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AI cloud readiness check

Eight questions on running an AI workload on AWS or Azure. See your readiness by area and what to set up first.

Accounts and ownership

Do you have an AWS or Azure account set up for production work?

Who owns the account, the billing and the admin access?

Security and data

How do people and systems sign in to the cloud?

Do you know which regions your data may be stored and processed in?

Connectivity and models

Can the cloud reach the systems the AI needs?

Are the models you plan to use approved and available to you?

Operations and cost

Who will watch the workload once it is live?

How will you control cloud and model usage costs?

0/8answered

How the scoring works

  • This check covers the cloud foundations for one AI workload on AWS or Azure: accounts, security, connectivity, models and day-to-day operation.
  • Each answer scores from 0 to 3. An area score is its two answers as a share of the maximum, and the overall score weights every question equally.
  • 70% and above suggests you are ready for a first workload; below 40% suggests settling accounts, access and ownership first.
  • The next steps come from your three weakest answers. When answers tie, account ownership comes first, because every other control depends on it.
  • The scores are planning estimates from your own answers, not a review of your cloud accounts.
Read the full guide

Go deeper

Guide · 15 min read

Running AI in production on AWS and AzureRunning AI in production on AWS or Azure means giving a model the same controls as any business system: identity, secrets, logging, cost limits, fallbacks and a named owner for every operating task. Managed services such as Amazon Bedrock and Azure OpenAI in Microsoft Foundry host the models, but the architecture, data boundaries and day-to-day operations remain your decisions to make and assign.Read

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