Java Developers Ditch Oracle as AI Workloads Surge
A new report reveals a mass exodus from Oracle’s Java distribution driven by cost concerns, coupled with a growing reliance on the language for production-grade artificial intelligence applications.
Java developers are increasingly abandoning Oracle’s Java distribution in favor of alternatives, while simultaneously expanding their use of the language to power the next generation of AI. The 2026 State of Java Survey and Report, published by Azul and based on feedback from over 2,000 Java professionals, highlights a strategic shift – a defensive move away from unpredictable licensing costs and an aggressive push to operationalize AI workloads.
The Great Java Migration Accelerates
Migration away from Oracle’s Java distribution has gained significant momentum. A striking 81% of organizations have either migrated, are currently migrating, or plan to migrate at least a portion of their Oracle Java estate to non-Oracle OpenJDK distributions. Furthermore, 63% of respondents indicated an intention to migrate their entire Java estate.
Cost is the primary driver for 37% of those making the switch. Dissatisfaction has been steadily mounting since Oracle introduced employee-based pricing in 2023. The report reveals that a staggering 92% of Java professionals are concerned about Oracle’s pricing structure, a sentiment shared by a significantly larger portion of the community than the previous year – with only 7% reporting no concern.
Operational risk also plays a key role in the exodus. According to the report, 21% of organizations have been subjected to an Oracle Java audit, and the uncertainty surrounding future licensing changes is driving 26% of migration decisions. An additional 31% of enterprises cite a preference for open-source alternatives as their primary motivation.
Java’s Rise as an AI Engine
While Python currently dominates the landscape of AI model training, Java is rapidly establishing itself as the preferred engine for deploying AI into production environments. A significant 62% of organizations now utilize Java to code AI functionality, a notable increase from 50% just last year.
This adoption reflects a critical need to integrate machine learning models into existing, established enterprise architectures. As companies transition from AI experimentation to full-scale deployment, the focus shifts towards scalability and robust security. Notably, 31% of respondents report that over half of the applications they are building now incorporate AI functionality, leveraging libraries such as JavaML and the Deep Java Library (DJL).
To facilitate this transition, technical leaders are prioritizing specific runtime capabilities. Long-term support (LTS) for modern Java versions was identified as essential for an AI-enabled landscape by 35% of participants, alongside built-in security features (34%) and comprehensive observability insights (32%).
Tackling Cloud Waste and Optimizing Performance
Cloud cost management remains a top priority for organizations, with 97% actively taking steps to reduce public cloud spending. Despite these efforts, inefficiencies persist. A substantial 74% of organizations report having more than 20% unused compute capacity within their cloud environments – often provisioned as a buffer against slow start-up times or unpredictable runtime behavior.
In response, 41% of engineering teams are adopting high-performance Java platforms specifically to lower compute costs. This trend is even more pronounced among enterprises heavily invested in Java – those where at least 90% of applications run on the language – with 81% adopting high-performance platforms to improve application output.
DevOps Challenges: Technical Debt and Security Alerts
The report also identifies friction points hindering DevOps teams. “Dead” or unused code is impacting productivity for 63% of respondents, creating technical debt and a reluctance to refactor due to concerns about disrupting legacy components. This ultimately slows development cycles and increases operational risk.
Security alerts further complicate operations. 56% of enterprises are dealing with Java-related Common Vulnerabilities and Exposures (CVEs) on a daily or weekly basis, a significant increase from 41% in 2025. A concerning 30% of teams are spending more than half their time investigating false positives, often triggered by scanners flagging libraries that are present in the codebase but are never actually executed in production.
“Java continues to prove its durability and strategic importance as enterprises navigate one of the most transformative periods in modern computing,” said Scott Sellers, Co-Founder and CEO at Azul. “From powering the next generation of AI-driven applications to helping organizations regain control of cloud spend and modernize their estates, Java remains at the centre of innovation and operational excellence.”
For Java developers, the data underscores the need to address operational overhead – including unnecessary licensing fees, unused cloud capacity, and legacy code – to effectively accommodate the expanding landscape of AI workloads in production environments.
Keep reading
