AWS Bedrock vs Azure OpenAI: How to Choose a Cloud AI Platform

• October 19, 2026
• Nadeem Shaikh
• no comments

Choose Amazon Bedrock if your systems and data already live on AWS and you want one API for models from several providers. Choose Azure OpenAI if you run on Microsoft Azure and Microsoft 365, or you specifically want OpenAI’s GPT models with Microsoft’s security and identity tools. For most companies, the cloud you already use matters more than any single model, because that is where your data, access rules and billing already sit.

Both services let a business use large language models through a managed cloud service instead of a consumer chatbot. Both keep your data inside your cloud account’s controls, and both can power assistants, document search and automation. The differences are in which models you get, how each fits your existing stack, and how your team will build and run things day to day. This guide walks through those differences in plain words.

What Amazon Bedrock is

Amazon Bedrock is AWS’s managed service for foundation models. Instead of hosting models yourself, you call them through one AWS API. Bedrock offers models from several companies, including Anthropic’s Claude, Meta’s Llama, Mistral and Amazon’s own models, so you can compare and switch models without changing clouds.

Around the models, Bedrock adds building blocks such as knowledge bases for answering from your own documents, agents that can call your systems, and guardrails for filtering what goes in and out. Access is controlled with the same AWS identity and networking tools your team may already use.

What Azure OpenAI is

Azure OpenAI is Microsoft’s service for running OpenAI’s models, such as the GPT family, inside Azure. It is now part of Microsoft’s broader AI Foundry platform, which also lists models from other providers. You get the OpenAI models many people already know from ChatGPT, but under your company’s Azure account, with Microsoft Entra ID for sign-in and access, private networking options and Azure’s content filters.

For document search, teams usually pair it with Azure AI Search. If your company runs on Microsoft 365, Teams and SharePoint, Azure tends to connect to those tools with less custom work.

AWS Bedrock vs Azure OpenAI: the main differences

• Model choice. Bedrock is built around many model providers behind one API. Azure OpenAI centers on OpenAI’s models, with other models available through the wider Foundry catalog. Both catalogs change often, so check the current list for the exact models and regions you need.
• Fit with your stack. If your apps, databases and files are on AWS, Bedrock keeps everything close. If you run on Azure and Microsoft 365, Azure OpenAI fits more naturally.
• Identity and security. Bedrock uses AWS IAM and VPC networking. Azure OpenAI uses Entra ID and Azure networking. Neither is weaker; the better one is the one your team already manages well.
• Building blocks for RAG and agents. Both offer managed ways to ground answers in your documents and to build agents that take actions. The tools differ in setup and detail, so a short prototype on your own data tells you more than a feature list.
• Pricing and capacity. Both mainly charge by usage (tokens in and out), with options for reserved capacity at higher volumes. Rates vary by model and region and change over time, so price your real workload on each provider’s current pricing page rather than relying on old comparisons.
• Data handling. Both providers state that your prompts and outputs are not used to train the underlying models. Read the current terms for your region and compliance needs before you send sensitive data.

When Amazon Bedrock is the better fit

• Your main systems and data are already on AWS
• You want to test several model families and keep the option to switch
• You plan to build RAG over your company documents stored in AWS
• Your team already knows IAM, VPCs and AWS cost tools

When Azure OpenAI is the better fit

• Your company runs on Azure, Microsoft 365, Teams or SharePoint
• You specifically want OpenAI’s models with enterprise controls
• Your security team manages access through Microsoft Entra ID
• You already have an Azure agreement and want AI spend on the same bill

Can a company use both?

Yes, and some do. A common pattern is to put a thin layer in your own code between your app and the model provider, so the app does not care which cloud answers. That lets you route each task to the model that does it best, keep a backup if one service has an outage, and compare cost over time. The trade-off is extra setup: two sets of access rules, two bills and two sets of logs to watch. Our post on LLMOps covers how to keep that monitoring under control.

How to decide in four steps

1. Start from your current cloud. If one cloud already holds most of your data and apps, begin there unless a model you need is missing.
2. List the models you need. Confirm they are available on each platform in the regions where your data must stay.
3. Run the same test on both. Use real examples from your business, with known good answers, and compare accuracy, speed and cost per request.
4. Check security and compliance. Confirm identity, networking, logging and data terms meet your requirements before anything goes to production.

If your use case is narrow and high-volume, also test a smaller model. Our guide to small language models explains when a smaller model is the better choice.

How GrtLabs can help

GrtLabs is a Houston AI lab that works in whichever cloud you already use, or helps you choose. Through our AWS and Azure AI services, we help with architecture and service selection, model hosting, RAG over your documents, and moving a working prototype into a secure, monitored production setup inside your own cloud account, with access controls you own and budgets that keep cost in check. If you are still deciding where AI fits, start with AI Discovery.

Talk to GrtLabs about choosing your cloud AI platform.

FAQ

What is the difference between AWS Bedrock and Azure OpenAI?

Amazon Bedrock is AWS’s managed service that gives one API to foundation models from several providers. Azure OpenAI is Microsoft’s service for running OpenAI’s GPT models inside Azure. Bedrock is built around model choice on AWS, and Azure OpenAI is built around OpenAI models with Microsoft’s security and identity tools.

Which is better for a business, Bedrock or Azure OpenAI?

Neither is better for everyone. The best choice is usually the cloud that already holds your data and apps, as long as it offers the models you need. Test both on your own examples if you are unsure.

Can a company use both AWS and Azure for AI?

Yes. Many teams add a small layer in their own code so an app can call either provider. This lets them route tasks to the best model and keep a backup, at the cost of managing two sets of access rules, bills and logs.

Do AWS Bedrock and Azure OpenAI use my data to train models?

Both providers state that customer prompts and outputs are not used to train the underlying models. Check each provider’s current data terms for your region and compliance needs before sending sensitive data.