Enterprise AI Infrastructure Requirements Checklist for Planning

An enterprise AI infrastructure requirements checklist covers seven dimensions — compute, storage, networking, security, compliance, operations, and cost — each specified before procurement because a dimension planned after buying is a constraint discovered too late. For the capacity planning, see how to size AI infrastructure capacity. For the security framework, see AI infrastructure security requirements.

The Seven Dimensions

Compute: GPU type, count, memory, and the parallelism strategy that connects them. Storage: capacity, throughput tier, and data lifecycle governance. Networking: interconnect fabric, topology, bandwidth, and RDMA support. Security: isolation, encryption, IAM, audit logging, and AI-specific data path controls. Compliance: applicable frameworks, evidence production, and audit readiness. Operations: monitoring, incident response, patching, optimization, and staffing or managed service scope. Cost: total cost model including compute rate, storage, networking, operations, and lifecycle — not just the GPU rate. Each dimension must be specified before procurement; a dimension that is "we'll figure it out later" is a cost or performance surprise waiting to happen. For the full planning method, see GPU cluster capacity planning checklist.

FAQ

What should an enterprise AI infrastructure plan cover?

Compute, storage, networking, security, compliance, operations, and cost — all seven specified before procurement. A dimension planned after buying is a constraint discovered too late. See the seven dimensions above.

Summary

Enterprise AI infrastructure is seven dimensions planned together. For the full framework, see how to size AI infrastructure capacity.