A comparison of running large language models (LLMs) on AWS reveals distinct cost and operational trade-offs between Bedrock, SageMaker Endpoints, and self-hosted solutions on EKS. For low-volume, high-quality tasks like customer-facing chat, Bedrock's pay-per-token model offers simplicity and speed. However, for high-volume, cost-sensitive tasks such as classification or embeddings, self-hosting LLMs using vLLM on EKS with GPU spot instances becomes more economical, with a crossover point estimated between 10,000-20,000 requests per day. A hybrid approach, leveraging Bedrock for complex reasoning and self-hosting for high-throughput tasks, is often recommended to balance cost and performance. AI
IMPACT Provides guidance on optimizing LLM inference costs and operational complexity across different AWS services.
RANK_REASON Article compares different LLM hosting options on a cloud platform, detailing cost and operational trade-offs.
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