The State of Java CI 2026

The JVM carries the heaviest fixed-cost CI profile of any mainstream backend stack, and almost all of it is dependency resolution, warmup, and integration startup rather than the test logic itself.

Executive summary

The JVM has the heaviest fixed-cost CI profile of the mainstream backend languages, and the weight comes from three places that compound rather than from any single villain. A large dependency graph that must be resolved and downloaded, a JVM and build-tool daemon that pay a warmup cost on every cold runner, and integration suites that spin up databases, message brokers, and containers before a single assertion runs. None of these is the test logic itself, yet together they routinely outweigh it, which is why JVM CI feels slow even when the tests are fast.

The Maven-versus-Gradle question that JVM teams agonize over is, on a cold CI runner, largely a non-question. Both tools pay the full dependency-resolution cost, both start the JVM from cold, and the daemon Gradle relies on has nothing cached to reuse. The real distinction is caching and incrementality: Gradle's build cache and configuration cache can skip large amounts of already-done work, but only when that cache persists across runs, while Maven is simpler and reruns more. Without a persisted cache the choice barely moves the needle; with one, it becomes decisive in Gradle's favor.

Integration tests, not unit tests, set JVM pipeline length. The modern JVM testing stack leans heavily on Testcontainers and real dependencies booted in containers, which catches a class of bug that mocks hide but pays for it in startup time. Adding a Testcontainers-backed integration layer roughly triples median pipeline time versus a unit-only suite, and a full end-to-end layer pushes it further still. The fastest JVM teams do not avoid integration tests; they manage where and how often the heavy layers run.

Because so much of the JVM CI bill is fixed overhead and transient flakiness, the leverage in JVM CI sits unusually far from the application code. Warm caches attack the dependency-resolution and warmup tax; warm runner pools attack the daemon and JVM cold start; and automatic retry attacks the Testcontainers startup races, port conflicts, and network blips that produce most red JVM builds with a clean diff. All three are infrastructure moves that reclaim more of the minute on the JVM than they would on a leaner stack.

The practical conclusion is that a team chasing faster JVM CI should look at its runner before it looks at its tests. A persistent local-repository cache, a persisted Gradle build cache, a warm pool that keeps the daemon and JVM hot across jobs, and self-healing retries together address the dependency tax, the warmup tax, and the flaky tax that dominate the JVM minute, without touching application or test code. Those are the moves that turn the heaviest CI profile in mainstream backend development back into a manageable one.

Gradle wins on CI only when its cache is warm

On a cold runner Gradle and Maven land in the same range, and the cold-versus-warm chart shows why. A cold Maven build sits near 264 seconds and a cold-daemon Gradle build near 231, close enough that the choice between them is mostly a wash. Both pay full dependency resolution, both start the JVM from nothing, and Gradle's daemon, which is supposed to be its advantage, has no warm state to draw on because the runner just booted.

Gradle's real advantage appears only when its build cache and configuration cache persist across runs. A warm Gradle build with the build cache populated drops to roughly 96 seconds, well below the warm Maven number of about 158 with a warm repository cache. The gap between warm Gradle and warm Maven is the gap between skipping already-compiled work and merely skipping already-downloaded dependencies, and it is substantial.

The decision rule that follows is clean. Without a persisted cache, the Maven-versus-Gradle question barely matters, and a team should pick on ergonomics and familiarity rather than expected CI speed. With a persisted cache, Gradle's incrementality is decisive, but realizing it requires a runner that keeps the cache warm across jobs, which a default ephemeral runner does not. The choice of build tool is therefore downstream of the choice of runner architecture, not independent of it.

• Cold Maven and cold-daemon Gradle land within roughly 30 seconds of each other; the choice is a wash when cold.
• Warm Gradle with a populated build cache (~96s) beats warm Maven with a repo cache (~158s) by a clear margin.
• Gradle wins only with a persisted cache, which requires a runner that keeps state warm across jobs.

Integration tests, not unit tests, set your pipeline length

The test-layer chart is the clearest single picture of JVM pipeline cost. A unit-only suite is the baseline; adding slice and web-layer tests pushes median pipeline time to roughly 1.7x; adding a Testcontainers-backed integration layer takes it to about 3.3x; and a full end-to-end suite pushes it past 4.6x. The cost is not in the assertions, it is in standing up the real databases, brokers, and services those higher layers exercise.

Testcontainers and similar approaches are genuinely valuable, which is why teams adopt them despite the cost. Booting a real Postgres or Kafka in a container catches integration bugs, schema mismatches, and serialization problems that mocked dependencies cheerfully hide, and the confidence that comes from testing against the real thing is hard to give up once a team has it. The problem is not the integration tests; it is running the heaviest layer on every push as though it were free.

The fastest JVM teams manage the layers rather than avoiding them. They split the test layers across pipeline stages so a fast unit stage gives quick feedback, they run the heavy integration suite in parallel shards so its wall-clock is the slowest shard rather than the sum, and they gate the slowest end-to-end layer to pre-merge or a schedule rather than every commit. The integration coverage stays; the per-push cost of it does not.

Dependency resolution is the most cacheable JVM cost

On an uncached Maven build, dependency resolution and download can be the single largest slice of the minute, around 44 percent in the modeled case. The JVM ecosystem's dependency graphs are wide and deep, and a cold build must resolve the full transitive closure and fetch every artifact it does not already have. This is pure overhead in the sense that it produces nothing the build did not have last time; it is the runner re-fetching a repository it already had on the previous run.

A warm local-repository cache erases most of this cost, because the artifacts are already present and resolution collapses to a graph walk over local files. The trap is the same one that bites every cached CI step: hosted caches frequently cold-miss on the first run of a branch and re-upload the whole repository per job, so a team that thinks it is caching dependencies is actually paying upload and download cost to rebuild a cache it then discards. The benefit is real only when the cache persists across runs.

Because dependency resolution is both expensive and almost perfectly cacheable, it is the highest-leverage, cheapest fix in JVM CI. A persistent local-repository cache that survives across runs turns a 44-percent slice of the uncached minute into a near-zero one, with no change to the build configuration beyond pointing the build at a cache that actually persists. It is the first thing a team chasing faster JVM CI should fix, ahead of any test-level work.

JVM and daemon warmup is a fixed tax you pay per cold job

Class loading, JIT warmup, and Gradle daemon startup all begin from cold on every fresh hosted runner, and that overhead is proportionally largest on short builds. The JVM is designed to get faster the longer it runs, as the JIT compiler identifies and optimizes hot paths, but a CI job that boots the JVM, runs once, and exits never reaches the steady state where that optimization pays off. CI pays the warmup cost on every job and collects the benefit on none.

Reusing a warm runner with the daemon already up amortizes this tax across many jobs instead of paying it on each one. On a developer laptop the daemon stays alive between builds and the JVM is already hot, which is a large part of why local builds feel faster than the same build in CI. The difference is not the hardware; it is that the laptop keeps a warm process alive and the ephemeral CI runner discards it.

On hosted runners you cannot keep the daemon warm across jobs, because the runner is destroyed when the job ends, so this saving is structurally unavailable. Managed runners with warm pools can keep a hot process alive across jobs, which is where much of the JVM-specific saving comes from. The warmup tax is therefore not a Gradle or Maven tuning problem; it is a runner-architecture problem, and it is solved by the runner keeping state warm rather than by any build-tool flag.

The JVM CI minute is mostly overhead, and overhead is addressable

The Java-minute split is a map of where an integration-heavy JVM pipeline actually spends its billed time, and the lesson is that the test logic is the minority. Integration and unit tests together are the largest slice at roughly 43 percent, dependency resolve and download is about 24 percent, compile plus JVM and daemon warmup is around 21 percent, and re-runs from flaky failures take the remaining 12 percent or so. Strip out the dependency, warmup, and flake slices and more than half the minute is overhead the test author never wrote.

What makes this encouraging rather than discouraging is that every one of those overhead slices is addressable by infrastructure rather than by code. Dependency resolution yields to a persistent repository cache, warmup yields to a warm runner pool, and the flaky re-runs yield to automatic retry. The team does not have to make its tests faster or fewer to reclaim the overhead; it has to stop the runner from discarding the state and the work that would make the overhead disappear.

This is the structural reason the JVM benefits more from runner-level investment than leaner stacks do. A language with a small dependency graph, fast startup, and few integration dependencies has little overhead to reclaim, so its leverage is in the test work itself. The JVM is the opposite: its overhead is large, fixed, and almost entirely cacheable or retryable, so the largest, cheapest wins live in the runner rather than in the test suite.

• More than half the integration-heavy JVM minute is dependency resolution, warmup, and flaky re-runs, not test logic.
• Each overhead slice is addressable by infrastructure: caches, warm pools, and automatic retry.
• The JVM gains more from runner-level investment than leaner stacks because its overhead is large and cacheable.

Most flaky JVM failures are startup races, not bugs

A large share of red JVM builds with a clean diff are not failures of the code under test at all. They are Testcontainers startup races where a test queried a container that was not ready yet, port conflicts where two parallel tests grabbed the same port, or network blips against a dependency that had just booted. These are mechanical, environmental failures, and they are green on a clean retry against a fresh environment, because the only thing wrong was timing.

The cost of these transient failures has two parts, and the smaller part is the wasted minutes. The larger part is the context switch: a developer who pushed a green change and came back to a red integration test has to stop, read the failure, decide whether a Testcontainers timeout is real or transient, and manually re-run, and that interruption is far more expensive than the compute the re-run consumes. On an integration-heavy JVM suite, where the surface for startup races is wide, this drip of false reds is a steady tax on focus.

Self-healing runners detect and retry these transient failures automatically, removing both the re-run minutes and the context switch before a red check ever reaches the pull request. At roughly 70% lower effective cost than hosted runners, they let teams hold change-failure rate inside the elite 0-15% band without anyone re-running a job by hand. The failures that remain are the ones a developer actually wants to see, which is the whole point of a CI signal.

Shard the heavy layers instead of buying a bigger runner

When a JVM pipeline gets slow, the reflexive fix is a bigger runner, and it is usually the wrong one. A larger runner with more cores helps only to the degree the work is parallel, and a serialized integration suite that boots containers one stage at a time leaves most of those cores idle while the slow critical path runs. Paying for an eight-core runner to run a sequential suite buys a little headroom and a lot of wasted capacity.

Sharding the heavy layers across parallel jobs is the move that actually scales. Splitting a Testcontainers integration suite into shards that run concurrently means the wall-clock is the slowest shard rather than the sum of all of them, and it scales with the number of shards rather than the size of a single runner. The same money spent on parallel jobs instead of a single fat runner buys a genuinely shorter pipeline rather than a slightly faster sequential one.

Sharding pairs naturally with the warm-cache and warm-pool story, because each shard wants the same warm dependency cache and hot JVM that a single job does. A managed runner that supplies a warm cache to every shard and scales the shards out elastically gives the heavy integration layer both parallelism and warmth at once, which is what turns the 3.3x integration multiplier from a wall-clock penalty into a cost that runs in parallel and finishes fast.

Recommendations

Fix the dependency cache before you touch the tests

Dependency resolution can be 44 percent of an uncached Maven minute and it is almost perfectly cacheable, so a persistent local-repository cache that survives across runs is the cheapest, highest-leverage win in JVM CI. Make sure the cache genuinely persists rather than being rebuilt and re-uploaded per job, which is the common failure mode that makes teams think they are caching when they are not.

Choose the build tool downstream of the runner

Without a persisted cache, Maven and Gradle perform similarly on a cold runner, so pick on ergonomics. With a persisted cache, Gradle wins decisively, but only if the runner keeps that cache warm across jobs. Decide the runner architecture first; the build-tool advantage follows from it rather than the other way around.

Stage and shard the test layers

Integration tests, not unit tests, set pipeline length, roughly tripling it. Split the layers across stages so a fast unit stage gives quick feedback, shard the heavy integration suite so its wall-clock is the slowest shard rather than the sum, and gate the slowest end-to-end layer to pre-merge or a schedule rather than every push.

Recover JVM and daemon warmth with a warm pool

JIT warmup and daemon startup are paid on every cold hosted job and collected on none, because the runner is destroyed before the JVM reaches steady state. A managed runner with a warm pool keeps a hot process alive across jobs, which is where much of the JVM-specific saving lives. Treat warmup as a runner-architecture problem, not a build-tool flag.

Auto-heal Testcontainers and startup races

Most red JVM builds with a clean diff are startup races, port conflicts, or network blips against a freshly booted dependency, all green on a clean retry. Retry these transient failures on a fresh environment automatically so the re-run minutes and the context switch never reach a developer, and change-failure rate stays inside the elite band without manual re-runs.

Outlook

The JVM's fixed-cost CI profile is not going to get lighter on its own, because the things that make it heavy, large dependency graphs, JIT-based runtime, and a strong culture of testing against real dependencies, are also things the ecosystem values and is not going to abandon. What will change is how much of that fixed cost teams choose to pay on every job versus amortize away, and the gap between teams that have moved the overhead onto warm infrastructure and teams still paying it cold will widen through 2026.

Gradle's incrementality advantage will keep growing in importance as more of the ecosystem adopts the build cache and configuration cache, but its realization will stay gated on runner architecture. The teams that pair Gradle with a runner that keeps the cache warm will pull ahead of both Maven teams and Gradle teams running cold ephemeral builds, while a Gradle team on a default ephemeral runner will keep getting most of Maven's speed and little of Gradle's, which is the worst of both.

The durable direction is that JVM CI increasingly wants to look like the JVM laptop experience: a hot JVM, a warm daemon, a populated dependency cache, and integration tests that boot fast because the containers and caches are already close. Delivering that in CI means warm caches, warm runner pools, parallel sharding, and automatic recovery for the mechanical failures an integration-heavy suite cannot afford to surface to a human. The teams that build their JVM CI on that foundation will tame the heaviest CI profile in backend development; the teams that do not will keep paying the dependency, warmup, and flaky taxes on every single job.

Methodology

This report focuses on the JVM CI pipeline: Maven versus Gradle, JVM and daemon warmup, dependency and build caching, and the cost of slow integration suites. Build timings, minute-split, and test-layer figures are Latchkey modeled estimates derived from typical mid-size JVM service shapes and published GitHub-hosted runner pricing; the CI adoption headline is from the Stack Overflow Developer Survey. Where a figure is attributed to a named source it reflects that source; where a figure is labeled modeled it is an illustrative estimate intended to show direction and magnitude rather than a precise population value. Figures labeled "modeled" are illustrative estimates derived from public pricing and typical pipeline shapes, not a primary survey; figures attributed to a named source reflect that source. Pricing reflects published rates at time of writing and should be verified against current provider pricing.

Sources

• Stack Overflow Developer Survey
• JetBrains Developer Ecosystem Survey
• GitHub Actions - billing & pricing
• GitHub Actions documentation

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