Integrating AI Into Apache Kafka Architectures: Patterns and Best Practices

A deep-dive into three repeatable patterns for integrating AI/LLM inference into Apache Kafka architectures: External RPC (calling managed APIs like OpenAI asynchronously), Embedded (running lightweight models like ONNX/TF Lite in-process), and Sidecar (co-located Python/GPU serving over Unix Domain Sockets). The post explains why naive synchronous LLM calls cause consumer group rebalance storms, and covers topic taxonomy design (raw-events, enriched-context, model-outputs, human-review), failure handling with dead-letter queues, exactly-once semantics via the Transactional Outbox pattern, cost control through upstream filtering, PII governance with Schema Registry, and observability requirements for each pattern. A decision matrix helps teams choose the right pattern based on latency, team maturity, model update frequency, and hardware needs.