Java development services for systems that need to last

Why Java remains a strong choice for business-critical backends

Java's strengths are rarely exciting, and that is the point. Strong static typing, a mature standard library, decades of tooling and one of the most heavily optimized runtimes available make it well suited to systems that must run correctly for years and be maintained by changing teams.

The language has also changed a lot since Java 8. Records, sealed interfaces, pattern matching for switch, text blocks and virtual threads (final in Java 21) remove much of the boilerplate Java was known for. Virtual threads in particular make the simple thread-per-request model viable again for high-concurrency I/O workloads, without forcing the whole codebase into reactive programming.

For most backend work we pair Java with Spring Boot, but we also know when lighter options such as Quarkus or Micronaut make more sense, for example for fast-starting serverless functions or memory-constrained containers.

How we work with Java

We target the current long-term support release (Java 21, or Java 17 where a platform constraint requires it) and keep builds reproducible with Gradle or Maven, a pinned toolchain and dependency locking. Code style and static analysis run in CI on every commit, not only before releases.

Testing follows the shape of the system: fast unit tests with JUnit 5 and AssertJ, integration tests against real databases and message brokers using Testcontainers, and contract tests at service boundaries. This lets us refactor confidently, which matters more in a long-lived Java codebase than any individual design decision.

• Domain-driven package structure with clear module boundaries, enforced with ArchUnit
• Immutable data with records and explicit null handling at API edges
• Structured logging, metrics and tracing from the first deployment
• Container images built with Jib or layered Dockerfiles for fast, small deployments

JVM performance and reliability

Most Java performance problems we encounter are not caused by the JVM itself but by N+1 database queries, unbounded caches, blocking calls inside thread pools or oversized heaps. We start with measurement: JDK Flight Recorder, async-profiler and production metrics, before changing any configuration.

When tuning is needed, we choose garbage collectors deliberately (G1 for most services, ZGC where low pause times matter), size heaps relative to container limits and make sure the JVM is container-aware. The goal is predictable latency under real load, verified with load tests rather than assumed.

Taking over and upgrading existing Java codebases

Many organizations run important systems on Java 8 or 11, often with outdated frameworks and dependencies that no longer receive security fixes. We regularly take over codebases like these: we start with an assessment of the build, dependency tree, test coverage and deployment process, then propose an upgrade path ordered by risk.

Upgrades are done incrementally. We first make the build reproducible and add characterization tests around critical behavior, then move through LTS versions while keeping the application deployable at every step. Tools such as OpenRewrite automate a large share of mechanical changes, including the javax to jakarta namespace migration that usually accompanies a Spring Boot 3 migration.

If the system needs deeper restructuring, for example splitting a monolith, we approach it as part of our backend and API engineering work, extracting services only where there is a clear operational or organizational benefit.

When Java is not the right fit

Java is not always the answer. For small backends-for-frontends tightly coupled to a TypeScript web application, Node.js often means less context switching for the team. For short-lived scripts or data exploration, Python is usually more practical. We will tell you when we think another runtime serves your situation better.