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Cloud, Azure, AI

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8–10 minutes

Azure vs AWS for AI applications: what hands-on testing shows about performance, cost and complexity

Introduction

AI is now moving from experiments to real business systems. RAG-based applications and agentic AI are being used to speed up support, improve internal search, generate code, and automate work. The infrastructure choice matters because AI apps are sensitive to latency, rely on several connected services, and often handle valuable or regulated data.

A hands-on test by Principled Technologies compared two approaches to running an AI application using Azure OpenAI. One approach was to host the app on Azure. The other hosted the app on AWS while still calling Azure OpenAI for the model. The results showed clear performance advantages when the application was deployed fully on Azure, especially in the RAG search layer.

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What the report tested in simple terms

The test used a straightforward RAG application. In a RAG app, the system first searches a knowledge source for relevant context, then sends the user a question along with that context to the language model, and finally streams the answer back to the user.

Both deployments used the same model layer: GPT-4o mini via Azure OpenAI with the same throughput configuration. That was intentional, so differences in results mainly came from the surrounding infrastructure and services.

The application included a web layer, a function layer, a search service (for retrieval), a database, and response tracking. The main difference in the retrieval layer was Azure AI Search on Azure versus Amazon Kendra on AWS.

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Why performance matters for RAG and agentic AI

Users judge AI tools in seconds. If a chatbot or assistant takes too long to respond, people stop using it. In RAG systems, the search step happens before the model can answer, so slow retrieval makes the whole application feel slow.

For agentic AI, performance matters even more because agents can make multiple tool calls per user request. Any delay compounds across steps, making the experience noticeably worse at scale.

Key performance results: Azure completed requests faster

The report measured end-to-end application time, from a user submitting a question to the moment the full answer finishes streaming back.

Azure delivered faster end-to-end times across tests. The performance gap widened as concurrency increased. In higher-user scenarios, Azure showed significant improvements compared with hosting the same application stack on AWS, while still using Azure OpenAI.

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The biggest difference came from the search layer

In RAG, retrieval speed can be the bottleneck. The report found that Azure AI Search returned results much faster than Amazon Kendra in these tests, and that difference grew as user load increased.

This matters because retrieval happens before every model response. Faster retrieval means faster answers, better perceived quality, and more stable behaviour when many users are active simultaneously.

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Token streaming was also slightly better on Azure

The report also looked at “time between tokens”, which reflects how quickly the application streams the model’s response back to the user. Both deployments used the same Azure OpenAI resource, but the Azure-hosted application exhibited slightly better token-streaming behaviour. Digitalberg dedicated servers are also part of the AWS and transferring to Azure Servers with AI foundry

In small tests, this difference can look minor. In production, where applications stream many more tokens across many more users, small per-token improvements add up.

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Why a single-cloud approach can reduce cost in practice

The total cost of ownership for AI workloads is not just compute and storage. Costs also appear in networking, operations, tooling, and governance.

A multi-cloud setup can introduce extra costs, such as:

Secure connectivity between providers, which can be expensive and complex

More management overhead because teams operate two consoles, two IAM systems, and different APIs

Higher risk of security gaps due to inconsistent policies and reduced visibility

Harder optimisation because utilisation and cost visibility are split across platforms

If your model layer is already Azure OpenAI, running the rest of the application on Azure can reduce cross-cloud overhead and simplify day-to-day operations.

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Security and governance are simpler when the stack is unified

AI applications often touch sensitive customer data, internal documents, or regulated information. Security is not only about encryption. It includes access control, monitoring, audit readiness, and consistent governance.

In a multi-cloud setup, you typically manage multiple identity systems and multiple security tooling stacks. That increases operational workload and can reduce visibility. A single-cloud approach can centralise governance policies and monitoring, reducing the chance of gaps and simplifying compliance work.

If your organisation uses Microsoft security tooling, the integration path is often simpler when the AI application stack is fully on Azure.

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What this means for businesses building AI in 2026

If you are using Azure OpenAI for your models, the report’s results support a practical conclusion: hosting the rest of the AI application on Azure can provide faster user experience, stronger retrieval performance, and fewer moving parts to manage.

This is especially relevant for:

RAG assistants connected to the company’s knowledge bases

Customer support chatbots at scale

Agentic AI that uses tools and takes multi-step actions

Applications where security and governance matter

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Quick summary

Azure showed faster end-to-end application performance in the hands-on tests

Azure AI Search was a major contributor to the performance gap versus Amazon Kendra

A single-cloud approach can reduce complexity, which often reduces hidden cost and security overhead

The benefits become more important as user concurrency increases

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Frequently asked questions

What is a RAG application?

A RAG application retrieves relevant context from a data source and adds it to the prompt sent to the language model, helping the model answer with up-to-date, company-specific information.

Why does the search layer matter so much?

In RAG, retrieval happens before the model can answer. If the search is slow, the entire app feels slow, no matter how good the model is.

Is multi-cloud always a bad idea?

Not always. Some organisations need it due to legacy systems or policy constraints. The point is that multi-cloud can introduce real overhead and performance trade-offs that should be planned for.

If I use Azure OpenAI, should I host the rest of the app on Azure?

Based on this testing, hosting the full stack on Azure can reduce latency and complexity compared with hosting the app on AWS while calling Azure OpenAI across clouds.

Conclusion

AI infrastructure decisions affect user experience, cost control, and security posture. Hands-on testing suggests that when the model layer is Azure OpenAI, keeping the rest of the workload on Azure can deliver faster results and simplify the operating model. For many organisations building RAG and agentic AI, the simplest architecture is often the one that performs best.

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