The State of CI/CD in 2025: Key Insights from the Latest JetBrains ...

#### Migration in process Changing a CI/CD tool is not like switching an individual engineering tool. Setting up CI/CD pipelines is time-consuming, and once companies invest in the process, they won’t churn that easily. Many teams are caught mid-move, running their legacy pipelines alongside new ones. As a result, companies might still be using legacy products like Azure DevOps or Jenkins while switching to a more modern approach with GitHub Actions or GitLab CI, sometimes taking months or even years to make the switch fully. … #### Cost and performance trade-offs A few teams cited cost or speed as the reason they juggle multiple systems. GitHub Actions is free and fast for lightweight tasks, while Jenkins or TeamCity may be preferred for heavier lifting. In some cases, on-premises solutions are simply cheaper to maintain for large workloads. Some companies find the time it takes to migrate to a new tool so prohibitive that they decide not to do so at all. That’s how different smaller teams within one company can end up using multiple CI/CD tools. ## The “adoption lag” effect What’s popular with individual developers doesn’t always translate immediately into enterprise adoption. GitHub Actions may dominate side projects and open-source work, but in enterprises, it hasn’t yet displaced legacy tools like Jenkins and GitLab CI. This is a familiar pattern in technology: Personal usage often sets the trend, but large organizations typically adopt more slowly due to legacy systems, compliance demands, and migration costs. … The reasons behind this hesitation are straightforward. **Cost** remains a significant barrier, with many organizations finding AI-enhanced solutions prohibitively expensive for broad deployment. Teams are also unsure about the **value** that AI adoption brings to their organizations. **Security considerations** add another layer of complexity, as teams struggle with legitimate concerns about code integrity, data protection, and regulatory compliance when introducing AI into their build and deployment workflows. Restrictions rise with company size, as 27% of large organizations identify security as an impediment to AI adoption, compared to only 9% of small ones.