This article was originally published on AI Study Room . For the full version with working code examples and related articles, visit the original post. AI Model Deployment: Strategies for Production LLM Serving Deploying AI models to production requires infrastructure for serving, scaling, and monitoring. LLM deployment differs from traditional ML deployment due to high compute requirements, variable latency, and unique cost models. Serving Options Managed APIs (OpenAI, Anthropic, Google) provide the simplest deployment. No infrastructure management. Pay per token. Best for most applications. Limited customization and data control. Self-hosted (vLLM, TGI, Triton) provide full control. Lower per-token cost at scale. Data stays within your infrastructure. Requires GPU infrastructure and operational expertise. Hybrid: use managed APIs for production and self-hosted for high-volume or sensitive workloads. This balances cost, latency, and control. Infrastructure LLM serving requires GPU instances (A100, H100).…