Sponsor Editorial

While enterprises negotiate GPU reservations to the dollar, a growing share of production AI infrastructure runs on the JVM: Kafka, Spark, Flink, Elasticsearch, Cassandra, HDFS upstream, and Spring AI/LangChain4j downstream. The guide frames this as "a Python core flanked by Java edges" and argues those edges carry the bulk of the latency budget and cloud bill. It details three compounding drags — conservative HotSpot C2 JIT compilation (throughput), stop-the-world GC pauses (latency, e.g. a 100–200ms Elasticsearch pause eating 10–25% of a RAG budget), and repeated cold starts from deployments, auto-scaling, and pod cycling (warmup). It explains why AI workloads amplify all three (6KB embedding vectors, sub-100ms SLAs, 500-node scale, and a measurement gap), maps Azul Prime's Falcon, C4, and ReadyNow to each drag, walks three architecture scenarios (RAG pipeline, batch feature engineering, real-time Flink serving), and closes with a practical instrumentation checklist teams can run in a week using existing tooling.