A FHIR record is a graph of coded, dated, status-bearing resources, not a document corpus. This article sets out a retrieval design for generative AI over FHIR data: FHIR search for exact questions, similarity search for notes, pinned allergies and medication, a hard partition by patient, and citations on every statement. Tested on synthetic data and mapped to AWS HealthLake and the Azure Health Data Services FHIR service.
Read more →Tag: Multi-Cloud
Hosting Remote MCP Servers Securely on AWS and Azure: Token Validation, Gateways and Network Placement
A remote MCP server is an API that a language model can drive. This article covers the three things that decide whether it is safe: what the MCP authorization specification requires of the server, where to host it on AWS and Azure and who validates the token in each case, and where the endpoint belongs on the network. Includes a tested Python resource server and an ASP.NET Core version for Entra ID.
Read more →A2A and MCP Across Clouds: Connecting a .NET Agent Framework Agent to an Agent on Bedrock AgentCore
MCP connects an agent to its tools; A2A connects an agent to another agent. This article wires a .NET Microsoft Agent Framework coordinator on Azure to a Python agent and MCP tool server laid out for Amazon Bedrock AgentCore Runtime, and reports what broke: A2A 0.3 versus 1.0 version skew, identity across the cloud boundary, and task lifecycle handling.
Read more →Prompt Caching Economics on Amazon Bedrock and Azure OpenAI: Where It Pays and Where It Costs
Prompt caching cannot raise your bill on Azure OpenAI, but on Amazon Bedrock with Claude models a cache write costs more than not caching. This article derives the break-even points (one request per prefix every 20 minutes for the five-minute cache, every 80 minutes for the one-hour cache), gives a tested cost model, and shows how to measure cache read ratios on both platforms.
Read more →One Abstraction, Two Clouds: Microsoft.Extensions.AI over Azure OpenAI and Amazon Bedrock
Microsoft.Extensions.AI gives .NET services one interface over Azure OpenAI and Amazon Bedrock. This article builds a two-provider ASP.NET Core service with per-provider telemetry, caching, token metering, tool calling and failover, and is clear about where the abstraction stops: model identity, errors, content safety and model behaviour remain provider-specific.
Read more →Cloud LLMOps: Mastering AWS Bedrock, Azure OpenAI, and Google Vertex AI
Deep dive into cloud LLMOps platforms. Compare AWS Bedrock, Azure OpenAI Service, and Google Vertex AI with practical implementations, RAG patterns, and enterprise considerations.
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