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Generative AI Integration Patterns in Enterprise Microservices Ecosystems

Phanindra Gangina

Abstract

In the given paper, the authors provide a detailed architectural vision of how to integrate generative AI capabilities, especially the large language models (LLMs), into the enterprise microservices ecosystem, and how to overcome such significant issues as latency, cost optimization, security, and operational governance. The study presents new integration patterns across multiple architectural layers: (1) intelligent patterns of API gateway with semantic routing, batching of requests, and adaptive patterns of timeouts which reduce the cost of an LLM invocation by 45 percent by caching responses promptly and streaming responses; (2) service mesh integration patterns by using sidecar proxies to support distributed tracing, circuit breaking, and A/B testing of competing AI models with real-time performance measurements; (3) event-driven orchestration patterns with asynchronous message queues and event sourcing to decouple the The architecture includes security limits in a fine-grained access control, prompt injection detection, content filtering layer, and data residency compliance to controlled industries. There are also advanced optimization methods, such as quantization of model, resource pooling in the GPUs, caching inference in Redis/Memcached and smart routing of requests between cloud and on-premises AI services due to classification of sensitivity of data. The framework uses the observability patterns of distributed tracing (OpenTelemetry), monitoring the usage of tokens, cost per microservice, and AI-friendly SLO tracking. The performance standards can be shown to be horizontally scalable to 100,000 or more requests with predictable cost scaling to concurrent AI requests by using spot instances and reservation capacity planning. The suggested patterns allow enterprises to implement generative AI in microservices architectures offering an enterprise-grade reliability, security posture, and control over costs, and give practitioners production-ready reference implementations of the integration of LLM, lifecycle management of models, and design of AI-native services.

Article Information

Journal

International Journal of Science, Research and Technology

Volume (Issue)

Vol. 7 No. 6 (2024): International Journal of Science, Research and Technology (IJSRAT)

DOI

https://doi.org/10.15662/IJSRAT.2024.0706004

Pages

13153-13165

Published

November 10, 2024
Copyright

Open Access

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

Phanindra Gangina (%2024). Generative AI Integration Patterns in Enterprise Microservices Ecosystems. International Journal of Science, Research and Technology , Vol. 7 No. 6 (2024): International Journal of Science, Research and Technology (IJSRAT) , pp. 13153-13165. https://doi.org/10.15662/IJSRAT.2024.0706004

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