Apple has migrated its global Password Monitoring service from Java to Swift, achieving a 40% increase in throughput and significantly reducing memory usage—freeing up nearly 50% of previously allocated Kubernetes capacity.
In a recent post, Apple engineers detailed how the rewrite helped the service scale to billions of requests per day while improving responsiveness and maintainability. The team cited lower memory overhead, improved startup time, and simplified concurrency as key reasons for choosing Swift over further JVM optimization.
Swift allowed us to write smaller, less verbose, and more expressive codebases (close to 85% reduction in lines of code) that are highly readable while prioritizing safety and efficiency.
Apple’s Password Monitoring service, part of the broader Password app’s ecosystem, is responsible for securely checking whether a user’s saved credentials have appeared in known data breaches, without revealing any private information to Apple. It handles billions of requests daily, performing cryptographic comparisons using privacy-preserving protocols.
This workload demands high computational throughput, tight latency bounds, and elastic scaling across regions. Traffic fluctuates significantly over the course of a day, with regional peaks differing by up to 50%. To accommodate these swings, the system must quickly spin up or wind down instances while maintaining low-latency responses.
Apple’s previous Java implementation struggled to meet the service’s growing performance and scalability needs. Garbage collection caused unpredictable pause times under load, degrading latency consistency. Startup overhead—from JVM initialization, class loading, and just-in-time compilation, slowed the system’s ability to scale in real time. Additionally, the service’s memory footprint, often reaching tens of gigabytes per instance, reduced infrastructure efficiency and raised operational costs.
Originally developed as a client-side language for Apple platforms, Swift has since expanded into server-side use cases. Apple’s engineering team selected Swift not just for its ecosystem alignment, but for its ability to deliver consistent performance in compute-intensive environments.
The rewrite also used Vapor, a popular Swift web framework, as a foundation. Additional custom packages were implemented to handle elliptic curve operations, cryptographic auditing, and middleware specific to the Password Monitoring domain.
Swift’s deterministic memory management, based on reference counting rather than garbage collection (GC), eliminated latency spikes caused by GC pauses. This consistency proved critical for a low-latency system at scale. After tuning, Apple reported sub-millisecond 99.9th percentile latencies and a dramatic drop in memory usage: Swift instances consumed hundreds of megabytes, compared to tens of gigabytes with Java.
Startup times also improved. Without JVM initialization overhead or JIT warm-up, Swift services could cold-start more quickly, supporting Apple’s global autoscaling requirements.
Apple’s migration reflects a broader trend: the shift toward performance-oriented languages for services operating at extreme scale. Meta has a long history with Rust from hyper-performant Source control solutions to programming languages for the blockchain. Netflix introduced Rend, a high-performance proxy written in Go, to take over from a Java-based client interacting with Memcached. AWS increasingly relies on Rust in services where deterministic performance and low resource usage improve infrastructure efficiency.
While this isn’t a sign that Java and similar languages are in decline, there is growing evidence that at the uppermost end of performance requirements, some are finding that general-purpose runtimes no longer suffice.
