GitLab Duo Agent Platform delivers 400% ROI in Forrester study
A Forrester Consulting study commissioned by GitLab Inc. found that the GitLab Duo Agent Platform offers a 400% ROI and $7.5 million NPV over three years. Key benefits include a 20% productivity boost for developers, 40% time savings for security engineers, and faster onboarding and code migration cycles.

*this image is generated using AI for illustrative purposes only.
GitLab Inc. today released the results of a Total Economic Impact (TEI) study conducted by Forrester Consulting, which found that organizations using the GitLab Duo Agent Platform can achieve a 400% return on investment (ROI) and $7.5 million in net present value (NPV) over three years. The study indicates a payback period of under six months for the composite organization analyzed. These findings demonstrate the financial efficiency and productivity gains available to enterprises integrating AI agents into their software development lifecycle.
The study examined a composite organization modeled on interviews with four GitLab customers across financial services, software development, entertainment, and insurance sectors. This organization generates $3 billion in annual revenue and employs 3,000 people. It deployed the platform to 150 users in year one, expanding to 250 users by year three. Forrester found that the platform reduced manual effort and produced measurable gains in productivity, security remediation, and onboarding speed.
Key Financial and Operational Benefits
The TEI study quantified several areas where the platform delivered value, including labor savings and project acceleration. The following table summarizes the key financial impacts identified over the three-year period.
| Benefit Category | Metric | Three-Year Value |
|---|---|---|
| Productivity Gain | 20% increase in individual developer productivity | $7.4 million |
| Security & QA Savings | 40% time savings for engineers | $1.3 million |
| Onboarding Efficiency | 80% acceleration in new developer onboarding | $582,000 |
| Code Migration | 75% acceleration in code migration | $157,000 |
Operational Efficiencies
Beyond the direct financial returns, the study highlighted specific operational improvements driven by the platform. New team members used the GitLab Duo Agent Platform to explore codebases and development standards independently, which cut onboarding time significantly. Additionally, the platform helped diagnose pipeline failures and remediate issues during a legacy code migration, compressing an eight-month project into two months.
Quality assurance and security remediation engineers realized a 40% time savings as the platform provided contextual explanations and recommended fixes for security vulnerabilities. This reduced reliance on senior engineers and contributed to the substantial labor savings noted in the report. Individual developers also saw a 20% gain in productivity due to reduced time spent on planning, context discovery, code review, and troubleshooting.
Strategic Implications
Manav Khurana, chief product and marketing officer at GitLab, emphasized the importance of combining agentic coding with governed infrastructure. "Speed of agentic coding without control can turn into an expensive liability quickly," Khurana said. "This study shows that teams who pair agentic coding with governed agentic infrastructure get results they can measure and trust at scale."
Forrester also identified unquantified benefits, such as reduced spending on overlapping AI development tools, improved developer satisfaction, stronger cross-team collaboration, and higher-quality code output. The study was commissioned by GitLab and delivered by Forrester Consulting, with GitLab providing customer names but not participating in the interviews to ensure editorial independence.
How will the rapid scalability from 150 to 250 users impact the platform's performance and governance protocols?
What risks does the reduced reliance on senior engineers pose to long-term talent development and mentorship?
How might competitors respond to these findings with their own AI-driven development tools?





























