Adaptive Workflows with Kafka Streams and AI Models: A Comparative Study with EventBridge

Event-driven systems are foundational in modern applications that demand real-time processing and dynamic workflows. Kafka Streams, a distributed event streaming platform, and AWS EventBridge, a serverless event bus, are two prominent solutions for managing such workflows. The integration of artificial intelligence (AI) models into these systems enables dynamic decision-making and adaptive workflows. This paper explores the technical aspects, performance, and scalability of Kafka Streams and EventBridge when integrated with AI models for dynamic event routing. Through comparative analysis, we evaluate their suitability for real-time adaptive workflows, focusing on latency, throughput, fault tolerance, and cost-efficiency. Experimental results highlight the trade-offs between these platforms and provide insights into choosing the right architecture for specific use cases.