- GitLab launches GitLab 18 with integrated AI features to enhance developer productivity.
- New capabilities include AI-powered Code Suggestions and Chat for Premium and Ultimate customers.
- Enhanced security and compliance features aim to streamline and secure DevSecOps workflows.
GitLab Inc. (GTLB, Financial) has announced the release of GitLab 18, introducing AI-native capabilities aimed at significantly boosting developer productivity. This major update integrates AI features throughout the platform, offering new innovations across core DevOps, and security and compliance workflows.
GitLab 18 introduces GitLab Duo, a suite of AI-powered tools that provides real-time code completion, code generation, and instant access to code explanations, refactoring, test generation, and fixes. These features are now available at no additional cost to GitLab Premium and Ultimate customers. Duo's advanced AI capabilities are designed to help organizations deliver secure source code management and improved developer experiences without the need for extra tools or licenses.
To address customer demand, GitLab Premium customers can now purchase Duo Enterprise, which includes enhanced team collaboration and context-aware AI capabilities, without the necessity to upgrade to GitLab Ultimate. This move aims to provide greater efficiency and cost savings as teams can focus more on complex challenges instead of routine coding tasks.
In addition to AI features, GitLab 18 offers significant enhancements in its core platform capabilities. These include optimized CI/CD pipelines for faster and more secure software delivery, centralized artifact management, and improved cross-functional team collaboration through a unified platform. The built-in security and compliance capabilities provide organizations with comprehensive visibility and control over security risks, featuring custom compliance frameworks and enhanced Static Application Security Testing (SAST) capabilities.
GitLab's new enhancements are expected to streamline workflows, improve productivity, and maintain high security and compliance standards, thereby catering to the needs of modern development teams seeking to overcome the complexities of fragmented toolchains in AI and DevSecOps environments.
