Event-Driven Architecture for AI

For non-real-time AI workloads, event-driven architecture beats request-response on throughput, resilience, batching efficiency. Kafka / Pub/Sub / SQS / RabbitMQ trigger AI inference; results published to downstream topics; consumers process. Standard distributed-systems patterns applied to AI.

Patterns

• Pub/Sub fan-out: one event → multiple AI processors (translate, classify, summarise) in parallel
• Worker pool: shared queue; N workers pull and process; auto-scale on queue depth
• Batching: workers accumulate events for ~1-5 seconds; batch-call AI for efficiency
• Pipeline stages: chain queues for multi-step AI workflows (extract → analyse → categorise)
• Dead-letter queue: failed events for manual review

Backpressure

• Rate-limit at queue layer to prevent saturating AI tier
• Auto-scale workers based on queue depth
• Cap concurrent AI calls per worker
• Acknowledgement after success only (idempotency on retry)
• Monitor lag; alert on growing backlog

Examples

• Document processing: doc upload → SQS → AI worker (OCR + extraction + classification) → result topic
• Customer support: ticket created → pubsub → AI suggests resolution → updates ticket
• Content moderation: user post → queue → AI classifies safety → gates publishing
• Embedding ingest: source change → CDC event → embedding worker → updates Qdrant

Verdict

For async / batch / pipeline AI workloads, event-driven architecture is the right shape. Standard distributed-systems patterns apply with AI-specific extensions (batching, idempotency, structured-output validation). vLLM's continuous batching benefits compound with worker-level batching for excellent throughput.