Real-Time AI: Processing 1M Predictions per Second

Real-time AI at million-QPS scale requires disciplined architecture. Use event streams (Kafka, Pulsar) with partitioning strategies that align to your workload, and keep payloads compact. Stateless services backed by fast feature caches reduce dependency latency and simplify scaling decisions.

Model serving stacks should support dynamic batching and GPU-aware schedulers to maximize utilization. For ultra-low latency paths, consider CPU-optimized models with SIMD or server-class accelerators. Co-locate models and feature stores, and minimize cross-AZ chatter to cut network hops.

Backpressure and graceful degradation keep systems stable under load spikes. Implement circuit breakers, retry policies with jitter, and load-shed non-critical traffic before core paths suffer. Health probes and autoscaling tied to real metrics—not just CPU—prevent thrash.

End-to-end observability is non-negotiable. Trace requests through ingestion, feature retrieval, model inference, and downstream sinks. Monitor queue lag, p95/p99 latency, and error budgets. Simulate disaster scenarios regularly so recovery plans are battle-tested when it counts.