The Hidden Reason Your AI Assistant Feels So Sluggish

AI workloads expose a mismatch between modern demands and how many data platforms were designed: batch-oriented systems struggle with high concurrency and sub‑second latency targets.

Agent-driven analytics query differently than humans—one natural‑language prompt often spawns many rapid, concurrent SQL queries, producing high‑concurrency, low‑latency, interactive traffic.

Traditional cloud data warehouses are optimized for throughput on infrequent, heavyweight queries, so placing AI analyst workloads on them can cause latency or cost problems.

Real‑time analytical databases are a natural fit for interactive workloads, and a common architecture is Postgres for transactions paired with a columnar OLAP store.

With MCP servers and open‑source agentic architectures exposing databases to agents and bots, production agentic analytics workflow becomes: natural language in, SQL out, answers back in seconds.