Ray Serve is a flexible and efficient compute system for online inference that addresses common challenges in AI application deployment, such as model microservices, the rise of large language models, and increasing hardware costs. It provides a python native framework to express complex applications with multiple models in a single Python program, simplifying iteration and deployment. Ray Serve has introduced optimizations like the RayLLM subproject for LLMs, model multiplexing to maximize hardware usage, and spot instance support to reduce costs. The system offers observability features like the Ray dashboard, cloudWatch integration, and Grafana dashboards for metrics and analytics, as well as auto-scaling capabilities to dynamically scale resources based on load. With its focus on production readiness, stability, and cost-effectiveness, Ray Serve empowers organizations to deliver AI solutions that are adaptable to changing trends and can harness the potential of LLMs efficiently.