AI Architecture: Sync vs Async vs Event-Driven

Q: Should AI systems be synchronous or asynchronous?

Use synchronous for low-latency, simple queries (chatbots, classification). Use asynchronous for long-running AI tasks (document processing, batch analysis). Use event-driven for real-time AI pipelines triggered by data changes.

Q: What is event-driven AI architecture?

Event-driven AI processes data as events flow through the system. AI models are triggered by events (new document uploaded, user action, data change) rather than synchronous API calls. Ideal for real-time processing at scale.

Q: How do you handle AI request timeouts?

For long-running AI tasks: accept the request, return a job ID immediately, process asynchronously with a queue/worker pattern, and notify the client via webhook, WebSocket, or polling when complete.

Q: What message queue is best for AI workloads?

SQS for simple queue-based AI processing on AWS. Kafka for high-throughput event streaming with AI pipelines. RabbitMQ for complex routing. The choice depends on throughput, ordering guarantees, and processing patterns.