Azure AI Infrastructure: The Architecture We Use for Every Enterprise AI Deployment

Azure is our primary cloud platform for enterprise AI workloads. Over multiple deployments, we've refined a reference architecture that we use as the starting point for every new engagement. Here's what it looks like and why we made the choices we did.

The Core Components

Azure Kubernetes Service (AKS)

AKS is the compute backbone for our AI deployments. We run model inference, API services, and agent orchestration on AKS. The key configuration choices: node pools separated by workload type (CPU for API services, GPU for inference), cluster autoscaling configured for AI workload burst patterns, and Azure CNI networking for pod-level network policies.

Azure OpenAI Service

For LLM inference, we use Azure OpenAI rather than the public OpenAI API for enterprise deployments. The reasons are straightforward: data residency guarantees, private networking via Private Endpoint, and the ability to use Azure's identity and access management for API authentication. We configure PTU (Provisioned Throughput Units) for predictable latency on production workloads.

Azure Databricks

Databricks is our data platform of choice for the lakehouse layer. We use Unity Catalog for data governance, Delta Live Tables for pipeline orchestration, and Databricks Model Serving for custom model deployment. The integration between Databricks and Azure OpenAI via the AI Gateway is particularly useful for RAG applications.

Azure AI Search

For vector search in RAG applications, we use Azure AI Search with the hybrid search capability (combining vector and keyword search). The managed service eliminates the operational overhead of running a vector database, and the integration with Azure OpenAI for embedding generation simplifies the pipeline.

Networking and Security

All components communicate over private endpoints — no public internet exposure for data or model traffic. We use Azure Private DNS zones to resolve private endpoint addresses within the virtual network. Network Security Groups enforce least-privilege traffic rules between components.

Identity is managed through Azure Managed Identity — no credentials stored in code or configuration. Every service authenticates to every other service using its managed identity, with RBAC assignments scoped to the minimum required permissions.

Observability

We instrument every deployment with Azure Monitor, Application Insights for application telemetry, and LangSmith for LLM-specific tracing. The combination gives us visibility into infrastructure metrics, application performance, and AI-specific metrics (token usage, latency by model, retrieval quality) in one place.

If you're planning an Azure AI deployment and want to discuss the architecture specifics for your workload, we're happy to have that conversation.