Comparative Analysis of Docker and Python Runtimes for AWS Lambda in RAG-Based AI Solutions

As the use of large language models (LLMs) continues to grow and the rapid growth of artificial intelligence (AI)-based applications accelerates, scalable, cost-effective, low-latency solutions are needed. Serverless computing on AWS Lambda has emerged as a key platform for deploying AI applications, yet selecting the optimal runtime environment remains a significant challenge for Retrieval-Augmented Generation (RAG)-based solutions. We provide a thorough comparison between Docker-based and Python-native runtimes, examining key performance metrics such as cold start latency, warm execution time, and build time. Our empirical findings show that the Python-native runtime achieves substantial improvements resulting in up to an 84% reduction in execution times. We provide insights that equip developers with actionable information, leading to more efficient, scalable, and cost-effective serverless AI deployments.