Azure OpenAI Service Solutions

According to Microsoft’s published data, privacy and security documentation for Azure OpenAI, prompts and completions are not used to train OpenAI’s foundation models and are not made available to OpenAI. Data is processed within Microsoft’s Azure environment under Microsoft’s product terms. Abuse monitoring settings and data residency depend on deployment type and configuration, so we review these details with your security and legal teams during design.

Microsoft Foundry, previously called Azure AI Foundry, is Microsoft’s platform for building AI applications and agents. Azure OpenAI models are offered in Foundry alongside models from other providers, together with tools for agents, evaluation, safety and monitoring. Existing Azure OpenAI resources and APIs continue to work. We design solutions with Foundry’s current project structure and help migrate older resources where useful.

Global Standard deployments generally offer the highest availability and capacity, Data Zone deployments keep processing within a geographic zone such as the EU or US, and regional Standard keeps processing in one region. Provisioned throughput reserves capacity for predictable latency at high volume, and Batch suits large asynchronous jobs at lower cost. We choose based on data residency, latency, volume and budget.

We ground answers in your content with retrieval and require citations, restrict the assistant to its intended scope, and apply Azure AI Content Safety filters and prompt shields against jailbreak attempts. Before launch we evaluate quality, groundedness and safety on a test set built from real questions. In production we monitor answers, collect feedback and rerun evaluations whenever prompts, models or data change.

Running costs consist mainly of model usage, charged per token or through provisioned capacity, plus supporting services such as Azure AI Search, storage, hosting and monitoring. Costs depend on traffic, prompt length, retrieved context and model choice. We estimate cost per transaction during the prototype and reduce it through prompt optimization, caching, model routing and choosing smaller models where they meet quality targets.

A focused prototype tested against an evaluation set typically takes four to six weeks. Hardening for production, including security review, integration, user testing and monitoring, usually adds several more weeks depending on complexity and approvals. Agents with multiple tool integrations take longer. We share progress through weekly written updates and sprint demos, so you see working software throughout.