Problem

Adding large language model capabilities to an established multi-tenant SaaS application fundamentally alters the operational profile of your Kubernetes cluster. Standard SaaS architectures are optimized for predictable, low-latency CRUD operations with short execution windows. AI workflows, by contrast, introduce high memory consumption, long-running connections for streaming tokens, unpredictable CPU and GPU utilization, and severe cost amplification if scaling policies are misconfigured.

A naive implementation routes AI prompts directly through the primary API gateway to synchronous endpoint workers. Under load, these requests exhaust connection pools, trigger cascading HTTP timeouts, and starve deterministic tenant operations of compute resources.…