AI gateway routing, rate limits, provider failover, and prompt policy.
Topic hub
AI infrastructure engineering.
AI infrastructure is the production platform layer around model-powered systems. It connects model routing, RAG, Kubernetes runtime, observability, cost control, and policy into something teams can operate safely.
01
Coverage
What this hub covers.
A practical hub covering AI gateways, RAG platforms, Kubernetes/EKS runtime, observability, token cost controls, policy, and production operations.
RAG knowledge platforms with citations, source freshness, and clear answer boundaries.
Kubernetes/EKS runtime patterns for AI services and platform workloads.
Token, cost, latency, and provider-health observability.
Policy-as-code and data-handling controls around AI requests.
02
Related cases
Evidence nearby.
Architecture Rehearsal
Teams discover architectural blast radius only after a change lands in production
Pre-deploy break risk → verified after change
TwinOps Control Plane
Industrial digital twins are often demos, not operable infrastructure with drift, reconciliation, and incident replay
Compose → detect drift → reconcile
AI Runtime Platform
AI workloads often start as API calls but become production systems that need identity, policy, scaling, telemetry, and clear ownership
Every model call: policy + trace + budget
AI Infra Control Plane
Private AI platforms need shared governance across identity, policy, audit, cost, and SLO signals — not scattered service configs
Private AI with explicit platform contracts
Cloud-Native AI Gateway
AI usage needs routing, policy, budget awareness, and provider resilience
One boundary: rate, budget, fallback, OTel
RAG Knowledge Platform
Engineering knowledge is spread across repositories, runbooks, tickets, architecture notes, and project history
Cited answers from project evidence
LLM Infrastructure Runtime
LLM workloads move faster than traditional platform controls and can quickly become expensive, opaque, and hard to operate
LLM serving with operability signals
Cost and Token Observability
AI and cloud costs can grow quietly when usage is disconnected from teams, services, and deployment changes
Token spend visible before finance week
Available for meaningful infrastructure conversations