LLM Deployment Pipeline Explained Step by Step

Deploying a large language model (LLM) into a production-ready service involves a complex process that includes infrastructure management, scaling, and monitoring to ensure reliability and cost-effectiveness. The deployment is structured around five key layers: containerization, infrastructure and GPU allocation, the API and serving layer, autoscaling, and monitoring. Each layer is crucial for maintaining stable performance and predictable costs while handling live user requests. Organizations must decide between using cloud APIs, self-hosting on cloud GPUs, or on-premises solutions, balancing factors like speed, control, cost, and compliance. Effective GPU selection, inference frameworks, and API design are essential for optimizing performance, with metrics such as Time to First Token (TTFT) and Tokens Per Second (TPS) being critical for user experience. Scaling strategies should focus on latency and GPU utilization rather than traditional resource metrics, while monitoring requires a dual-layer approach to ensure both operational stability and semantic accuracy. Cost management is vital due to the unique nature of LLM expenses, which increase with usage volume, necessitating strategies like semantic caching, intelligent routing, and batch processing. An AI gateway, such as Portkey, can enhance deployment by providing intelligent routing, observability, semantic caching, cost controls, and automatic failover, enabling scalable and efficient LLM production systems.