AWS or Azure for AI: decision checklist

Identity and existing estate

Do employees already sign in with Microsoft Entra ID, and would a new AI application need to use the same identities and groups?
How much of your core software runs on Microsoft 365, Dynamics 365 or other Microsoft products today?
Do you already run production workloads in AWS or Azure, and who manages those accounts?
Where do the systems the AI workload must connect to run: on premises, in one of these clouds or in SaaS applications?
Would adding a second cloud provider create security, networking or governance work your team is not ready for?

Data and location

Where does the data the AI workload needs live today, and what would it take in time and transfer fees to move or copy it?
Do contracts, regulations or customer commitments require data to stay in a specific country or region?
Are the AI models and services you need available in the regions where your data must stay?
Is your reporting and analytics data already in one provider, for example in Amazon Redshift or Microsoft Fabric?
What do each provider's terms say about storing your prompts and outputs and using them to train models?

Models and services

Which models does the workload need, and are they offered through Amazon Bedrock, Microsoft Foundry or both?
What quotas and rate limits apply to those models on each platform, and how quickly can they be raised?
Does the workload need managed services beyond models, such as search, document extraction or speech, and how well do they fit on each platform?
Can the application be designed to switch models or providers later, so the choice is not permanent?
Which platform offers the evaluation, monitoring and logging tools your team will use to run the workload?

Skills and support

Which platform does your IT team, or your managed service provider, already know well?
Who will monitor, patch and support the workload after launch, and which cloud are they equipped for?
What support plan do you have, or would you need, with each provider?
Can you hire or contract specialists for the platform in your region and time zone?
Do your existing monitoring and security tools support both clouds equally well?

Commercials and contracts

Do you have a Microsoft Enterprise Agreement or a committed-spend agreement with AWS that new usage would count toward?
Are there cloud credits, discounts or marketplace purchasing options that change the comparison?
What will the workload cost to run at expected volumes, including model usage, storage, networking and data transfer?
Who in finance will own the cloud bill, and how will costs be tracked by workload?
What would it take to move away later, including data egress fees and rework?
Do procurement, security review or vendor approval processes already favor one provider?

If the answers point in different directions, a discovery call with Kastling can help you weigh them against the workload you have in mind. Kastling's infrastructure work covers both AWS and Azure, so the conversation starts from your constraints rather than a preferred platform.