AI Inference Operates by an Entirely Different Set of Rules

AI agent inference behaves like OLTP++. Instead of a human slowly clicking buttons, a ReAct loop issues multiple queries in parallel at millisecond intervals, so the problem isn't simple compute but exploding concurrency and unpredictable read patterns.

The bottleneck in RAG and vector search also lies in the data path, not the prompt. When metadata filtering is added to HNSW and IVFFlat, index scans, embedding lookups, and scatter-gather delays overlap, and the piece emphasizes that sub-millisecond reads on hot vectors and predictable throughput at hundreds of millions of rows are required. It also points out that scaling up RDS read replicas merely shifts the bottleneck elsewhere.

AWS EBS struggles to withstand sudden AI traffic because of burst credits and per-volume IOPS/throughput caps. That's why p99/p999 tail latency, rather than average latency, needs to be examined under real mixed workloads, and the following situations must be verified together.

• Concurrent OLTP + inference + maintenance jobs: You need to check how vector search behaves while large batch jobs or vacuum operations are running.
• AZ-to-AZ variability: You need to see how much latency increases during failover or batch changes.
• Autoscaling events and cache warm-ups: How quickly the cache warms up when new EC2 nodes are added matters.

In the example given, FinRetail exhausted its EBS burst credits in just 15 minutes through 40-step reasoning and hundreds of vector searches, with latency spiking from 0.8ms to 120ms, causing site outages and stale inventory recommendations. The article argues that software-defined storage that decouples performance from capacity is needed, and explains that Silk claims 20 GiB/s throughput and stable tail latency through a distributed cache and symmetric active-active architecture.