JetBrains Report Reveals Surprising Resurgence of Self-Hosted CI/CD and AI's Peripheral Role in Pipelines

A new report from JetBrains, drawing on data from four of its recent studies, reveals key trends shaping the CI/CD landscape in 2026. Notably, the report indicates a significant uptick in self-hosted CI/CD systems, with 72.8% of organizations running their primary CI/CD system on-premises or on self-managed cloud infrastructure. This includes a five-year high of 30.5% using on-premises installations. Concurrently, the report highlights that nearly half (48.8%) of organizations leverage AI for at least one CI/CD task. However, only a small fraction (8%) currently integrate AI-powered steps directly within their build or test processes. This data is crucial for DevOps and cloud engineers as it signals a potential re-evaluation of cloud-native CI/CD strategies. The increased preference for self-hosting suggests that organizations are prioritizing factors like control, security, and cost efficiency over the perceived simplicity of fully managed cloud offerings. For practitioners, this means a renewed focus on managing and optimizing on-premises or self-managed cloud CI/CD infrastructure, potentially requiring different skill sets and operational models. The widespread, yet peripheral, adoption of AI also indicates that while AI is seen as beneficial, its transformative impact on core CI/CD execution is yet to materialize, presenting both opportunities and challenges for future integration. This trend fits into a broader context of organizations seeking more granular control over their software supply chain and infrastructure. While the initial wave of cloud adoption pushed many towards fully managed services, the complexities of cost management, data governance, and specific security requirements are driving some back to more controlled environments. The cautious integration of AI within CI/CD aligns with the industry's measured approach to adopting new technologies in critical production workflows. AI is currently augmenting human tasks around the pipeline, such as code analysis and test generation, rather than autonomously driving core build and deployment processes. This reflects a pragmatic stance, ensuring reliability and stability before deeper AI integration. In practice, practitioners should consider re-evaluating their CI/CD hosting strategies, weighing the benefits of self-management against the convenience of managed services. For those already self-hosting, optimizing existing infrastructure for performance, scalability, and security will be paramount. Regarding AI, teams should explore how AI tools can enhance existing CI/CD processes, particularly in areas like intelligent test selection, anomaly detection in pipeline runs, and automated code review. However, a pragmatic approach is advised, focusing on augmenting human capabilities rather than fully automating critical steps with AI until the technology matures further and trust is firmly established. Organizations should also monitor the evolution of AI within CI/CD tools, as the current peripheral role is likely to evolve as AI capabilities advance and become more robust for direct pipeline integration.