Enterprise GenAI: LLM Deployment on AWS

Abstract

Generative AI and Large Language Models (LLMs) have transitioned from experimental prototypes to critical enterprise assets, requiring robust, scalable, and secure deployment frameworks. This paper presents a comprehensive survey of LLM deployment strategies on Amazon Web Services (AWS), focusing on the shift from consumer-grade to enterprise-ready architectures. We analyze the AWS Generative AI stack, specifically comparing managed serverless approaches via Amazon Bedrock with customizable infrastructure through Amazon SageMaker. The survey highlights key architectural patterns, including Retrieval-Augmented Generation (RAG) for grounding models in proprietary data and multi-agent systems for complex task orchestration. Furthermore, we examine the critical role of LLMOps in managing the model lifecycle, ensuring security through Guardrails, and optimizing costs via quantization and provisioned throughput. By synthesizing real-world case studies and performance metrics, this paper provides a scalable roadmap for organizations to implement production-grade Generative AI solutions that maintain data sovereignty and operational excellence.

Introduction

The text explores the rapid adoption of Enterprise Generative AI, particularly Large Language Models (LLMs), and highlights the significant challenges organizations face in deploying them securely and efficiently. While LLMs enhance automation, decision-making, and operational efficiency, their improper deployment can lead to risks such as data leakage, compliance violations, hallucinations, and high costs. These issues largely stem from the lack of standardized, secure, and auditable deployment frameworks.

Traditional deployment approaches (e.g., standalone APIs or isolated systems) are insufficient due to limited control, poor scalability, vendor lock-in, and lack of monitoring. Cloud-native platforms—especially AWS—are presented as a solution, offering scalability, flexibility, and managed services for better governance and cost efficiency.

The background section explains:

• LLMs as powerful transformer-based systems capable of various language tasks but prone to risks if not properly managed.

• The enterprise AI ecosystem, which includes multiple interconnected components but often suffers from fragmentation and lack of visibility.

• Cloud infrastructure (AWS) as a scalable and reliable foundation for LLM deployment.

• Managed AI services that simplify deployment through automation, monitoring, and CI/CD integration.

The literature survey shows that:

• Cloud and hybrid frameworks improve scalability and flexibility but often lack cost analysis and performance validation.

• Techniques like Retrieval-Augmented Generation (RAG) improve accuracy and reduce hallucination.

• Governance features such as logging, access control, and versioning are essential but incomplete without cost and privacy considerations.

• Emerging solutions integrate security (encryption, role-based access), DevOps practices, and monitoring for better reliability.

• Key gaps remain in scalability, cost optimization, interoperability, and privacy-preserving techniques.

The major challenges identified include:

• Scalability and throughput issues due to high computational demands.

• High implementation costs from infrastructure and continuous operations.

• Data privacy vs. transparency conflicts, especially when handling sensitive enterprise data in cloud environments.

Conclusion

The rapid advancement of Generative Artificial Intelligence has transformed enterprise digital strategies, yet the deployment of Large Language Models at scale presents complex technical and operational challenges. This paper introduced the EnterpriseGenAI Framework, a cloud-based deployment approach for enterprise LLM implementation on AWS aimed at improving scalability, governance, cost efficiency, and security. The proposed framework adopts a hybrid architecture that combines managed foundation model services with enterprise-controlled infrastructure and monitoring mechanisms. These advancements will contribute to building robust, transparent, and future-proof enterprise GenAI ecosystems powered by AWS.

References

[1] T. Brown et al., “Language models are few-shot learners,” in Proc. Advances in Neural Information Processing Systems (NeurIPS), 2020, pp. 1877–1901. [2] A. Vaswani et al., “Attention is all you need,” in Proc. Advances in Neural Information Processing Systems (NeurIPS), 2017, pp. 5998–6008. [3] W. Fedus, B. Zoph, and N. Shazeer, “Switch transformers: Scaling to trillion parameter models with simple and efficient sparsity,” Journal of Machine Learning Research, vol. 23, no. 120, pp. 1–39, 2022. [4] Amazon Web Services, “Amazon SageMaker: Developer guide,” AWS Documentation, 2023. [5] Amazon Web Services, “Amazon Bedrock: User guide,” AWS Documentation, 2023. [6] J. Dean et al., “Large scale distributed deep networks,” in Proc. Advances in Neural Information Processing Systems (NeurIPS), 2012, pp. 1223–1231. [7] S. Rajbhandari et al., “ZeRO: Memory optimizations toward training trillion parameter models,” in Proc. International Conference for High Performance Computing, Networking, Storage and Analysis (SC), 2020, pp. 1–16.S. R. Patel, A. Verma, and P. K. Singh, “A blockchain-based drug supply management system using enhanced learning scheme,” International Journal of Advanced Computer Science and Applications, vol. 14, no. 2, pp. 245–252, 2023. [8] P. Lewis et al., “Retrieval-augmented generation for knowledge-intensive NLP tasks,” in Proc. Advances in Neural Information Processing Systems (NeurIPS), 2020, pp. 9459–9474. [9] S. Bubeck et al., “Sparks of artificial general intelligence: Early experiments with GPT-4,” arXiv preprint arXiv:2303.12712, 2023. [10] M. Mao et al., “Cost-efficient resource provisioning for cloud-based machine learning workloads,” IEEE Transactions on Cloud Computing, vol. 9, no. 2, pp. 456–469, 2021.

Copyright

Copyright © 2026 Sufiyan Shaikh, Arbaaz Kazi, Aryan Kadam, Sonali Bansode, Prof. Nikhil R. Zinzurke. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.