Gitlab Credits

Power and orchestrate AI agents with GitLab Credits—the flexible consumption currency behind the GitLab Duo Agent Platform.

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Product Description

GitLab Credits offer flexible, consumption-based access to GitLab’s agentic AI capabilities, enabling organizations to scale AI adoption at their own pace with predictable costs. Powered by the Duo Agent Platform, GitLab’s agentic AI helps software teams collaborate at AI speed while preserving code quality and enterprise-grade security.

Designed as a unified consumption currency, GitLab Credits support teams at every stage of AI maturity, providing immediate access to agentic AI across epics, projects, and release cycles. When usage exceeds monthly allocations, automated on-demand billing ensures uninterrupted access, allowing developers to continue working without disruption.

Real-time dashboards deliver full visibility into AI consumption. Teams can monitor usage by user, project, and group, enabling precise cost allocation. Automated threshold alerts support proactive budgeting, while advanced analytics provide trend analysis, forecasting, and FinOps integration.

Built on SOX-compliant infrastructure, GitLab Credits include comprehensive audit trails, automated reporting, and multi-instance aggregation across SaaS, Self-Managed, and Dedicated deployments—supporting complex enterprise environments.

By paying only for the AI work actually performed, organizations can reduce unnecessary costs while accelerating software delivery. Consumption-based pricing removes seat limitations, allowing AI capabilities to scale across the organization within defined governance and policy controls.

Granular usage controls and pre-commit credits help maintain spending discipline and prevent unexpected costs. With per-user and per-project attribution, automated alerts, and advanced analytics, teams can optimize AI investment using data-driven insights. Centralized dashboards highlight which workflows and teams deliver the most value, ensuring accurate billing, compliance, and effective AI governance at scale.

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