Integrating Large Language Model APIs into Enterprise Backend Services: Design Patterns and REST API Considerations
DOI:
https://doi.org/10.22399/ijcesen.5195Keywords:
Large Language Models, REST APIs, Enterprise Backend Architecture, Design Patterns, ObservabilityAbstract
Large language models (LLMs) have rapidly evolved from experimental research artifacts into production-grade services that are widely accessible through RESTful APIs. Although these interfaces share structural similarities with conventional web services, their underlying characteristics—such as probabilistic outputs, token-based cost models, and high, variable latency—introduce fundamentally new challenges for enterprise system design. This paper presents a structured taxonomy of integration patterns for incorporating LLM APIs into enterprise backend architectures. Adopting a design science research methodology, the study derives and formalizes five core patterns—Gateway, Prompt Template, Retry and Fallback, Streaming Response, and Context Window Management—each addressing a distinct set of engineering concerns specific to LLM-based systems. In addition, the paper provides a comparative analysis of LLM APIs and traditional REST services, highlighting key architectural divergences. It further proposes observability and data governance strategies tailored to the operational realities of LLM integration. The applicability of the proposed patterns is demonstrated through a case-based validation, and practical implementation guidance is provided within the context of Java and Spring Boot environments. Together, these contributions offer a comprehensive framework for designing scalable, reliable, and compliant enterprise systems that leverage LLM capabilities.
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