The role of AI in enterprise architecture is to connect model and data characteristics to an infrastructure topology that meets latency, compliance and cost goals. Senior architects must translate latency, model size, data residency and update cadence into defensible topology choices during procurement and architecture reviews.
This framework gives a vendor‑neutral decision matrix, PoC benchmarking checklist, portability guardrails for RFPs, and a six‑item governance readiness checklist for regulated sectors.
The Role of AI in Enterprise Architecture — Map Workloads To Topology
Match workload archetypes to topology by focusing on latency, data gravity and update cadence.
Define The Workload Types Architects See
- Batch training: throughput‑oriented, tolerant of higher latency (e.g., nightly re‑training).
- Low‑latency real‑time inference: sub‑second responses (e.g., fraud scoring).
- Edge sensing / near‑device inference: minimal latency and intermittent connectivity (e.g., in‑store personalization).
- Foundation‑model access: large models via API or self‑hosted; distinct for size, cost and update control.
Decision Matrix (visual/table) — Workload → Topology
| Workload | Recommended Topology | Primary Tradeoffs (Latency / Data Residency Risk / Speed‑to‑Market) |
|---|---|---|
| Batch training | Private / Hybrid | Higher latency OK / Lower residency risk / Slower to deploy |
| Low‑latency inference | Edge or Colocated Private | Very low latency / Moderate residency risk / Moderate speed |
| Edge sensing | Edge | Lowest latency / Low–moderate residency risk / Faster for device updates |
| Foundation‑model access | Public Cloud or Sovereign‑hosted | Moderate latency / Higher residency risk if public / Fastest speed‑to‑market |
| Mixed / Hybrid | Hybrid | Variable latency / Medium residency risk / Balanced speed |
Public clouds provide scalability and rapid innovation; private clouds give control and compliance for sensitive data — use those contrasts to justify topology during procurement (see Microsoft Azure link).
Quick Heuristics
- Extremely low 95th‑percentile inference latency (tight real‑time requirements) → prefer edge or colocated private.
- Non‑negotiable data residency → default to private/sovereign clouds; require region attestations.
- Weekly or slower model updates with high request volumes → managed hosted models often win speed‑to‑market.
Cost & Hardware Tradeoffs — What To Benchmark In PoCs
Capture these metrics so procurement can compare managed vs self‑hosted TCO.
Essential Metrics To Capture In Vendor Trials
- Accelerator hourly rates (training vs inference).
- Average accelerator utilisation during peak windows.
- Cold‑start time and cost for model loading.
- Network egress per inference and cross‑region patterns.
- Storage I/O patterns and model artifact size — see storage readiness checks in Enterprise ai infrastructure storage readiness gap.
Use MLPerf‑style scenarios to sample representative latency and throughput (see MLCommons link).
Rule‑Of‑Thumb Scenarios
- Managed hosted foundation models often cheaper when inference dominates, updates are infrequent, and traffic is high.
- Private GPU pools pay off for frequent large‑scale training or fine‑tuning when egress and residency costs would be high.
Practical TCO Pitfalls
Watch for cold starts, hidden storage I/O charges, under‑utilised accelerators, and undocumented cross‑region egress. Request profiling runs and sample 30/90‑day billing examples for the PoC.
Portability & Vendor Lock‑In Controls
Reduce migration risk by insisting on portability proofs and CI/CD controls in RFPs.
Technical Portability Options To Insist On
Require containerized serving images, ONNX export paths and compatibility with neutral runtimes. Cite the ONNX IR spec when asking for exportability tests.
CI/CD & Artifact Controls (Minimum RFP Requirements)
- Model snapshotting and exportable artifacts
- Deterministic inference image builds
- Automated canary rollouts and rollback playbooks
- Artifact signing for provenance
- Data egress SLAs
Reference governance considerations in Who is responsible when ai fails.
Vendor Questions To Reduce Lock‑In
- Do you support ONNX export for our model family?
- Can you run an exported model on a neutral runtime during the PoC?
- How are model snapshots stored and exported?
- Can you provide sustained accelerator utilisation reports and 30/90‑day egress/pricing examples?
- Can you reproduce an inference image from source with deterministic build logs?
Governance, Data Residency & Operational Readiness
A concise operational checklist for regulated sectors.
6‑Item Governance & Operational Readiness Checklist
- Data‑residency mapping by workload.
- Least‑privilege access control model.
- Audit trails for model inputs, outputs and decision logs.
- Retention & deletion policies for training and inference data.
- Encryption at rest and in transit.
- Breach/incident playbook and SLA alignment.
Choose private/sovereign when law, auditability or predictable performance demand local control; require provider attestations and testable region controls (see Dataiku link).
Three Micro‑Cases: Apply The Matrix (Retail, Finance, Healthcare)
Retail — Edge real‑time personalization: colocate inference in stores with cloud sync for updates. Tradeoffs: very low latency personalization versus update cadence; require offline serving, delta updates, edge telemetry and rollback playbooks.
Finance — Batch re‑training with residency constraints: private or hybrid for large batch re‑training on sensitive data to avoid egress costs. Require residency attestations, audit trail exports, KMS integration and bulk egress pricing examples (see legal governance link).
Healthcare — Foundation‑model access under residency rules: hybrid or sovereign‑hosted to balance compliance and speed. Ask for hosted model latency SLOs, data usage policies, exportability and region SLAs (see healthcare link).
Frequently Asked Questions
Conclusion
Map workloads to topology using latency, data gravity/compliance and update cadence. Capture PoC metrics (accelerator rates, utilisation, cold‑start and egress), require portability controls and vendor attestations, and apply the governance checklist for regulated workloads. If you need a tailored architecture brief or RFP checklist, contact Beyond Prompts for a short advisory workshop: Contact us. The role of AI in enterprise architecture is choosing the right topology and controls, not just a cloud vendor.
About the author
Beyond Prompts is an IrizPro publication offering advisory workshops and PoC support for enterprise AI architecture decisions. Learn more on the About page: About us.
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