Your GPU Bill Isn't the Whole Story: The Hidden JVM Drag on AI Infrastructure
Kafka, Spark, Flink, and Elasticsearch quietly power most production AI pipelines — and their JVM inefficiencies are costing you nodes, latency, and reliability you're not even measuring.
Can’t make either live session? Register anyway — we’ll send the recording.
Every enterprise is having the same conversation about AI infrastructure costs: GPU reservations, model selection, token throughput. It’s an important conversation. It’s also incomplete.
A surprising amount of production AI infrastructure runs on the JVM — Kafka, Spark, Elasticsearch, Spring AI, LangChain4j. These systems weren’t built for today’s latency budgets, and most teams aren’t instrumented to see what that costs them. Call it JVM drag, and it shows up in three ways: conservative JIT compilation that leaves 20-30% of performance ontapped (Throughput Drag), GC pauses that stall retrieval mid-request (Pause Drag), and every deploy or auto-scale evetn starting cold (Warm-up Drag).
None of this shows up in your GPU utilization dashboard. All of it shows up in your cloud bill.
What You’ll Learn
Where the JVM is quietly running your AI stack, even if you don’t think of it that way
How conservative JIT compilation, GC pauses, and slow warm–up each create measurable, compounding costs
Which metrics to track today to make the hidden drag visible
Who Should Attend
Platform and infrastructure engineers, SREs, architects, and engineering leaders who own the AI infrastructure bill.
Hosted By
Miles Dawborne
Senior Director, Azul
Session 1 — NAM & EMEA
Tuesday, September 29
NAM: 8:00 AM PT / 11:00 AM ET
EMEA: 4:00 PM London (BST) / 5:00 PM CET
Session 2 — NAM & APAC
NAM: September 29, 5:00 PM PT
APAC: September 30, 8:00–9:00 AM Singapore / HK / Tokyo, 10:00 AM Sydney
Can’t make either live session? Register anyway — we’ll send the recording.
Every enterprise is having the same conversation about AI infrastructure costs: GPU reservations, model selection, token throughput. It’s an important conversation. It’s also incomplete.
A surprising amount of production AI infrastructure runs on the JVM — Kafka, Spark, Elasticsearch, Spring AI, LangChain4j. These systems weren’t built for today’s latency budgets, and most teams aren’t instrumented to see what that costs them. Call it JVM drag, and it shows up in three ways: conservative JIT compilation that leaves 20-30% of performance ontapped (Throughput Drag), GC pauses that stall retrieval mid-request (Pause Drag), and every deploy or auto-scale evetn starting cold (Warm-up Drag).
None of this shows up in your GPU utilization dashboard. All of it shows up in your cloud bill.
What You’ll Learn
Where the JVM is quietly running your AI stack, even if you don’t think of it that way
How conservative JIT compilation, GC pauses, and slow warm–up each create measurable, compounding costs
Which metrics to track today to make the hidden drag visible
Who Should Attend
Platform and infrastructure engineers, SREs, architects, and engineering leaders who own the AI infrastructure bill.
Every enterprise is having the same conversation about AI infrastructure costs: GPU reservations, model selection, token throughput. It’s an important conversation. It’s also incomplete.
A surprising amount of production AI infrastructure runs on the JVM — Kafka, Spark, Elasticsearch, Spring AI, LangChain4j. These systems weren’t built for today’s latency budgets, and most teams aren’t instrumented to see what that costs them. Call it JVM drag, and it shows up in three ways: conservative JIT compilation that leaves 20-30% of performance ontapped (Throughput Drag), GC pauses that stall retrieval mid-request (Pause Drag), and every deploy or auto-scale evetn starting cold (Warm-up Drag).
None of this shows up in your GPU utilization dashboard. All of it shows up in your cloud bill.
