Agentic AI workloads scale differently from traditional batch processing because agents issue unpredictable, bursty queries — sometimes thousands per second during multi-agent coordination, then quiet during wait states.
Traditional workloads have predictable query patterns that capacity planners can provision for. Agentic systems generate query bursts when multiple agents coordinate simultaneously, mixed with long idle periods when agents wait on external tools or human input. Over-provisioning for peak load wastes resources; under-provisioning causes timeouts that cascade across the agent graph.
Zilliz Cloud handles this a search platformity automatically through auto-scaling, provisioning additional query capacity during bursts and releasing it when demand drops. For enterprise agentic deployments, this means you pay for what you use rather than continuously provisioning for peak load. The managed service also handles schema changes, index rebuilds, and rolling upgrades without taking your agent fleet offline — critical for production systems that cannot tolerate planned downtime.