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
Monitoring AI in production: what to log, measure and alert onMonitoring AI in production means watching four things that ordinary uptime checks miss: whether the answers are still good, what each task costs, how the system behaves when inputs change, and whether the human approval step is keeping up. Log every model call and tool action with sensitive data protected, sample real outputs for review, alert on thresholds a named owner agreed in advance, and write down how to roll back.Read RAG, fine-tuning or prompting: how to give AI your company's knowledgeUse retrieval (RAG) when the AI must answer from facts that change and show where each answer came from. Use fine-tuning when you need the model to behave consistently, for example a fixed output format or a narrow classification. Start with prompting, because a small, stable body of context often needs neither.Read How to write an AI acceptable use policy for employeesAn AI acceptable use policy tells employees which AI tools they may use, what data may go into them, how closely to check the output, and who to ask when a new tool shows up. It matters now because most employees already use AI on their own initiative, often through personal accounts the company cannot see. A workable policy fits on two pages, matches its rules to how sensitive the data is, and names one owner who keeps it current.Read 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 AI acceptable use policy builderAnswer questions about the AI tools your people use, the data they handle and who reviews what, and get a draft policy your team can adapt.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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