AI Infrastructure Architecture & Advisory

Challenge

The team needed a production architecture before committing an implementation: what to serve in-house, what to leave to a managed service, how traffic fails over, and which reliability and security properties the path required.

Decision

Separate what to implement now from what to leave as a later phase, and keep self-hosted serving versus managed options as an explicit choice — not an implied default.

Approach

The advisory work covered architecture and implementation guidance: serving and routing options, GPU and Kubernetes considerations, observability, and the security and reliability constraints a production path would need. It is labeled advisory because it was design and direction, not a claim of completed production operation.

Technical Delivery

Delivered a sequenced architecture covering model-serving approaches, routing and failover, Kubernetes-based compute, GPU workloads, logging, and production constraints. The team left with a plan they could implement — not a claim that the finished system had already been operated in production.

Outcome

The team left with a clear architecture for serving and routing, a reasoned self-host versus managed decision, and a phased path from current state to production.

Technologies

• Kubernetes
• Model serving
• GPU workloads
• AI gateways
• Observability
• Infrastructure security

Related services

• AI Platform & MLOps
• Advisory & Architecture
• Production readiness