As AI inference moves from prototype to production, Java services must handle high-concurrency workloads without disrupting existing APIs. This article examines patterns for scaling AI model serving in Java while preserving API contracts. Here, we compare synchronous and asynchronous approaches, including modern virtual threads and reactive streams, and discuss when to use in-process JNI/FFM calls versus network calls, gRPC/REST. We also present concrete guidelines for API versioning, timeouts, circuit breakers, bulkheads, rate limiting, graceful degradation, and observability using tools like Resilience4j, Micrometer, and OpenTelemetry.
Detailed Java code examples illustrate each pattern from a blocking wrapper with a thread pool and queue to a non-blocking implementation using CompletableFuture and virtual threads to a Reactor-based example. We also show a gRPC client/server stub, a batching implementation, Resilience4j integration, and Micrometer/OpenTelemetry instrumentation, as well as performance considerations and deployment best practices. Finally, we offer a benchmarking strategy and a migration checklist with anti-patterns to avoid.