab.com/user/application_security/vulnerabilities/false_positive_detection/) flow. Confirmed true positives go directly into the Agentic SAST Vulnerability Resolution Flow, where GitLab Duo Agent Platform:\n\n* Analyzes the vulnerability in context  \n* Generates a fix that addresses the root cause  \n* Validates the fix through automated testing  \n\nThe developer receives a ready-to-merge MR with a confidence score so they can make an informed decision on how to remediate the vulnerability. The sprint stays on track, developers stay in flow, and vulnerabilities get resolved before they ever reach production.\n\nAccelerating software production also means not waiting on your scanner. GitLab 18.11 introduces [incremental scanning for Advanced SAST](https://docs.gitlab.com/user/application_security/sast/gitlab_advanced_sast/#incremental-scanning), so developers get vulnerability results without waiting for a full scan to complete, and pipelines keep moving.\n\u003Ciframe src=\"https://player.vimeo.com/video/1183195999?badge=0&autopause=0&player_id=0&app_id=58479%2Fembed\" allow=\"autoplay; fullscreen; picture-in-picture\" allowfullscreen=\"\" frameborder=\"0\" style=\"position:absolute;top:0;left:0;width:100%;height:100%;\">\u003C/iframe>\n\n\n## Remediate by business risk, not just by score\n\nAutonomous remediation only works if the signal driving it is trustworthy. When severity scores don't reflect real exploitability, developers stop trusting the signal and start ignoring it.\n\nGitLab 18.11 addresses this issue on four levels. First, [vulnerability scores](https://docs.gitlab.com/user/application_security/vulnerabilities/severities/#critical-severity) are now grounded in Common Vulnerability Scoring System (CVSS) 4.0, the most current industry standard, with more granular metrics that better capture real-world exploitability. The score developers see in GitLab reflects the most current industry standard for measuring real-world risk.\n\nFrom there, AppSec teams can define [policy-based rules](https://docs.gitlab.com/user/application_security/policies/vulnerability_management_policy/#severity-override-policies) that automatically adjust vulnerability severity scores based on signals like Common Vulnerabilities and Exposures (CVE), Common Weakness Enumeration (CWE), and file path/directory. Once a policy is set, the severity overrides apply immediately so developers work from a backlog that reflects actual business risk, not raw scanner output.\n\nRisk-based enforcement doesn't stop at the backlog. AppSec teams can now configure [approval policies to block](https://docs.gitlab.com/user/application_security/policies/merge_request_approval_policies/#vulnerability_attributes-object) or warn based on Known Exploited Vulnerabilities (KEV) status or Exploit Prediction Scoring System (EPSS) score thresholds. When a merge gets blocked, developers know it's because the vulnerability has real-world exploitability data behind it, not a score that didn't account for their environment.\n\nLastly, the [new Top CWEs security dashboard chart](https://docs.gitlab.com/user/application_security/security_dashboard/#top-10-cwes) gives teams visibility into which vulnerability classes are appearing most frequently across their projects. Instead of chasing individual findings, teams can identify patterns, prioritize at the root cause-level, and address systemic risk before it compounds.\n\n## Stronger security controls with less operational overhead\n\nAn autonomous remediation pipeline is only as good as the security scanner coverage underneath it. If the scanner enablement is inconsistent, the findings flowing into the pipeline are incomplete and so are the fixes.\n\nGitLab 18.11 introduces [Security Manager](https://docs.gitlab.com/user/permissions/#default-roles), a new default role built specifically for security professionals. With the Security Manager role, security teams can enforce security scanners, define and configure security policies, manage vulnerability triage and remediation workflows, and maintain compliance frameworks and audit streams, without needing code modification or deployment permissions. Security teams get the access necessary for their jobs, and no more, keeping permissions scoped to the work at hand and keeping code and deployment permissions with developers.\n\nFor AppSec teams, getting consistent SAST scanner coverage across multiple projects and groups just got significantly easier. [SAST configuration profiles](https://docs.gitlab.com/user/application_security/configuration/security_configuration_profiles/) give security teams a single place to define scanning once and apply it across every project in a group in one action. Teams no longer have to write and maintain YAML policy files, depend on developers to configure scanners, or manually check each project to find coverage gaps.\n\n## Get started with agentic vulnerability remediation today\n\nGitLab 18.11 delivers the full vulnerability workflow in one platform: AI that automatically remediates vulnerabilities, smarter prioritization that cuts through vulnerability noise, and governance controls that give security teams the right access and coverage at scale.\n\n> To see how GitLab Duo Agent Platform puts automated remediation directly in your developer workflow, [start a free trial of GitLab Ultimate today](https://about.gitlab.com/free-trial/?utm_medium=blog&utm_source=blog&utm_campaign=eg_global_x_inbound-request_security_en_).","product",{"featured":13,"template":14,"slug":15},false,"BlogPost","automate-remediation-with-ready-to-merge-ai-code-fixes",{"title":5,"description":17,"authors":18,"heroImage":19,"date":20,"body":10,"category":11,"tags":21},"With GitLab 18.11, Agentic SAST Vulnerability Resolution becomes generally available, alleviating security bottlenecks.",[9],"https://res.cloudinary.com/about-gitlab-com/image/upload/v1776259080/cakqnwo5ecp255lo8lzo.png","2026-04-16",[22,23,11,24],"security","AI/ML","features","yml",null,{},true,"/en-us/blog/automate-remediation-with-ready-to-merge-ai-code-fixes","seo:\n  config:\n    noIndex: false\n  title: Automate remediation with ready-to-merge AI code fixes\n  description: With GitLab 18.11, Agentic SAST Vulnerability Resolution becomes\n    generally available, alleviating security bottlenecks.\ncontent:\n  title: 'GitLab 18.11: Automate remediation with ready-to-merge AI code fixes'\n  description: With GitLab 18.11, Agentic SAST Vulnerability Resolution becomes\n    generally available, alleviating security bottlenecks.\n  authors:\n    - Alisa Ho\n  heroImage: https://res.cloudinary.com/about-gitlab-com/image/upload/v1776259080/cakqnwo5ecp255lo8lzo.png\n  date: 2026-04-16\n  body: >-\n    AI is writing code faster than any security team can review it. What used to\n    be a manageable backlog of static application security testing (SAST)\n    vulnerabilities is now an overwhelming list  that has become difficult to\n    parse. Expecting developers to manually research and fix each one isn't a\n    process, it's a bottleneck. The answer isn't more human effort. It's an\n    autonomous pipeline. [Agentic SAST Vulnerability\n    Resolution](https://docs.gitlab.com/user/application_security/vulnerabilities/agentic_vulnerability_resolution/)\n    within GitLab Duo Agent Platform is built for that exact problem.\n\n\n    Now generally available, Agentic SAST Vulnerability Resolution automatically generates ready-to-merge code fixes to remediate SAST vulnerabilities. With this capability:\n\n\n    * Developers stay in flow  \n\n    * Vulnerabilities get resolved before they reach production  \n\n    * AppSec teams spend less time on triage and chasing down developers to close the loop \n\n\n    Agentic SAST Vulnerability Resolution is the future of application security. GitLab 18.11 also delivers faster SAST scanning, smarter prioritization, and tighter governance across the platform.\n\n\n    ## Auto-remediation without breaking your flow\n\n\n    When AI is generating code at scale, the math changes. A security backlog that once grew linearly now compounds with every model-assisted commit. There is no version of this problem that gets solved by asking developers to context-switch more and continue manually remediating vulnerabilities. According to [GitLab's 2025 DevSecOps Report,](https://about.gitlab.com/resources/developer-survey/) developers already spend 11 hours per month remediating vulnerabilities post-release — that is, fixing issues that are already exploitable in production instead of shipping new work.\n\n\n    Agentic SAST Vulnerability Resolution changes the economics of that cycle. When a SAST scan completes, findings automatically kick off the [SAST false positive detection](https://docs.gitlab.com/user/application_security/vulnerabilities/false_positive_detection/) flow. Confirmed true positives go directly into the Agentic SAST Vulnerability Resolution Flow, where GitLab Duo Agent Platform:\n\n\n    * Analyzes the vulnerability in context  \n\n    * Generates a fix that addresses the root cause  \n\n    * Validates the fix through automated testing  \n\n\n    The developer receives a ready-to-merge MR with a confidence score so they can make an informed decision on how to remediate the vulnerability. The sprint stays on track, developers stay in flow, and vulnerabilities get resolved before they ever reach production.\n\n\n    Accelerating software production also means not waiting on your scanner. GitLab 18.11 introduces [incremental scanning for Advanced SAST](https://docs.gitlab.com/user/application_security/sast/gitlab_advanced_sast/#incremental-scanning), so developers get vulnerability results without waiting for a full scan to complete, and pipelines keep moving.\n\n    \u003Ciframe src=\"https://player.vimeo.com/video/1183195999?badge=0&autopause=0&player_id=0&app_id=58479%2Fembed\" allow=\"autoplay; fullscreen; picture-in-picture\" allowfullscreen=\"\" frameborder=\"0\" style=\"position:absolute;top:0;left:0;width:100%;height:100%;\">\u003C/iframe>\n\n\n\n    ## Remediate by business risk, not just by score\n\n\n    Autonomous remediation only works if the signal driving it is trustworthy. When severity scores don't reflect real exploitability, developers stop trusting the signal and start ignoring it.\n\n\n    GitLab 18.11 addresses this issue on four levels. First, [vulnerability scores](https://docs.gitlab.com/user/application_security/vulnerabilities/severities/#critical-severity) are now grounded in Common Vulnerability Scoring System (CVSS) 4.0, the most current industry standard, with more granular metrics that better capture real-world exploitability. The score developers see in GitLab reflects the most current industry standard for measuring real-world risk.\n\n\n    From there, AppSec teams can define [policy-based rules](https://docs.gitlab.com/user/application_security/policies/vulnerability_management_policy/#severity-override-policies) that automatically adjust vulnerability severity scores based on signals like Common Vulnerabilities and Exposures (CVE), Common Weakness Enumeration (CWE), and file path/directory. Once a policy is set, the severity overrides apply immediately so developers work from a backlog that reflects actual business risk, not raw scanner output.\n\n\n    Risk-based enforcement doesn't stop at the backlog. AppSec teams can now configure [approval policies to block](https://docs.gitlab.com/user/application_security/policies/merge_request_approval_policies/#vulnerability_attributes-object) or warn based on Known Exploited Vulnerabilities (KEV) status or Exploit Prediction Scoring System (EPSS) score thresholds. When a merge gets blocked, developers know it's because the vulnerability has real-world exploitability data behind it, not a score that didn't account for their environment.\n\n\n    Lastly, the [new Top CWEs security dashboard chart](https://docs.gitlab.com/user/application_security/security_dashboard/#top-10-cwes) gives teams visibility into which vulnerability classes are appearing most frequently across their projects. Instead of chasing individual findings, teams can identify patterns, prioritize at the root cause-level, and address systemic risk before it compounds.\n\n\n    ## Stronger security controls with less operational overhead\n\n\n    An autonomous remediation pipeline is only as good as the security scanner coverage underneath it. If the scanner enablement is inconsistent, the findings flowing into the pipeline are incomplete and so are the fixes.\n\n\n    GitLab 18.11 introduces [Security Manager](https://docs.gitlab.com/user/permissions/#default-roles), a new default role built specifically for security professionals. With the Security Manager role, security teams can enforce security scanners, define and configure security policies, manage vulnerability triage and remediation workflows, and maintain compliance frameworks and audit streams, without needing code modification or deployment permissions. Security teams get the access necessary for their jobs, and no more, keeping permissions scoped to the work at hand and keeping code and deployment permissions with developers.\n\n\n    For AppSec teams, getting consistent SAST scanner coverage across multiple projects and groups just got significantly easier. [SAST configuration profiles](https://docs.gitlab.com/user/application_security/configuration/security_configuration_profiles/) give security teams a single place to define scanning once and apply it across every project in a group in one action. Teams no longer have to write and maintain YAML policy files, depend on developers to configure scanners, or manually check each project to find coverage gaps.\n\n\n    ## Get started with agentic vulnerability remediation today\n\n\n    GitLab 18.11 delivers the full vulnerability workflow in one platform: AI that automatically remediates vulnerabilities, smarter prioritization that cuts through vulnerability noise, and governance controls that give security teams the right access and coverage at scale.\n\n\n    > To see how GitLab Duo Agent Platform puts automated remediation directly in your developer workflow, [start a free trial of GitLab Ultimate today](https://about.gitlab.com/free-trial/?utm_medium=blog&utm_source=blog&utm_campaign=eg_global_x_inbound-request_security_en_).\n  category: product\n  tags:\n    - security\n    - AI/ML\n    - product\n    - features\nconfig:\n  featured: false\n  template: BlogPost\n  slug: 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what's in this latest patch release.","https://res.cloudinary.com/about-gitlab-com/image/upload/v1749661926/Blog/Hero%20Images/security-patch-blog-image-r2-0506-700x400-fy25_2x.jpg","2026-04-22",[723,724],"patch releases","security releases",{"featured":13,"template":14,"externalUrl":726},"https://docs.gitlab.com/releases/patches/patch-release-gitlab-18-11-1-released/",{"content":728,"config":739},{"title":729,"description":730,"body":731,"category":11,"tags":732,"date":734,"authors":735,"heroImage":738},"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.",[281,733,23],"AWS","2026-04-21",[736,737],"Joe Mann","Mark Kriaf","https://res.cloudinary.com/about-gitlab-com/image/upload/v1776362275/ozbwn9tk0dditpnfddlz.png",{"featured":28,"template":14,"slug":740},"gitlab-amazon-platform-orchestration-on-a-trusted-ai-foundation",{"content":742,"config":750},{"title":743,"description":744,"authors":745,"heroImage":19,"date":20,"body":747,"category":11,"tags":748},"GitLab 18.11: Budget guardrails for GitLab Credits","Learn how new spending caps and per-user credit limits give organizations the budget guardrails to scale GitLab Duo Agent Platform.",[746],"Bryan Rothwell","Teams using GitLab Duo Agent Platform with on-demand GitLab Credits are shipping faster, catching bugs earlier, and automating tasks that used to take entire sprints. But as adoption grows, so does oversight from finance, procurement, and platform teams to prove that AI spending is bounded, predictable, and controllable.\n\nOne of the greatest barriers to broader AI adoption isn't skepticism about the technology. It's uncertainty about managing spend. Without budget caps, a busy month could produce unexpected expenses. Without per-user limits, a handful of power users could burn through the team's credits before the month is over. And without either, engineering leaders who want to expand their use of agentic AI for software development have to jump through more hoops for budget approval.\n\nSince its [general availability](https://about.gitlab.com/blog/gitlab-duo-agent-platform-is-generally-available/), GitLab Duo Agent Platform has provided usage governance and visibility. With GitLab 18.11, we're introducing usage controls for [GitLab Credits](https://about.gitlab.com/blog/introducing-gitlab-credits/): spending caps and budget guardrails that give your organization even more control and transparency over how credits are consumed.\n\n## Managing GitLab Credits\n\nGitLab 18.11 adds three layers of control over GitLab Credits consumption: a subscription-level spending cap, per-user credit limits, and visibility into cap status and enforcement.\n\n### Subscription-level spending cap\n\nBilling account managers can now set a hard monthly ceiling for on-demand GitLab Credits consumption for their entire subscription.\n\nHere's how it works:\n\n* **Set a cap** in the `Customers Portal` under your subscription's GitLab Credits settings.  \n* **Enforce spend limits automatically.**  When on-demand usage reaches the cap, DAP access is paused for all users on that subscription until the next monthly period begins.  \n* **Make adjustments as you go.** Raise or disable the cap mid-month to restore access.\n\nThe cap resets each monthly period and your configured limit carries forward unless you change it. Because usage data is synchronized periodically rather than in real time, a small amount of additional usage may occur after the cap is reached before enforcement takes effect. See the [GitLab Credits documentation](https://docs.gitlab.com/subscriptions/gitlab_credits/) for details.\n\n### User-level spending caps\n\nNot every user consumes credits at the same rate, and that's expected. But when one or two power users account for a disproportionate share of the pool, the rest of the team can lose access before the month is over.\n\nPer-user credit caps prevent any single user from consuming more than their fair share:\n\n* **Flat per-user cap.** Set a uniform credit limit that applies equally to every user on the subscription through the GitLab GraphQL API. Unlike the subscription-level cap, the per-user cap applies to a user's total consumption across all credit sources.  \n* **Custom per-user overrides.** For organizations that need differentiated limits, you can set individual credit caps for specific users through the GraphQL API. For example, you could give your staff engineers a higher allocation while applying a standard limit to the broader team.  \n* **Individual enforcement.** When a user reaches their cap, they retain full access to GitLab. Only their Duo Agent Platform credit usage is paused until the next billing cycle. Everyone else keeps working uninterrupted until they hit their own limit or the subscription-level cap is reached, whichever comes first.\n\n### Visibility and notifications\n\nWhen a subscription-level cap is reached, GitLab sends an email notification to billing account managers so they can take action: raise the cap, wait for the next period, or redistribute credits.\n\nWithin GitLab, group owners (GitLab.com) and instance administrators (Self-Managed) can view which users have been blocked due to reaching their per-user cap and restore access by adjusting the cap through the GraphQL API. \n\n## How budget guardrails help organizations scale AI usage\n\nGuardrails are essential as organizations ramp up their AI adoption. Here's why:\n\n### Predictable AI budgets\n\nUsage controls for GitLab Duo Agent Platform turn AI into a bounded, predictable budget item using on-demand GitLab Credits. That makes it easier to deploy agents across the software development lifecycle and get sign-off from finance, justify renewals, and plan quarterly spend.\n\n### Governance and chargeback\n\nLarge organizations often need to align AI consumption with internal budgets, cost centers, or departmental policies. Per-user caps give platform teams a straightforward mechanism to allocate credits fairly and track consumption at the individual level. The API import options make it practical to manage caps at enterprise scale. Combined with per-user usage data from the GitLab Credits dashboard, organizations can track consumption patterns to inform their own internal chargeback or budget allocation processes.\n\n### Confidence to scale\n\nMany customers start GitLab Duo Agent Platform with a small pilot group. Usage controls remove risks associated with expanding that pilot across the organization. You can roll out Duo Agent Platform to hundreds or thousands of developers knowing there's a hard ceiling protecting your budget. If usage grows faster than expected, you'll hit the cap, not an unexpected invoice.\n\n## Addressing the seat-based and visibility conundrum\n\nMany AI coding tools take a seat-based approach to cost management. You buy a fixed number of seats at a flat per-user price, and that's your budget. It's simple, but rigid. You pay the same whether a developer uses the tool ten times a day or never touches it. And as vendors introduce premium models and usage-based overages on top of seat pricing, the cost predictability that seat-based licensing promised starts to erode.\n\n\nGitLab takes a different approach. Usage-based pricing with hard caps and a single governance dashboard. You get the flexibility of paying for what your teams actually use, with the budget predictability of enforced spending limits.\n\n## Real-world usage controls\n\n**One example is a mid-size SaaS customer that wants to protect their monthly budget.** A 200-person engineering organization sets a subscription-level cap equal to their expected on-demand usage. Their VP of Engineering can confidently tell finance that GitLab Duo Agent Platform spend will never exceed the approved amount, even as they onboard new teams. If they approach the cap mid-month, the billing account manager gets a notification and can decide whether to raise the limit or wait for the next period.\n\n**At GitLab, we also work with large enterprises that want to keep usage fair across teams.** A global financial services company with 2,000 developers uses per-user caps to ensure equitable access. Staff engineers working on complex refactoring projects get a higher individual allocation via API, while most developers receive a standard flat cap. No single user can exhaust the pool, and the platform team uses the per-user usage data in the GitLab Credits dashboard to track consumption patterns and inform quarterly budget planning.\n\n## Getting started\n\nUsage controls are available for both GitLab.com and Self-Managed customers running GitLab 18.11. Different controls are configured in different places depending on the scope and your role.\n\n**Subscription-level cap**\n\nBilling account managers set the subscription-level on-demand cap in the Customers Portal:\n\n1. Sign in to the `Customers Portal`.  \n2. On your subscription card, navigate to **GitLab Credits** settings.  \n3. Enable the monthly on-demand credits cap and enter your desired limit.\n\n**Flat per-user cap**\n\nThe flat per-user cap can be set through the GitLab GraphQL API by namespace owners (GitLab.com) or instance administrators (Self-Managed). Check the [GitLab Credits documentation](https://docs.gitlab.com/subscriptions/gitlab_credits/) for the latest on available configuration surfaces.\n\n**Custom per-user overrides**\n\nFor differentiated limits, namespace owners (GitLab.com) and instance administrators (Self-Managed) can set individual caps programmatically. This is useful for automation and infrastructure-as-code workflows.\n\n**Monitor usage and cap status**\n\n* **Customers Portal:** View detailed usage and cap status.  \n* **GitLab.com:** Group owners can view blocked users under **Settings > GitLab Credits**.  \n* **Self-Managed:** Instance administrators can view cap status and blocked users under **Admin > GitLab Credits**.\n\n## GitLab Duo Agent Platform is ready to scale\n\nUsage controls are available now in GitLab 18.11. If you've been waiting for the right guardrails before expanding GitLab Duo Agent Platform across your organization, this is your moment. Set your caps, roll out Duo Agent Platform to more teams, and start shipping faster!\n\n> [Learn more about GitLab Credits and usage controls](https://docs.gitlab.com/subscriptions/gitlab_credits/).",[11,23,749],"news",{"featured":13,"template":14,"slug":751},"gitlab-18-11-budget-guardrails-for-gitlab-credits",{"promotions":753},[754,768,779,790],{"id":755,"categories":756,"header":758,"text":759,"button":760,"image":765},"ai-modernization",[757],"ai-ml","Is AI achieving its promise at scale?","Quiz will take 5 minutes or less",{"text":761,"config":762},"Get your AI maturity score",{"href":763,"dataGaName":764,"dataGaLocation":243},"/assessments/ai-modernization-assessment/","modernization assessment",{"config":766},{"src":767},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/qix0m7kwnd8x2fh1zq49.png",{"id":769,"categories":770,"header":771,"text":759,"button":772,"image":776},"devops-modernization",[11,570],"Are you just managing tools or shipping innovation?",{"text":773,"config":774},"Get your DevOps maturity score",{"href":775,"dataGaName":764,"dataGaLocation":243},"/assessments/devops-modernization-assessment/",{"config":777},{"src":778},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138785/eg818fmakweyuznttgid.png",{"id":780,"categories":781,"header":782,"text":759,"button":783,"image":787},"security-modernization",[22],"Are you trading speed for security?",{"text":784,"config":785},"Get your security maturity score",{"href":786,"dataGaName":764,"dataGaLocation":243},"/assessments/security-modernization-assessment/",{"config":788},{"src":789},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/p4pbqd9nnjejg5ds6mdk.png",{"id":791,"paths":792,"header":795,"text":796,"button":797,"image":802},"github-azure-migration",[793,794],"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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