Architectural Patterns for Integrating Large Language Models (LLMs) into Node.js Server Applications

Abstract

This article examines the evolution and systematization of architectural patterns for integrating large language models (LLMs) into server applications built on the Node.js platform, against the backdrop of the rapid diffusion of generative technologies in industrial software development and the expanding market for Retrieval-Augmented Generation (RAG) solutions. The relevance stems from the fact that by 2025, LLMs will have become an indispensable component of digital products, while server architectures must embed computational speech into existing infrastructures under constraints of token budgets, call costs, and network latency. The objective is to identify and analytically describe stable architectural patterns that enable efficient, predictable LLM integration in Node.js backends. Methodologically, the work combines systemic architectural analysis, modeling of interactions with LLM APIs, and content analysis of industrial practices, enabling the author to construct an engineering-economic efficiency model for each configuration. The article’s novelty lies in formulating the concept of a balanced LLM-integration architecture in which throughput, token price, and service-layer observability are treated as interdependent architectural variables. An evolutionary pathway is proposed for transitioning from monolithic model calls to microservice and serverless patterns, informed by market growth dynamics and the scaling of compute resources. The article will benefit researchers and engineers engaged in server-application architectural design, cloud-service developers, and AI-engineering specialists aiming for resilient and cost-balanced deployment of LLM technologies in production environments.