Azure vs AWS for Generative AI Workloads in 2025
A head-to-head evaluation of Azure OpenAI, Foundry, Bedrock, and SageMaker for enterprise-scale generative AI.
Enterprise AI has entered its agentic phase. In this piece, we lay out the reference architecture our team has field-tested with dozens of Fortune 500 organizations across banking, healthcare, retail, and manufacturing.
The three-layer stack
We recommend organizing the enterprise agent stack into three composable layers: an orchestration layer for planning and tool use, a grounding layer for retrieval and enterprise data access, and a governance layer that intercepts every action for policy checks, telemetry, and human oversight.
Governance is a product, not a checklist
The winning teams we have observed treat governance not as a compliance afterthought but as an internal product with its own roadmap, SLAs, and telemetry. Guardrails, evals, and red teaming are shipped alongside every capability.
Where to start
Pick a narrow, high-frequency workflow with clear ground truth. Instrument it, ship a v0 in six weeks, and let your evals — not your intuitions — tell you when to expand scope.
This is a Nelara Insight preview. Full publication with detailed reference diagrams and code samples available upon request.