For years, the Java conversation felt predictable. It was the "enterprise workhorse" language: reliable, widely deployed, and quietly running critical systems behind the scenes. But the latest numbers suggest something more interesting is happening. Java teams are making two moves at the same time: cutting ties with Oracle's Java distribution to reduce licensing pain, and leaning harder into Java to run AI in real production environments.

That combination is reshaping how technical leaders think about their Java stacks. It is no longer just about keeping legacy systems alive. It is about making the platform cheaper to operate, easier to govern, and fast enough to support modern workloads.

The Quiet Shift Away From Oracle Is Turning Into a Stampede

One of the strongest signals in the survey is how normal it has become to move off Oracle's Java distribution. According to the 2026 State of Java Survey and Report published by Azul (based on responses from more than 2,000 Java professionals), most organizations are no longer "thinking about it" in theory. They are already migrating, actively migrating, or planning to migrate at least part of their Oracle Java estate to non-Oracle OpenJDK distributions.

Even more telling is the number of teams who want to go all-in. A large portion of respondents say their goal is not a partial shift but a complete migration of their Java estate.

Why the urgency? Money is the obvious answer, but it is not the only one.

Licensing Anxiety Is Now a Permanent Background Noise

Cost is a major driver of the move, and it is easy to see why. Oracle's pricing changes in recent years have made budgeting feel less predictable, and nobody enjoys uncertainty when Java is powering hundreds or thousands of applications. In the survey, a very large majority of Java professionals say they are concerned about Oracle's pricing model, while only a small minority claim they are not worried at all.

That matters because Java is rarely a "small line item." When a runtime touches everything from web services to internal tools to core back-office systems, pricing changes can ripple across an entire organization.

The outcome is a defensive reaction: reduce exposure, remove unpredictability, and regain cost control by moving to OpenJDK distributions that better match the organization's risk tolerance.

Audits and Operational Risk Are Pushing Teams to Act

Licensing cost is one pressure point. Operational risk is another.

A meaningful chunk of organizations report being subjected to Oracle Java audits. Even if an audit does not end in disaster, it introduces disruption, legal overhead, and leadership attention that most engineering teams would rather spend elsewhere. Combine that with the fear of future licensing changes, and the migration decision starts looking less like an optional optimization and more like a risk-reduction project.

Interestingly, some teams are motivated by something simpler than fear: preference. A significant group of enterprises say they are migrating because they want open-source alternatives as a default position, not because they are forced into it.

Java's New Role: Not Training Models, Running Them

At first glance, "Java and AI" might sound odd if you're used to seeing Python as the default language in machine learning. And that part remains true: Python still dominates training and experimentation.

But production is a different world.

Once a model is trained, real organizations need to deploy it inside existing systems, comply with security policies, scale it predictably, monitor it, and integrate it with APIs, databases, and enterprise workflows. That is where Java fits naturally. It already lives inside many of the environments where AI features must operate.

In the survey, a growing majority of organizations say they now use Java to implement AI functionality, rising noticeably from the previous year. That is a major signal that AI is no longer trapped in notebooks and prototypes. It is showing up inside customer-facing apps, internal decision tools, fraud detection pipelines, automation workflows, and "smart features" layered into established services.

From "AI Experiment" to "AI Everywhere" Apps

Another important shift is how deeply AI is being embedded into application portfolios. A sizable portion of respondents say that more than half of the applications they build now include AI functionality in some form.

That does not necessarily mean every app is running cutting-edge generative AI. In many cases, "AI functionality" can include classical ML models, ranking, classification, anomaly detection, recommendation, forecasting, or hybrid approaches where a model complements traditional rules and business logic.

The practical point is this: once AI features become common, you stop treating AI as a special project and start treating it as normal software development. Libraries like JavaML and the Deep Java Library (DJL) reflect this reality by making model integration feel more like regular Java work, rather than a separate "data science" universe.

What Teams Want From the Java Runtime in an AI World

As AI moves into production, priorities shift toward reliability and governance. In the survey, technical leaders highlight a few runtime capabilities that matter most.

Long-term support (LTS) becomes especially valuable because many enterprises cannot afford to chase rapid upgrade cycles when AI features are already increasing operational complexity. Built-in security remains central because AI workflows often touch sensitive data. Observability rises in importance because it is hard to trust AI-driven behavior without strong monitoring, logging, and performance visibility.

In other words, the "AI era" is making classic enterprise concerns more important, not less.

Cloud Waste Is Still Huge, and Java Performance Is Being Used to Fight It

The report also points to something most teams already feel: cloud spending is under constant scrutiny. Nearly every organization says they are actively working to reduce public cloud costs, yet waste remains common. A large share of enterprises report having significant unused compute capacity sitting around.

Why would teams keep that much slack? Because performance unpredictability is expensive. If applications start slowly, spike unpredictably, or behave inconsistently under load, teams compensate by overprovisioning. That buffer feels safer than being paged at 2 a.m., but it also burns money every hour of every day.

This is where "high-performance Java" platforms enter the conversation. Many engineering teams say they are adopting more performant Java runtimes or platforms specifically to reduce compute needs and cut cloud costs. Among organizations where Java dominates the application portfolio, the adoption rate is even higher, which makes sense: if most of your workloads run on Java, runtime efficiency directly affects your cloud bill.

DevOps Friction: Dead Code, Technical Debt, and Alert Fatigue

Performance and cost are not the only pain points. The survey highlights several operational issues that slow teams down.

A large number of respondents say "dead" or unused code is harming productivity. That kind of technical debt creates a nasty feedback loop: teams are afraid to remove old code because they cannot be sure what depends on it, so the codebase keeps growing, complexity increases, and releases get riskier.

Security adds another layer of friction. Many enterprises report dealing with Java-related CVEs frequently, and the volume appears to be increasing year over year. Even worse, a meaningful portion of teams spend huge amounts of time chasing false positives, often because scanners flag libraries that exist in the codebase but are never actually used at runtime.

That combination, growing codebases and noisy security workflows, becomes a real tax on the ability to ship AI features quickly and safely.

Why This All Connects Back to AI

When you zoom out, the story becomes clear.

AI is expanding into production systems. That expansion forces organizations to clean up operational overhead and uncertainty. If your licensing costs are unpredictable, cloud waste is high, and your engineering team is drowning in dead code and false alerts, it becomes hard to scale AI responsibly.

So the "Java shift" is not just about ideology or vendor preference. It is a practical move to remove friction so organizations can spend more time building and operating AI-enhanced applications.

Final Thoughts

Java's reputation as a durable enterprise platform is being reinforced in a surprising way: not by standing still, but by adapting to what production AI actually needs. The survey data suggests Java teams are doing two sensible things at once. They are reducing licensing uncertainty by moving away from Oracle's distribution, and they are strengthening their runtime foundations so Java can run AI features reliably at scale.

If you're a Java developer or technical lead, the takeaway is less about hype and more about housekeeping. The teams that win with production AI will likely be the ones that simplify their licensing exposure, reclaim cloud waste, trim dead code, and reduce security noise. That operational breathing room is what makes room for the next wave of AI work to land safely in production.