Principled Technologies (PT) has released a study demonstrating the advantages of a single-cloud approach using Microsoft Azure for AI applications, particularly in retrieval-augmented generation (RAG) workflows. Organizations leveraging Azure OpenAI with Azure AI Search achieved superior performance and cost efficiency compared to multi-cloud configurations involving AWS services like Amazon Kendra, highlighting the benefits of centralized deployment in streamlined AI development.
In an era where AI adoption demands agility and efficiency, PT's study underscores the pitfalls of piecemeal multi-cloud strategies for organizations blending AWS infrastructure with Azure OpenAI. By hosting the entire RAG AI application—encompassing data ingestion, search, and generation—on Azure, the setup streamlined operations, leveraging native integrations for robust security and development tools. This approach not only centralizes workflows but also harnesses OpenAI models' full potential within Azure AI Foundry, reducing silos that plague hybrid environments and enabling scalable AI in SaaS and IT ecosystems.
PT constructed a baseline RAG application using GPT-4o mini across both platforms, with AWS relying on Kendra for search and Azure utilizing AI Search. The results were compelling: Azure's end-to-end execution time plummeted by 59.7%, reflecting faster token processing and reduced latency in multi-turn interactions. Azure AI Search further excelled, slashing query times by up to 88.8% versus Kendra, thanks to optimized indexing and retrieval tuned for OpenAI compatibility. These metrics translate to real-world gains, such as quicker insights for enterprise users in data-heavy applications, without compromising accuracy or compliance.
Beyond speed, the single-cloud model on Azure yielded tangible cost savings through simplified architecture, avoiding the overhead of cross-provider data transfers and API management. Security benefits were equally pronounced, with Azure's unified governance—encompassing identity, encryption, and auditing—outpacing the fragmented controls in multi-cloud setups. For agentic AI extending RAG capabilities, these efficiencies amplify, minimizing risks in regulated sectors while accelerating time-to-value in dynamic IT landscapes.
According to the report, “…using Azure to get the latest and most popular OpenAI models from Azure OpenAI in Azure AI Foundry, but hosting your AI workloads on Amazon Web Services (AWS™), might cost you in terms of both performance and budget. Switching to a single cloud approach with Azure for your next OpenAI RAG LLM app can boost performance while saving costs and centralizing key parts of the development workflow.”
The report concludes, “In fact, running our app on Azure reduced end-to-end execution time by 59.7 percent compared to an AWS deployment. Also, in our tests, Azure provided a faster search service layer for our OpenAI RAG LLM, reducing Azure AI Search time by up to 88.8 percent compared to Amazon Kendra. In application configurations such as ours, the choice is clear: building and hosting your AI app on Azure the better strategy. It reduces complexity—which optimizes performance, saves money, and increases security compared to selecting a multi-cloud deployment. While we used a RAG-based AI app as an example, other more complex agentic AI applications could see similar benefits of a single-cloud strategy.”
PT's findings advocate for Azure as the strategic choice for OpenAI-centric AI, empowering organizations to innovate confidently amid rising multi-cloud complexities.
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