By 2025, ARM processors have stepped far beyond their mobile roots and started to shift the balance in the data center. Apple’s M-series, AWS’s Graviton 4, and Google’s Axion now run large-scale cloud workloads with ease. The reasons are hard to ignore:
- less power draw, more physical cores
- a strong price-to-performance tradeoff
For Java developers and DevOps teams, this opens the door to real gains—cutting infrastructure costs, improving energy use, and staying fast where it matters.
Java has come a long way on ARM. Runtime improvements, smarter garbage collectors, and better vector performance now make it a solid fit. CI/CD pipelines work without extra effort. This guide walks through:
- real-world benchmarks
- examples from AWS and Google Cloud
- gives hands-on advice for tooling, deployment, and compatibility
If you're weighing cost, speed, or sustainability, this will help you judge whether a move to ARM makes sense—and how to pull it off cleanly.
The evolution of ARM: from Acorn to the cloud
ARM’s story starts in a wooden shed in Cambridge, 1985, where engineers built the first chip - the ARM1 - for a line of educational computers for BBC. The goal was clear: keep it simple, keep it efficient.
Short pipelines (with fewer processing stages between fetching and executing each instruction), simpler architecture with fewer transistors (requiring less power and chip area), and a RISC (Reduced Instruction Set Computing - a small, uniform set of simple commands) set the tone early. That lean design helped ARM take over the mobile world (one of the first devices powered by it was Apple Newton - a smartphone from 1992) - and it didn’t stop there. Today, ARM cores show up almost everywhere, with over 300 billion sold and still climbing.
The shift began with ARM’s Neoverse line, built specifically for servers and high-performance tasks.
- Neoverse N1 (2019)
- N2 with Armv9 and SVE2
- the latest V2 “Demeter”
These chips pushed core counts and efficiency into new territory - up to 192 vCPUs and strong performance-per-watt.
Cloud vendors moved quickly.
AWS rolled out Graviton, Google followed with Axion, and Ampere Altra gained traction across Oracle, Azure, and OCI. These ARM-based platforms now offer 30–65% better price-performance and as much as 60% lower energy use compared to x86. What started as a mobile chip is now powering the cloud - and Java is increasingly part of that shift.
Java’s transformation on ARM: compatibility, optimization, and performance
Java’s path on ARM has moved fast. Ten years ago, running it on ARM was more of an experiment. Now it’s a serious, high-performance option. Things started to shift with JEP 237 in 2016, adding a Linux/AArch64 port to the JDK. JEP 297 came next, merging 32- and 64-bit ARM code into a single path, making builds more consistent across architectures.
Since then, each JDK release has pushed support further. JEP 315 added NEON intrinsics, tripling the speed of core operations like String.indexOf. Amazon’s Corretto Crypto Provider 2 helped too - on Graviton 3, TLS handshakes can be up to 13× faster, according to AWS.
Today’s JVMs tap directly into what ARM does well. The Vector API (JEP 460), still in incubation, can generate SVE and SVE2 instructions for fast, parallel workloads. JEP 442 improves native interop across platforms and helps phase out JNI - key for moving away from x86-bound libraries. Garbage collection and memory handling have also leveled up.
Shenandoah and Gen-ZGC keep pause times low even with large heaps. Lilliput (JEP 450) cuts object header size to ease memory strain, and Project Loom brings in lightweight virtual threads that scale well on ARM’s many-core setups. On Graviton, one JVM can handle hundreds of thousands of socket connections per core - sometimes making tools like Netty unnecessary. Java doesn’t just run on ARM anymore - it runs well.
Deploying Java on ARM: tooling, CI/CD, and multi-architecture builds
One of the biggest advantages for Java developers working with ARM is how easy deployment has become, so long as your code doesn’t depend on architecture-specific native libraries. For pure Java applications, it’s straightforward: the same JAR runs on x86 and ARM without modification. But if you're using JNI libraries built only for x86, you'll need to recompile them for ARM or swap in cross-platform options.
Containerization and modern CI/CD tools have made cross-architecture builds much simpler. Docker’s buildx supports multi-arch images using QEMU emulation, so you can create both ARM and x86 builds with a single command. The result is a unified Docker manifest - users pull the same image tag, and Docker serves up the right version for their hardware.
CI platforms now support ARM directly. GitHub Actions offers hosted runners for Linux and Windows on arm64, and CircleCI supports both hosted and self-hosted ARM VMs. That means no more juggling cross-build setups, you can test and release ARM-native builds right from the pipeline.
Full integration and load testing can run directly on ARM hardware, avoiding the overhead and fragility of QEMU scripts. In practice, you can ship production-ready Java apps for both ARM and x86 from a single pipeline, with less complexity, and possibly lower CI costs, thanks to ARM’s power efficiency.
Performance benchmarks: how Java web apps stack up on ARM vs x86
Benchmarks show ARM isn’t just holding its own, it often outperforms x86 in real Java workloads. Take AWS’s Spring PetClinic tests: Graviton 4, built on Neoverse V2, delivered about 30% more throughput than similarly priced x86 instances. A key reason is the use of physical cores instead of hyper-threaded ones, which gives more consistent, isolated thread performance, especially valuable for latency-sensitive apps.
Google’s Axion-powered C4A instances also show strong results - up to 40% better price-performance and 60% higher energy efficiency than top-tier x86 options.
Ampere’s Altra Max takes a different path, using up to 128 single-threaded cores for clean, linear scaling. By skipping simultaneous multithreading (SMT), it avoids the usual performance tradeoffs. For workloads like web services, caching, and video encoding, Altra Max can deliver over twice the performance-per-watt of many Xeon systems.
These examples make it clear: Java apps, particularly microservices and I/O-heavy workloads, can run as fast and cheaper on ARM, often with little to no code changes.
ARM’s FinOps advantage: cost, licensing, and sustainability benefits
ARM’s technical strengths often turn into real savings, both financial and environmental, especially for teams focused on FinOps or ESG targets. Unlike x86 chips that depend on hyper-threaded virtual cores, ARM processors like AWS Graviton and Ampere Altra use full physical cores.
That matters, especially for workloads priced by vCPU, such as commercial JVMs. Physical cores deliver consistent performance without the resource contention of SMT, which means fewer instances can handle the same load.
The numbers back it up. Java microservices moved to Graviton 4 typically see 15–30% more throughput, and CI pipelines run faster thanks to consistent CPU behavior. Cost models show real savings, instance bills drop, and power use can fall by as much as 60%. For large deployments, those gains add up quickly. Teams can shrink their clusters and still meet performance targets.
From a sustainability angle, the shift is significant. ARM’s lower energy use translates to smaller carbon footprints, now a reportable metric under CSRD and GRI standards. AWS even exposes performance and energy data that plugs straight into Excel-based FinOps dashboards, making ESG reporting easier for finance teams.
Deloitte estimates that better FinOps practices, including smarter hardware choices, could save enterprises $21 billion in 2025. ARM isn’t just more cost-effective—it’s cleaner and easier to manage. That’s a win on all fronts for modern IT teams.
When to migrate: evaluating Java on ARM for your workloads
Choosing to move Java workloads to ARM isn’t about chasing trends; it’s about fit. ARM shines where thread isolation, power efficiency, and cost-per-performance matter most. That makes it a strong match for Java microservices, web apps, and cloud-native services that scale out or handle heavy I/O. And if you’re paying per vCPU, ARM’s physical cores can offer instant savings and more predictable performance.
That said, ARM isn’t a universal fit. If your app depends on native JNI libraries, you’ll need to recompile them for ARM or swap in Java-based alternatives. And for workloads that push single-thread performance to the limit or rely on x86-specific instructions like AVX-512, Intel or AMD might still be the better choice for now.
The upside?
It’s never been easier to test the waters. With multi-arch Docker images and ARM-ready CI pipelines, setting up test environments is fast and low-cost. If your app scales well and doesn’t rely on x86-specific code, now’s a good time to benchmark and explore the return. The savings in cost, power, and emissions might be bigger than you expect.
Java on ARM in 2025 — from experiment to enterprise standard
Let’s recapitulate what we know already.
What started as a niche experiment is now a serious default. In 2025, Java on ARM isn’t just possible, it’s often the better choice. Chips like AWS Graviton 4, Google Axion, and Ampere Altra Max offer strong performance with far less power draw, making ARM a core platform for cloud-native Java. The JVM has kept up, with support for vector instructions, smarter garbage collection, improved native access, and seamless CI/CD integration.
For organizations focused on cost, sustainability, or scaling modern workloads, ARM presents a strong case. Achieve 15–30% performance gains, cut infrastructure costs significantly, and reduce your carbon footprint all while keeping your Java stack largely intact. While certain workloads tied to x86-native libraries or specialized compute needs may still favor traditional platforms, most Java applications can now transition to ARM with minimal friction and maximum payoff.
The takeaway is clear: it’s no longer a question of if ARM makes sense for Java, it’s a question of when and how much you stand to gain.
