AI & Cloud Infrastructure
The cloud, data and model foundations an AI workload needs to run in production.
About the serviceRunning AI in production on AWS and Azure
Running 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.
Start with the guideArticles and guides
Data security and governance for AI in the enterpriseAI data security and governance means knowing which data each AI workflow touches, approving the sources it may use, checking what your AI provider does with prompts and outputs, and making the AI respect the same permissions as the person using it. It also covers defenses against prompt injection, logging with sensible retention, human approval for consequential actions and a clear usage policy for staff. Frameworks such as the NIST AI RMF and ISO/IEC 42001 give that work a structure.Read AWS or Azure for AI workloads: how to chooseFor most companies, the right cloud for AI is the one where their identity, data and technical team already sit. As of September 2026, Amazon Bedrock and Microsoft Foundry both offer models from OpenAI, Anthropic and other providers, so model choice rarely settles the question alone. Identity, data location, compliance scope, skills and existing agreements usually do.Read Connecting AI to your ERP, CRM and data warehouseConnect AI to your ERP, CRM and data warehouse through the vendor's supported APIs, webhooks, event streams or an integration platform, not by writing straight to the database. Start with read access, send every write through a named approver until the workflow has proven itself, and check API limits, licenses, permissions and data quality before you commit to a design.Read Planning a legacy application migration to the cloudA legacy application migration to the cloud starts with deciding, application by application, whether to retire, retain, rehost, replatform, refactor or replace it. Then map dependencies and data, set a cost baseline, plan cutover with a tested rollback, and treat security and identity as part of the move rather than an afterthought. Add AI or automation during the migration only where you are already changing that part of the system.Read Model-agnostic AI architecture: avoiding vendor lock-inA model-agnostic AI architecture puts one internal interface between your application and any model provider, so swapping models is a configuration change rather than a rewrite. It matters because models are retired on published schedules, often within 12 to 18 months of launch. The real lock-in usually sits elsewhere: in proprietary vector stores, agent frameworks, fine-tunes and contract commitments.Read
Tools and checklists
AI data readiness checkCheck the data behind one workflow across five areas, and get the three steps that would make it ready for AI.Open the tool AI cloud readiness checkEight questions on running an AI workload on AWS or Azure. See your readiness by area and what to set up first.Open the tool AWS or Azure for AI: decision checklistQuestions to answer before choosing AWS or Azure for an AI workload, covering identity, data location, model access, skills, support and commercial terms.See the checklist
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