How I cut enterprise LLM costs by 40% with Kubernetes and LiteLLM gateways

Most enterprises don't have an AI cost problem — they have an AI governance problem. When every team calls OpenAI or Anthropic directly, there is no single place to cache, rate-limit, or budget. Spend compounds silently until finance asks why the bill looks like a phone number.

The fix is an OpenAI-compatible gateway (I use LiteLLM) running on Kubernetes as the single egress point for all AI traffic. Every request flows through it, which means every request can be measured, cached, routed, and capped.

Three levers move the number the most: semantic caching for repeated prompts, automatic fallback routing to cheaper models when quality thresholds allow, and hard per-team token budgets enforced at the proxy. Together these routinely take 30–40% off inference spend without changing a line of application code.

The bonus is observability. Once traffic is centralized, Prometheus and Grafana give you latency, error rates, and cost-per-team in real time — the reporting layer FinOps has been asking for.