Server side Java is usually the first and most rewarding part of an estate to migrate, because it is centrally managed and runs on standard Java. A phased runtime swap removes a large share of the workloads inside the per employee count.
Server side Java is usually the first and most rewarding part of an estate to migrate, because it is centrally managed, well instrumented, and already part of a deployment pipeline. The runtime is the same standard Java, so most applications run on OpenJDK without code changes. The migration becomes a controlled change to your build and deploy process rather than a rewrite, and it removes a large share of the workloads that would otherwise sit inside the per employee count.
There are three reasons to start with the server side. It is centrally controlled, so a small team can move many workloads without touching a single end user device. It is already automated, so the runtime change rides the pipeline you already trust. And it is observable, so you can prove that the new runtime behaves correctly before you widen the rollout. Together these make the server side the fastest place to demonstrate that OpenJDK works in your environment, which builds the confidence the rest of the program needs.
Server side testing is mostly about confirming that behavior and performance match. Run your existing automated tests on the new runtime, then watch the operational signals that matter: startup time, memory behavior under load, garbage collection, and any platform specific flags. Most differences are minor and are addressed with configuration rather than code.
| Check | What to confirm |
|---|---|
| Functional parity | The existing test suite passes on the new runtime |
| Performance | Throughput and latency hold under representative load |
| Memory and GC | Behavior is stable with your chosen collector settings |
| Startup | Service starts within expected time across versions |
| Observability | Logging, metrics, and tracing keep working |
Because OpenJDK and Oracle Java SE are built from the same source, performance is generally equivalent for the same release. Where you see a difference it is usually down to runtime flags or garbage collector settings rather than the distribution itself. The safe approach is to carry your existing tuning across, run a representative load test, and adjust only where the data tells you to. Resist the urge to retune everything at once, because doing so confuses a migration with an optimization project and makes any regression hard to attribute.
If your services run in containers, the migration is often as simple as changing the base image to one built on an OpenJDK distribution and rebuilding. The pipeline carries the change everywhere the image is used, which makes the rollout fast and consistent. For services not yet containerized, the runtime swap is still a managed deployment change, but containerized estates can frequently move a large share of their workloads with a single, well tested base image update.
Most application servers and frameworks support OpenJDK as a first class runtime, because they target standard Java rather than a particular distribution. Confirm the supported runtime versions for each framework you use, since the constraint is usually the major Java version rather than the distribution. Where a framework lags, the right move is often to align your runtime version to what the framework supports, which is a normal compatibility exercise rather than a migration blocker.
The build side of a server migration is usually straightforward. Point your build tooling at the new runtime, rebuild, and run your dependency checks. Watch for libraries that pin a specific runtime behavior, and update them where needed. Standardizing on one or two OpenJDK distributions across your build and run environments keeps the estate governable and avoids subtle differences between where code is built and where it runs.
The safest cutovers are the ones you can watch. Before you widen a wave, make sure your monitoring captures the signals that would reveal a problem early, such as error rates, latency, memory pressure, and garbage collection behavior. Deploy with the ability to roll back quickly, watch the signals through the first business cycle, and only then move to the next wave. This disciplined rhythm is what lets a small team move a large server estate with confidence.
Lead your migration with server side workloads. They are the fastest to prove, the easiest to roll out through a pipeline, and they remove the largest block of avoidable Oracle Java exposure first.
Server side is one phase of the whole program. Once it is moving, bring the desktop fleet and the remaining workloads across, then negotiate the residual. For the full method, see the OpenJDK Migration Playbook. For the desktop companion, read migrating Java desktop applications to OpenJDK, and to size the prize before you start, see estimating the savings from an OpenJDK migration.
Consider an anonymized retailer running several hundred server side Java services, most of them already containerized. The team inventories the runtimes, picks a low risk internal service as a pilot, and swaps its base image to one built on an OpenJDK distribution. The existing test suite passes, a load test confirms performance, and the service runs cleanly in staging and then production. The team captures the steps as a pattern and applies it wave by wave, grouping services by owning team. Within two phases the containerized estate has moved, and the Oracle runtime is retired from those services. A small set of services with vendor constraints is left for a later phase. The figures are indicative, but the sequence is the one that consistently works.
Most server side Java is stateless or externally backed, which makes a runtime swap simple. Stateful and long running services need a little more care, because you cannot simply restart them without a plan. The approach is the same as any careful deployment: drain the service, deploy the new runtime, warm it, and bring it back into rotation, watching the operational signals throughout. Because the runtime behavior is equivalent, the state itself is unaffected. The care is in the orchestration of the restart, not in the runtime change.
Every wave should carry a rollback path, because confidence comes from knowing you can reverse a change quickly if a signal turns bad. Keep the previous runtime image available, define the trigger conditions that would prompt a rollback, and rehearse the rollback on the pilot so the team has done it once before it matters. In practice rollbacks are rare, because the runtime is equivalent, but the existence of a tested rollback path is what lets a small team move a large estate without hesitation.
Will our applications need code changes? For the same Java version, almost never, because OpenJDK and Oracle Java SE are built from the same source. Will performance change? Generally no, and where it does the cause is usually a runtime flag or collector setting rather than the distribution. How do we handle frameworks and application servers? Confirm their supported Java versions, since the constraint is the major version rather than the distribution. What is the fastest way to start? Containerized services, because a single tested base image change can move many workloads through the pipeline at once.
A server migration is only as good as its inventory. Use your configuration management, deployment, and monitoring tooling to find every server side Java runtime, the version it runs, and the service it supports. Pay attention to runtimes hidden inside packaged software and inside build agents, because these are easy to miss and can hold a dependence on Oracle Java that surfaces late. A complete inventory lets you plan the waves accurately and ensures that when you retire Oracle Java you have not left a forgotten runtime behind.
The runtime your code is built and tested against should match the runtime it runs on in production. As you migrate, update your continuous integration pipelines to build and test on the chosen OpenJDK distribution, so that compatibility is proven automatically with every change rather than checked once during the migration. Aligning the build runtime with the run runtime also removes a class of subtle issues that arise when code is built on one runtime and deployed on another.
For cloud hosted services, the migration can intersect with capacity and cost planning. Because OpenJDK distributions are free to run, moving off Oracle Java removes a per employee charge without adding a runtime license cost, which improves the unit economics of every service. Some teams use the migration as a moment to revisit container base images and resource settings, capturing a small efficiency gain alongside the licensing saving. Keep these two efforts separate enough that any performance change can be attributed correctly, but there is real value in aligning them.
As the server estate moves, govern it the same way you would any platform standard. Approve one or two distributions and a small set of releases, name an owner for the update pipeline, and document how exceptions are handled. Standardization keeps the estate supportable and makes the next round of upgrades cheaper, because every service follows the same pattern. The discipline that makes the migration succeed is the same discipline that keeps the estate healthy afterward.
We can help you pick the pilot, prove the pattern, and plan the waves so the server side moves cleanly and the saving lands. Book a strategy call and we will scope it for your estate.
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