Why real-time AI at scale is so hard
The New Stack 1 month ago 40
Real-time AI deployments struggle in production when data pipeline and infrastructure bottlenecks—not model logic—drive issues like latency spikes and accuracy drops. Tim’s financial trading app reached about 740K operations per second when P99 latency rose to 3 seconds. To avoid the resulting doom loop, teams should monitor freshness and index health, separate workloads and vector indexing, and plan for retraining with controlled rollouts.
