AI Inference Just Plays By Different Rules
Silk, Monday, May 4th, 2026
AI inference workloads demand fundamentally different data infrastructure design than traditional human-speed applications.
AI agents and agentic reasoning loops create unprecedented concurrency and data access patterns that stress existing cloud infrastructure in ways traditional applications never do. Unlike human-speed applications that can be cached and averaged out, AI inference exhibits OLTP++ characteristics with sudden extreme I/O spikes that overwhelm standard AWS EBS storage limits.
Organizations must rethink data architecture - including vector database design, RAG implementations, and storage abstraction layers - and measure success through tail latency metrics like p99/p999 rather than averages.
The article illustrates the problem with a case study of "FinRetail," whose successful AI shopping assistant caused a production outage by exhausting EBS burst credits within 15 minutes due to the thousands of concurrent agent queries it generated.