LLMOps Playbook: How to Keep Compound AI Systems Fast, Safe, and Affordable

Most generative AI pilots fail not due to poor models but because the surrounding infrastructure is unprepared for production traffic. Modern AI systems are compound, combining embedders, retrievers, re-rankers, validators, and multiple LLMs, making operational complexity a serious challenge. Key strategies include deploying a model gateway for smart routing, implementing semantic caching to reduce redundant API calls by up to 60%, and instrumenting every pipeline stage with OpenTelemetry-compatible traces for full observability. Automated output validation using lightweight judge models can catch hallucinations before they reach end users, while independent autoscaling of GPU inference and vector search layers prevents cascading failures. Together, these LLMOps controls can significantly cut token costs and latency while maintaining safety in high-traffic deployments.