DragonflyDB CEO: Real-time AI stacks need a low-latency reset
A DragonflyDB executive argues today’s real-time AI stacks need a low-latency data layer and stricter tail-latency discipline to serve interactive workloads at scale. The piece contends that infrastructure built around batch or async assumptions struggles when inference paths demand predictable p99/p999 latency and high concurrency, calling for memory-centric state management and better end-to-end observability ([The New Stack](https://thenewstack.io/scaling-real-time-ai-workloads/)). It emphasizes simplifying coordination across services, pushing state closer to compute, and implementing robust backpressure to avoid queue blowups under bursty traffic. For teams scaling RAG and streaming inference, the guidance is to prioritize tail-latency budgets, data locality, and a leaner messaging topology over raw throughput, backed by instrumentation that traces latency and token usage across the request path ([The New Stack](https://thenewstack.io/scaling-real-time-ai-workloads/)).