Executive Summary
AI is transforming the software development world faster than any previous technological advancement. What started as limited experiments with tasks like generating code snippets and writing test scripts is now expanding into agentic engineering across the entire development lifecycle. Across enterprises, AI is changing how teams write code — boosting productivity, lowering barriers to entry, and redefining roles within development teams. While agentic coding accelerates individual developer productivity, corresponding agentic infrastructure is required to ensure that speed is delivered within the enterprise context, with custom workflows, and within organizational guardrails. As adoption scales, technology leaders need to ensure that teams continue to vet agentic coding use and provide robust governance from code to production. This shift is strategic versus technical: Organizations must prepare for this transition now to avoid shipping unreliable software, speed up their software delivery, and unlock AI’s full engineering potential.1
GitLab Duo Agent Platform embeds intelligent agents throughout the software development lifecycle (SDLC) for tasks such as planning, coding, security analysis, and analytics. Organizations can invoke foundational agents or develop custom agents specific to their AI governance standards and workflows, automating complex tasks across the software lifecycle with agentic workflows to accelerate delivery and maintain quality.
GitLab commissioned Forrester Consulting to conduct a Total Economic Impact™ (TEI) study and examine the potential return on investment (ROI) enterprises may realize by deploying GitLab Duo Agent Platform.2 The purpose of this study is to provide readers with a framework to evaluate the potential financial impact of GitLab Duo Agent Platform on their organizations.
To better understand the benefits, costs, and risks associated with this investment, Forrester interviewed four decision-makers with experience using GitLab Duo Agent Platform. For the purposes of this study, Forrester aggregated the experiences of the interviewees and combined the results into a single composite organization, which is a globally operating company with $3 billion in annual revenue.
Interviewees said that prior to using GitLab Duo Agent Platform, their organizations relied heavily on manual processes, individual expertise, and ad hoc knowledge sharing to build, review, and maintain software. As a result, teams struggled with time‑consuming development, troubleshooting, and security remediation activities, and they often depended on senior engineers to provide critical context. These constraints limited the amount of time their teams could spend on higher‑value work and ultimately reduced overall productivity and speed of feature delivery.
After investing in GitLab Duo Agent Platform, interviewees reported that their organizations embedded agentic AI directly into existing software development and DevSecOps workflows. By providing agents for activities such as coding, code review, fixing failed pipelines, task delegation and analysis, software development implementation and tests, security remediation, and knowledge discovery, GitLab Duo Agent Platform reduced manual effort across the software lifecycle and enabled teams to work more efficiently. Key outcomes included higher teamwide productivity, faster security issue remediation, and shorter onboarding timelines for new team members. Collectively, these improvements helped the interviewees’ organizations increase output and improve quality.
Key Findings
Quantified benefits. Three-year, risk-adjusted present value (PV) quantified benefits for the composite organization include:
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An 80% acceleration in onboarding time for new developers. With GitLab Duo Agent Platform, new team members can independently explore and familiarize themselves with the composite organization’s development environment and gain context about projects and development standards, improving time to productivity. The shortened ramp up time yields an estimated three-year savings of $582,000 for the composite organization.
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A 75% acceleration in code migration. The composite organization undertakes a one‑time code migration initiative and uses GitLab Duo Agent Platform to investigate legacy code, diagnose pipeline failures, and remediate issues. As a result, it compresses an eight-month migration timeline to two months. From an operational perspective, that’s a single quarter of planning and execution rather than having to manage the same project across three quarters — bringing the potential delays and distractions to a bare minimum. The resulting savings are worth $157,000 to the composite organization.
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A 40% time savings for quality assurance and security remediation engineers. GitLab Duo Agent Platform shortens the time required to analyze, explain, and remediate security vulnerabilities for the composite. It tackles quality issues by providing contextual summarizations and recommended fixes. This reduces reliance on senior engineers to share context and helps security and quality assurance engineers identify and resolve issues faster. Over three years, the labor savings are worth $1.3 million to the composite organization.
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A 20% gain in individual developer productivity. GitLab Duo Agent Platform materially improves developer productivity at the composite by reducing time spent on project planning, context discovery, code review, test generation, and ongoing troubleshooting. Developers leverage agentic chat and AI agents to automate activities, including code reviews and security tasks, enabling greater delivery capacity for feature development. Over three years, this effort is worth $7.4 million to the composite organization.
Unquantified benefits. Benefits that provide value for the composite organization but are not quantified for this study include:
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Savings from tool consolidation. By standardizing on GitLab Duo Agent Platform, the composite organization was able to reduce spending on overlapping AI assisted development tools.
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Improved developer experience and satisfaction. Software and platform teams at the composite organization spend less time on repetitive, manual tasks, enabling them to focus on more engaging and higher‑value problem-solving and design work.
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Improved cross‑team collaboration and knowledge sharing. GitLab Duo Agent Platform helps team members more quickly understand unfamiliar systems and repositories, reducing friction when the composite’s engineers collaborate across teams and projects.
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Higher-quality code and output. GitLab Duo Agent Platform contributes to higher-quality code and more stable outputs by giving developers and their agents more context to understand code changes, generate tests, and catch issues earlier during the development process.
Costs. Three-year, risk-adjusted PV costs for the composite organization include:
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Consumption credit costs totaling $1.3 million. The composite organization incurs consumption costs associated with its use of GitLab Duo Agent Platform.
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Implementation and ongoing management costs totaling $589,000. These costs include internal labor for an initial pilot program and implementation, training, and ongoing support.
The financial analysis that is based on the interviews found that a composite organization experiences benefits of $9.4 million over three years versus costs of $1.9 million, adding up to a net present value (NPV) of $7.5 million and an ROI of 400%.
Key Statistics
400%
Return on investment (ROI)
$9.4M
Benefits PV
$7.5M
Net present value (NPV)
<6 months
Payback
Benefits (Three-Year)
The GitLab Duo Agent Platform Customer Journey
Drivers leading to the GitLab Duo Agent Platform investment
Interviews
| Role | Industry | Region | Revenue |
|---|---|---|---|
| Head of automation | Financial services | Global (headquarters: US) | $20 billion to $25 billion |
| Operations manager | Software development | Global (headquarters: US) | n/a |
| Senior engineering manager | Entertainment | Global (headquarters: APAC) | $3 billion |
| Senior systems engineer | Insurance and financial services | Global (headquarters: EMEA) | $3 billion to $5 billion |
Key Challenges
Prior to adopting GitLab Duo Agent Platform, interviewees described development environments where teams relied heavily on manual processes and individual expertise to build, review, and maintain software. While organizations had established development and CI/CD practices, software teams often lacked efficient ways to understand complex development environments across many projects within their organizations or remediate errors and security issues quickly.
Interviewees noted how their organizations struggled with common challenges, including:
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Time‑consuming manual workflows and troubleshooting. Interviewees said that developers spent significant time validating/shipping code, diagnosing errors, and reviewing changes using manual investigation and trial‑and‑error approaches. For example, the senior systems engineer at an insurance and financial services company noted that code reviews often took several hours to complete because developers manually inspected changes and traced issues across files. In several organizations, resolving common issues required developers to manually piece together context across repositories or consult peers.
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Inefficient security remediation processes. Identifying and remediating security vulnerabilities and errors required manual analysis to understand root causes and determine appropriate fixes. This work could often be carried out only by senior engineers with the required context, which extended remediation timelines. Interviewees noted that even straightforward vulnerabilities could take days, or longer, to remediate when teams needed to manually interpret findings and decipher remediation paths.
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Slow transfer of context and institutional knowledge. Understanding existing repositories, architectures, and development standards depended heavily on self-directed context gathering and ad hoc support from experienced team members. This slowed onboarding of new team members and made it difficult for engineers to move between projects or contribute quickly in unfamiliar areas. Interviewees described situations where developers required extended explanation and guidance to understand how systems interacted before they could begin productive work.
Why GitLab Duo Agent Platform?
The interviewees searched for a solution that could:
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Embed AI into their existing DevSecOps workflows.
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Deliver measurable productivity gains for software and platform teams.
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Enable faster development and DevSecOps efficiency.
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Provide a foundation for agent-driven automation across the software development lifecycle.
Composite Organization
Based on the interviews, Forrester constructed a TEI framework, a composite company, and an ROI analysis that illustrates the areas financially affected. The composite organization is representative of the interviewees’ organizations, and it is used to present the aggregate financial analysis in the next section. The composite organization has the following characteristics:
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Description of composite. The composite organization is a globally operating company that generates $3 billion in annual revenue and has 3,000 employees. It is an existing GitLab customer and leverages traditional development workflows (e.g., manual development, review, and error remediation processes) before adopting Duo Agent Platform.
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Deployment characteristics. The composite deploys GitLab Duo Agent Platform to 150 users in Year 1, including 100 software developers, 30 platform/DevSecOps engineers, 15 security and QA engineers, and five data science engineers. By Year 3, the user population reaches 250.
KEY ASSUMPTIONS
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$3 billion annual revenue
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3,000 employees
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250 software team members with access to GitLab Duo Agent Platform
Analysis Of Benefits
Quantified benefit data as applied to the composite
Total Benefits
| Ref. | Benefit | Year 1 | Year 2 | Year 3 | Total | Present Value |
|---|---|---|---|---|---|---|
| Atr | Team member productivity gains | $2,091,000 | $3,485,000 | $3,485,000 | $9,061,000 | $7,399,406 |
| Btr | Code migration acceleration | $172,656 | $0 | $0 | $172,656 | $156,960 |
| Ctr | Security remediation cost savings | $364,650 | $607,750 | $607,750 | $1,580,150 | $1,290,384 |
| Dtr | New developer onboarding acceleration | $64,338 | $536,154 | $107,231 | $707,723 | $582,156 |
| Total benefits (risk-adjusted) | $2,692,644 | $4,628,904 | $4,199,981 | $11,521,529 | $9,428,906 |
Team Member Productivity Gains
Evidence and data. Interviewees reported that GitLab Duo Agent Platform materially improved developer productivity by reducing time spent on manual coding, troubleshooting, and code reviews. With GitLab Duo Agent Platform, team members at the interviewees’ organizations leveraged agentic chat and task‑specific agents to answer questions, develop code, create tests, assist with code reviews, and troubleshoot errors. Using chat and agents reduced manual software delivery, review, and iteration efforts, freeing them to focus on higher‑value design and problem‑solving work. These improvements allowed teams to deliver more output with existing headcount, with interviewees citing time savings across different stages of the software lifecycle.
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The operations manager at a software development company reported that software teams used agentic chat, code review assistance, and security analyst agents throughout the SDLC to reduce manual effort. As a result, developers at this interviewee’s organization experienced an estimated 20% to 40% in development time savings.
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The senior systems engineer at an insurance and financial services organization stated that teams leveraged custom agents and agentic chat for code generation, test creation, query optimization, and rapid code reviews for data and analytics workloads. They estimated reducing the time to complete code reviews from hours to seconds, with 80% to 90% of code generation now handled by GitLab Duo Agent Platform. These improvements allowed engineers to reallocate time toward analysis and validation.
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The senior engineering manager at an entertainment company reported that developers used agentic chat within GitLab to understand unfamiliar repositories, analyze the impact of code changes across microservices, and troubleshoot pipeline issues. The interviewee observed a 60% increase in merge requests after adoption and estimated that each developer saved approximately 10 hours per week, improving delivery velocity.
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The head of automation at a financial services organization described widespread use of agentic chat and agents to accelerate feature development, generate tests, and assist with code reviews. They shared: “I’ve heard examples of feature enhancements that used to take 16 hours, and with GitLab Duo Agent Platform, it takes only 2 hours. That’s a pretty impressive decrease.” Based on early successes, the organization expects GitLab Duo Agent Platform to support a 20% to 30% improvement goal in targeted developer productivity as usage continues to scale.
Modeling and assumptions. Based on the interviews, Forrester assumes the following about the composite organization:
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The composite organization has 150 team members who actively use GitLab Duo Agent Platform in Year 1, which increases to 250 team members in Year 2 and Year 3 as adoption expands across development teams.
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The average fully burdened annual salary per team member is $164,000.
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With GitLab Duo Agent Platform, team members experience an overall conservative productivity improvement of 20%.
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Forrester applies a 50% productivity recapture rate to account for the portion of time savings that can be converted into incremental productive output.
Risks. Forrester recognizes that these results may not be representative of all experiences and the value of the benefit will vary depending on:
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The baseline maturity of development practices before adopting GitLab Duo Agent Platform.
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The degree of adoption and proficiency with GitLab Duo Agent Platform.
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Variation in developer roles and time spent coding.
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The degree to which time savings are recaptured productively.
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Differences in compensation levels, which can vary based on industry, geography, experience, and skill sets.
Results. To account for these risks, Forrester adjusted this benefit downward by 15%, yielding a three-year, risk-adjusted total PV (discounted at 10%) of $7.4 million.
$2 million
Year 1 productivity savings
Team Member Productivity Gains
| Ref. | Metric | Source | Year 1 | Year 2 | Year 3 | |
|---|---|---|---|---|---|---|
| A1 | Total Duo Agent Platform users | Composite | 150 | 250 | 250 | |
| A2 | Average fully burdened annual salary for a Duo Agent Platform user | Composite | $164,000 | $164,000 | $164,000 | |
| A3 | Overall productivity improvement with Duo Agent Platform | Interviews | 20% | 20% | 20% | |
| A4 | Productivity recapture rate | TEI methodology | 50% | 50% | 50% | |
| At | Team member productivity gains | A1*A2*A3*A4 | $2,460,000 | $4,100,000 | $4,100,000 | |
| Risk adjustment | ↓15% | |||||
| Atr | Team member productivity gains (risk-adjusted) | $2,091,000 | $3,485,000 | $3,485,000 | ||
| Three-year total: $9,061,000 | Three-year present value: $7,399,406 | |||||
Code Migration Acceleration
Evidence and data. Two interviewees described how their organizations used GitLab Duo Agent Platform to accelerate complex code modernization and migration initiatives.
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The head of automation at a financial services company described how GitLab Duo Agent Platform was critical to accelerating a large‑scale migration from an on-prem GitLab environment to GitLab SaaS. The organization undertook a complex migration involving a large volume of repositories and new CI/CD standards, which they estimated would take eight months to complete. The interviewee shared that GitLab Duo Agent Platform enabled developers to quickly understand legacy code, diagnose pipeline failures, and remediate issues as they mirrored and rebuilt repositories in the new environment. Developers used GitLab Duo Agent Platform during the migration process to evaluate failing builds and receive guidance on required changes, reducing manual investigation and trial and error. As a result, the organization completed the core migration effort in approximately two months, compressing the overall timeline by 75%.
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The senior engineering manager at an entertainment company described how GitLab Duo Agent Platform helped accelerate modernizing a complex legacy system that had been undergoing refactoring for an extended period of time. Before using GitLab Duo Agent Platform, the team struggled to make progress due to interdependencies across multiple services and new bugs introduced during refactoring efforts. By using agentic chat to analyze existing code, understand the impact of changes, and troubleshoot issues across repositories, developers moved through refactoring work more efficiently and delivered more stable releases.
Modeling and assumptions. Based on the interviews, Forrester assumes the following about the composite organization:
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The composite organization undertakes one major legacy code migration initiative during Year 1 of the three‑year analysis period. It dedicates five software developers to the migration effort.
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Without GitLab Duo Agent Platform, the migration would require eight months to complete.
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With GitLab Duo Agent Platform, it completes the same migration in two months, representing a 75% decrease in migration duration.
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The average fully burdened annual salary for a team member involved in the migration is $162,500.
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Forrester applies a 50% productivity recapture rate to account for the portion of time savings that can be converted into incremental productive output.
Risks. Forrester recognizes that these results may not be representative of all experiences, and the value of migration acceleration benefits will vary depending on:
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The scope, complexity, and condition of legacy code bases.
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The scale and requirements of the migration initiative.
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The extent to which an organization adopts and actively uses GitLab Duo Agent Platform used during migration activities.
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The actual time needed for a migration if an organization does not use GitLab Duo Agent Platform.
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Variation in developer compensation.
Results. To account for these risks, Forrester adjusted this benefit downward by 15%, yielding a three-year, risk-adjusted total PV (discounted at 10%) of $157,000.
75%
Acceleration in code migration
Code Migration Acceleration
| Ref. | Metric | Source | Year 1 | Year 2 | Year 3 | |
|---|---|---|---|---|---|---|
| B1 | Team members | Interviews | 5 | |||
| B2 | Migration time before Duo Agent Platform (months) | Interviews | 8 | |||
| B3 | Migration time after Duo Agent Platform (months) | Interviews | 2 | |||
| B4 | Time savings with Duo Agent Platform | Interviews | 75% | |||
| B5 | Fully burdened annual salary for a team member | Composite | $162,500 | |||
| B6 | Productivity recapture rate | TEI methodology | 50% | |||
| Bt | Code migration acceleration | B1*(B2-B3)/12*B5*B6 | $203,125 | $0 | $0 | |
| Risk adjustment | ↓15% | |||||
| Btr | Code migration acceleration (risk-adjusted) | $172,656 | $0 | $0 | ||
| Three-year total: $172,656 | Three-year present value: $156,960 | |||||
Security Remediation Cost Savings
Evidence and data. Interviewees reported that GitLab Duo Agent Platform reduced the time required to analyze, explain, and remediate security vulnerabilities and other issues. By providing contextual explanations of findings and suggested remediation steps, GitLab Duo Agent Platform reduced manual investigation, helped teams prioritize vulnerabilities that posed the most risk, accelerated issue resolution, and increased productivity across security and other engineering teams involved.
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The operations manager at a software development company reported that GitLab Duo Agent Platform materially reduced the effort required to analyze and remediate security issues across development teams. Before using the platform, security remediation required manual investigation to determine root causes and resolution paths, which was work only senior engineers with the required context could perform. With GitLab Duo Agent Platform, teams used agentic assistance and agents to explain vulnerabilities, prioritize, and guide remediation, reducing the time spent on these activities by an estimated 40%. The interviewee noted that this automation allowed less experienced engineers to remediate issues that previously required senior‑level intervention.
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The head of automation at a financial services organization described how GitLab Duo Agent Platform helped accelerate security vulnerability remediation. As an example, the interviewee cited a large vulnerability remediation effort in which hundreds of required code changes were proposed through merge requests using agentic chat. Work they thought would take weeks of manual effort took less than an hour, significantly compressing remediation timelines.
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The senior engineering manager at an entertainment company noted that GitLab Duo Agent Platform improved the team’s ability to identify and remediate security issues during development. They shared: “I’ve had good success going into a repository and saying, ‘Hey, I need to fix any open vulnerabilities.’ Duo takes that request and applies security guidance broadly across the codebase.”
Modeling and assumptions. Based on the interviews, Forrester assumes the following about the composite organization:
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The composite organization has 15 QA and security remediation engineers in Year 1, which increases to 25 engineers in Year 2 and Year 3.
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The average fully burdened annual salary for a QA and security remediation engineer is $143,000.
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Reducing the time required to remediate security vulnerabilities with GitLab Duo Agent Platform results in an estimated 40% overall time savings.
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Forrester applies a 50% productivity recapture rate to account for the portion of time savings that can be converted into incremental productive output.
Risks. Forrester recognizes that these results may not be representative of all experiences, and the value of this benefit will vary depending on:
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The volume and complexity of security vulnerabilities and other QA issues found.
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Adoption of GitLab Duo Agent Platform and the extent to which an organization uses it during QA and security remediation activities.
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The maturity of QA and security remediation processes before using GitLab Duo Agent Platform.
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Variation in salaries for QA and security remediation roles.
Results. To account for these risks, Forrester adjusted this benefit downward by 15%, yielding a three-year, risk-adjusted total PV (discounted at 10%) of $1.3 million.
40%
Time savings for QA and security remediation engineers
Security Remediation Cost Savings
| Ref. | Metric | Source | Year 1 | Year 2 | Year 3 | |
|---|---|---|---|---|---|---|
| C1 | QA/security remediation engineers | Composite | 15 | 25 | 25 | |
| C2 | Fully burdened annual salary for a QA/security remediation engineer | Composite | $143,000 | $143,000 | $143,000 | |
| C3 | Percentage time savings with Duo Agent Platform | Interviews | 40% | 40% | 40% | |
| C4 | Productivity recapture rate | TEI methodology | 50% | 50% | 50% | |
| Ct | Security remediation cost savings | C1*C2*C3*C4 | $429,000 | $715,000 | $715,000 | |
| Risk adjustment | ↓15% | |||||
| Ctr | Security remediation cost savings (risk-adjusted) | $364,650 | $607,750 | $607,750 | ||
| Three-year total: $1,580,150 | Three-year present value: $1,290,384 | |||||
New Developer Onboarding Acceleration
Evidence and data. Several interviewees reported that GitLab Duo Agent Platform accelerated new developer onboarding by reducing the time required to understand codebases, architectures, and development workflows. By embedding agentic chat directly into integrated development environments (IDEs) and repositories, GitLab Duo Agent Platform enabled new hires to get up to speed on unfamiliar environments, ask contextual questions, and resolve blockers. These capabilities shortened time to productivity and reduced the overall effort required to onboard new developers.
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The operations manager at a software development company reported that GitLab Duo Agent Platform materially reduced onboarding time by allowing new developers to quickly gain context about projects and understand development standards through agentic chat. Rather than asking colleagues for context and explanations, new hires could ask questions directly within GitLab and receive immediate guidance. The interviewee noted that developers joining new projects became productive within days instead of weeks and reduced their reliance on existing team members for information.
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The head of automation at a financial services organization also said that using GitLab Duo Agent Platform shortened time to productivity. They noted that new engineers could use agentic chat to understand repositories, pipelines, and development practices, minimizing interruptions to their peers for guidance. As a result, newly hired developers submitted their first merge requests more quickly, supporting faster ramp‑up and earlier productivity.
Modeling and assumptions. Based on the interviews, Forrester assumes the following about the composite organization:
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The composite organization has 150 team members who use GitLab Duo Agent Platform in Year 1, which increases to 250 team members in Year 2 and Year 3 as adoption expands.
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The composite organization also onboards 15 new developers in Year 1, 25 new developers in Year 2, and 25 new developers in Year 3.
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The average fully burdened annual salary for a developer is $164,000.
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Before GitLab Duo Agent Platform, new developers required approximately 80 hours of onboarding time to become meaningfully productive. GitLab Duo Agent Platform reduces onboarding time by 80%.
Risks. Forrester recognizes that these results may not be representative of all experiences, and the value of onboarding acceleration will vary depending on:
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The complexity of existing codebases and environments.
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The extent to which new team members actively use GitLab Duo Agent Platform during onboarding.
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Variation in developer experience levels.
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The quality of existing documentation and onboarding efforts.
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Variation in developer salaries.
Results. To account for these risks, Forrester adjusted this benefit downward by 15%, yielding a three-year, risk-adjusted total PV (discounted at 10%) of $582,000.
80%
Reduction in onboarding time
New Developer Onboarding Acceleration
| Ref. | Metric | Source | Year 1 | Year 2 | Year 3 | |
|---|---|---|---|---|---|---|
| D1 | Total Duo Agent Platform users | Composite | 150 | 250 | 250 | |
| D2 | New developers onboarded | Composite | 15 | 125 | 25 | |
| D3 | Average fully burdened annual salary for a Duo Agent Platform user | Composite | $164,000 | $164,000 | $164,000 | |
| D4 | Onboarding time before Duo Agent Platform (hours) | Interviews | 80 | 80 | 80 | |
| D5 | Time savings with Duo Agent Platform | Interviews | 80% | 80% | 80% | |
| Dt | New developer onboarding acceleration | D2*D3/2,080*D4*D5 | $75,692 | $630,769 | $126,154 | |
| Risk adjustment | ↓15% | |||||
| Dtr | New developer onboarding acceleration (risk-adjusted) | $64,338 | $536,154 | $107,231 | ||
| Three-year total: $707,723 | Three-year present value: $582,156 | |||||
Unquantified Benefits
Interviewees mentioned the following additional benefits that their organizations experienced but were not able to quantify:
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Savings from tool consolidation. Interviewees reported that their organizations were able to consolidate multiple AI developer tools after standardizing on GitLab Duo Agent Platform. Additionally, the senior systems engineer in insurance and financial services noted that their organization reduced its informal use of ChatGPT following the adoption of GitLab Duo Agent Platform.
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Improved developer experience and satisfaction. Interviewees reported that GitLab Duo Agent Platform improved the day‑to‑day developer experience by reducing routine, repetitive, and cognitively taxing tasks. Developers at other interviewees’ organizations were able to spend less time troubleshooting syntax issues, searching for context, or switching between tools and more time focusing on higher‑value problem-solving and design work. As a result, interviewees said development work was more engaging and workflows were more continuous with fewer switches between context. The head of automation at a financial services organization said: “Rather than hacking through syntax issues, they can focus on the bigger picture. It’s more fun.”
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Improved cross‑team collaboration. Multiple interviewees highlighted improvements in cross‑team mobility and coordination. The head of automation at a financial services organization described situations where developers shifted between teams and projects, noting that it previously took one to two weeks to understand what was going on before they could contribute effectively. With GitLab Duo Agent Platform, developers were able to get up to speed faster, easing collaboration when capacity shifted across teams. Interviewees also noted that GitLab Duo Agent Platform helped teams align more easily when working across multiple repositories and services. A senior engineering manager at an entertainment company explained that understanding how repositories interacted had previously required manual investigation and discussion across teams, while GitLab Duo Agent Platform made it easier to connect context across systems.
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Higher-quality code and output. Interviewees reported that GitLab Duo Agent Platform contributed to higher‑quality code and more stable outputs by helping developers better understand changes, generate tests, and catch issues earlier in the development process. The senior engineering manager at an entertainment company noted that newer releases became “a lot more stable” as teams used GitLab Duo Agent Platform to understand the impact of changes across repositories and services.
Flexibility
The value of flexibility is unique to each customer. There are multiple scenarios in which a customer might implement Duo Agent Platform and later realize additional uses and business opportunities, including:
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Expansion of custom AI agent use. Beyond leveraging prebuilt AI agents offered by GitLab Duo Agent Platform, the operations manager at a software development company described how their organization was using custom agents: “We are also using custom agents within our projects. For example, we built a refinement agent that runs when a new issue is raised in a repository. When triggered, the agent scans the full repository, including the code and project documentation, and applies project‑ and client‑specific context. It understands how it should act within that project and for that client. We share memory about how to work with the client the agent is supporting, and it then posts an internal comment on the issue with additional context about what needs to be changed, what is in scope, and follow‑up questions that the project manager can ask the client who raised the issue.”
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Extension of AI capabilities to nontechnical roles. The head of automation at a financial services organization described a longer‑term vision for extending GitLab Duo Agent Platform beyond traditional developer use cases to support nontechnical roles. The interviewee emphasized encouraging nontechnical teams to store their work artifacts, such as documentation and other materials, in GitLab, where they could be version‑controlled and queried using AI. The interviewee also shared examples of using GitLab Duo Agent Platform to generate diagrams for nontechnical roles to consume.
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Expansion of security automation. The head of automation at a financial services organization shared an additional longer‑term goal of shifting security further left in the development process by automating more aspects of vulnerability analysis and remediation within software delivery workflows.
Flexibility would also be quantified when evaluated as part of a specific project (described in more detail in Total Economic Impact Approach).
Analysis Of Costs
Quantified cost data as applied to the composite
Total Costs
| Ref. | Cost | Initial | Year 1 | Year 2 | Year 3 | Total | Present Value |
|---|---|---|---|---|---|---|---|
| Etr | Consumption credits spend | $24,000 | $360,000 | $600,000 | $600,000 | $1,584,000 | $1,297,929 |
| Ftr | Implementation and ongoing management | $105,181 | $246,631 | $213,989 | $110,621 | $676,422 | $589,353 |
| Total costs (risk-adjusted) | $129,181 | $606,631 | $813,989 | $710,621 | $2,260,422 | $1,887,282 |
Consumption Credits Spend
Evidence and data. Consumption costs for the interviewees’ organizations varied based on factors such as adoption, usage patterns, and GitLab licensing editions. GitLab Credits are the standardized consumption currency for usage-based billing, where each usage action consumes a number of credits. Credits are calculated based on the features and models. Pricing may vary. Contact GitLab for additional details.
Modeling and assumptions. Based on the interviews, Forrester assumes the following about the composite organization:
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The composite organization spends $20,000 on consumption credits during the initial pilot period.
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As adoption and consumption grow, the organization spends $300,000 in Year 1, $500,000 in Year 2, and $500,000 in Year 3.
Risks. Forrester recognizes that these results may not be representative of all experiences, and the value of consumption credit costs will vary depending on adoption rates and usage patterns.
Results. To account for these risks, Forrester adjusted this cost upward by 20%, yielding a three-year, risk-adjusted total PV (discounted at 10%) of $1.3 million.
Consumption Credits Spend
| Ref. | Metric | Source | Initial | Year 1 | Year 2 | Year 3 |
|---|---|---|---|---|---|---|
| E1 | Consumption credits spend | Composite | $20,000 | $300,000 | $500,000 | $500,000 |
| Et | Consumption credits spend | E1 | $20,000 | $300,000 | $500,000 | $500,000 |
| Risk adjustment | ↑20% | |||||
| Etr | Consumption credits spend (risk-adjusted) | $24,000 | $360,000 | $600,000 | $600,000 | |
| Three-year total: $1,584,000 | Three-year present value: $1,297,929 | |||||
Implementation And Ongoing Management
Evidence and data. The interviewees’ organizations incurred costs for implementation, training, and ongoing management.
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Interviewees described the implementation process for GitLab Duo Agent Platform as low friction since their organizations already had GitLab embedded in development workflows. Implementation primarily required enabling the service within the existing GitLab environment and installing the supported IDE extensions, with no additional infrastructure or complex integrations required. Most interviewees’ organizations started using GitLab Duo Agent Platform through a pilot before expanding adoption. The head of automation at a financial services organization described a structured pilot involving approximately eight to 10 internal staff members, who each dedicated roughly 10% to 15% of their time over several months to evaluate, enable, and provide feedback on GitLab Duo Agent Platform before general availability.
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Interviewees shared that hands‑on use drove training and adoption rather than formal instruction, and most users learned how to use the platform through experimentation in their day‑to‑day work. Training and ramp time varied based on developer backgrounds, with some users becoming productive quickly due to prior experience with AI tools. The head of automation at a financial services organization described supplementing informal learning with targeted, instructor‑led virtual training for select teams to accelerate adoption.
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Interviewees reported that ongoing management of GitLab Duo Agent Platform focused on enablement and governance. Once they enabled the platform within existing GitLab environments, day‑to‑day management effort was limited to user access provisioning, usage guidance, and periodic coordination with GitLab. The head of automation at a financial services organization described ongoing involvement from a small internal team to support governance, track adoption, and engage with GitLab about updates, though this effort decreased over time as usage stabilized.
Modeling and assumptions. Based on the interviews, Forrester assumes the following about the composite organization:
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The composite organization has 10 internal staff members involved in the initial GitLab Duo Agent Platform pilot and implementation effort. The blended fully burdened annual salary for a staff member is $164,000.
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During the initial period, each staff resource dedicates approximately 20% of their time to the pilot.
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New GitLab Duo Agent Platform users participate in 12 hours of training. During the initial period, 10 staff members receive training. In Year 1, Year 2, and Year 3 respectively, 140, 110, and 15 new users receive training.
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Ongoing management and support activities require the equivalent of $82,000 per year in internal labor.
Risks. Forrester recognizes that these results may not be representative of all experiences, and the value of the costs will vary depending on:
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The scope and pace of initial adoption.
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The number of users requiring training.
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Differences in internal labor costs.
Results. To account for these risks, Forrester adjusted this cost upward by 15%, yielding a three-year, risk-adjusted total PV (discounted at 10%) of $589,000.
Implementation And Ongoing Management
| Ref. | Metric | Source | Initial | Year 1 | Year 2 | Year 3 |
|---|---|---|---|---|---|---|
| F1 | Staff members involved in the pilot | Interviews | 10 | |||
| F2 | Fully burdened annual salary for a staff member | Composite | $164,000 | |||
| F3 | Percentage of time involved in the pilot | Interviews | 20% | |||
| F4 | Time to implement (months) | Interviews | 3 | |||
| F5 | Implementation cost | F1*F2*F3*F4/12 | $82,000 | |||
| F6 | Training for Duo Agent Platform users (hours) | Interviews | 12 | |||
| F7 | Duo Agent Platform users requiring training | Composite | 10 | 140 | 110 | 15 |
| F8 | Training cost | F2*F6*F7/2,080 | $9,462 | $132,462 | $104,077 | $14,192 |
| F9 | Ongoing management effort | Interviews | $82,000 | $82,000 | $82,000 | |
| Ft | Implementation and ongoing management | F5+F8+F9 | $91,462 | $214,462 | $186,077 | $96,192 |
| Risk adjustment | ↑15% | |||||
| Ftr | Implementation and ongoing management (risk-adjusted) | $105,181 | $246,631 | $213,989 | $110,621 | |
| Three-year total: $676,422 | Three-year present value: $589,353 | |||||
Financial Summary
Consolidated Three-Year, Risk-Adjusted Metrics
Cash Flow Chart (Risk-Adjusted)
Cash Flow Analysis (Risk-Adjusted)
| Initial | Year 1 | Year 2 | Year 3 | Total | Present Value | |
|---|---|---|---|---|---|---|
| Total costs | ($129,181) | ($606,631) | ($813,989) | ($710,621) | ($2,260,422) | ($1,887,282) |
| Total benefits | $0 | $2,692,644 | $4,628,904 | $4,199,981 | $11,521,529 | $9,428,906 |
| Net benefits | ($129,181) | $2,086,013 | $3,814,915 | $3,489,360 | $9,261,107 | $7,541,624 |
| ROI | 400% | |||||
| Payback | <6 months |
Please Note
The financial results calculated in the Benefits and Costs sections can be used to determine the ROI, NPV, and payback period for the composite organization’s investment. Forrester assumes a yearly discount rate of 10% for this analysis.
These risk-adjusted ROI, NPV, and payback period values are determined by applying risk-adjustment factors to the unadjusted results in each Benefit and Cost section.
The initial investment column contains costs incurred at “time 0” or at the beginning of Year 1 that are not discounted. All other cash flows are discounted using the discount rate at the end of the year. PV calculations are calculated for each total cost and benefit estimate. NPV calculations in the summary tables are the sum of the initial investment and the discounted cash flows in each year. Sums and present value calculations of the Total Benefits, Total Costs, and Cash Flow tables may not exactly add up, as some rounding may occur.
From the information provided in the interviews, Forrester constructed a Total Economic Impact™ framework for those organizations considering an investment in Duo Agent Platform.
The objective of the framework is to identify the cost, benefit, flexibility, and risk factors that affect the investment decision. Forrester took a multistep approach to evaluate the impact that Duo Agent Platform can have on an organization.
Due Diligence
Interviewed GitLab stakeholders and Forrester analysts to gather data relative to Duo Agent Platform.
Interviews
Interviewed four decision-makers at organizations using Duo Agent Platform to obtain data about costs, benefits, and risks.
Composite Organization
Designed a composite organization based on characteristics of the interviewees’ organizations.
Financial Model Framework
Constructed a financial model representative of the interviews using the TEI methodology and risk-adjusted the financial model based on issues and concerns of the interviewees.
Case Study
Employed four fundamental elements of TEI in modeling the investment impact: benefits, costs, flexibility, and risks. Given the increasing sophistication of ROI analyses related to IT investments, Forrester’s TEI methodology provides a complete picture of the total economic impact of purchase decisions. Please see Appendix A for additional information on the TEI methodology.
Total Economic Impact Approach
Benefits
Benefits represent the value the solution delivers to the business. The TEI methodology places equal weight on the measure of benefits and costs, allowing for a full examination of the solution’s effect on the entire organization.
Costs
Costs comprise all expenses necessary to deliver the proposed value, or benefits, of the solution. The methodology captures implementation and ongoing costs associated with the solution.
Flexibility
Flexibility represents the strategic value that can be obtained for some future additional investment building on top of the initial investment already made. The ability to capture that benefit has a PV that can be estimated.
Risks
Risks measure the uncertainty of benefit and cost estimates given: 1) the likelihood that estimates will meet original projections and 2) the likelihood that estimates will be tracked over time. TEI risk factors are based on “triangular distribution.”
Financial Terminology
Present value (PV)
The present or current value of (discounted) cost and benefit estimates given at an interest rate (the discount rate). The PVs of costs and benefits feed into the total NPV of cash flows.
Net present value (NPV)
The present or current value of (discounted) future net cash flows given an interest rate (the discount rate). A positive project NPV normally indicates that the investment should be made unless other projects have higher NPVs.
Return on investment (ROI)
A project’s expected return in percentage terms. ROI is calculated by dividing net benefits (benefits less costs) by costs.
Discount rate
The interest rate used in cash flow analysis to take into account the time value of money. Organizations typically use discount rates between 8% and 16%.
Payback
The breakeven point for an investment. This is the point in time at which net benefits (benefits minus costs) equal initial investment or cost.
Appendix A
Total Economic Impact
Total Economic Impact is a methodology developed by Forrester Research that enhances a company’s technology decision-making processes and assists solution providers in communicating their value proposition to clients. The TEI methodology helps companies demonstrate, justify, and realize the tangible value of business and technology initiatives to both senior management and other key stakeholders.
Appendix B
Endnotes
1 Source: Predictions 2026: Software Development, Forrester Research, Inc., October 21, 2025.
2 Total Economic Impact is a methodology developed by Forrester Research that enhances a company’s technology decision-making processes and assists solution providers in communicating their value proposition to clients. The TEI methodology helps companies demonstrate, justify, and realize the tangible value of business and technology initiatives to both senior management and other key stakeholders.
Disclosures
Readers should be aware of the following:
This study is commissioned by GitLab and delivered by Forrester Consulting. It is not meant to be used as a competitive analysis.
Forrester makes no assumptions as to the potential ROI that other organizations will receive. Forrester strongly advises that readers use their own estimates within the framework provided in the study to determine the appropriateness of an investment in Duo Agent Platform. For any interactive functionality, the intent is for the questions to solicit inputs specific to a prospect’s business. Forrester believes that this analysis is representative of what companies may achieve with Duo Agent Platform based on the inputs provided and any assumptions made. Forrester does not endorse GitLab or its offerings. Although great care has been taken to ensure the accuracy and completeness of this model, GitLab and Forrester Research are unable to accept any legal responsibility for any actions taken on the basis of the information contained herein. The interactive tool is provided ‘AS IS,’ and Forrester and GitLab make no warranties of any kind.
GitLab reviewed and provided feedback to Forrester, but Forrester maintains editorial control over the study and its findings and does not accept changes to the study that contradict Forrester’s findings or obscure the meaning of the study.
GitLab provided the customer names for the interviews but did not participate in the interviews.
Consulting Team:
Maria Kulikova
Published
June 2026
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