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.
Core topics
- AI gateway routing, rate limits, provider failover, and prompt policy.
- 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.
Related architecture cases
- Cloud-Native AI Gateway — AI becomes operable infrastructure, not an opaque API call.
- RAG Knowledge Platform — The AI assistant can answer infrastructure questions with project context, sources, and a safer boundary around what it knows.
- LLM Infrastructure Runtime — LLM usage becomes a controlled platform capability with observability and operating contracts instead of isolated API calls.
- Cost and Token Observability — Cost becomes an operational signal teams can understand before it becomes a finance surprise.
- OpenTelemetry Observability Mesh — Production behavior becomes easier to understand from request path to workload to infrastructure signal.
FAQ
What does an AI Infrastructure Engineer do?
An AI Infrastructure Engineer designs the platform layer around AI workloads: model routing, RAG systems, Kubernetes runtime, observability, cost controls, policy, and production operations.
What is an AI Gateway?
An AI Gateway is an infrastructure boundary for AI requests. It handles routing, rate limits, provider failover, prompt policy, token budgets, and telemetry before requests reach model providers.
How do you monitor LLM infrastructure?
LLM infrastructure needs request telemetry, token and cost attribution, latency/error SLOs, provider health, prompt-policy signals, and traces that connect AI requests back to services and users.
What makes AI infrastructure production-ready?
Production AI infrastructure needs observable request flows, provider fallback, token and cost controls, latency/error SLOs, policy enforcement, secure data handling, and clear operational ownership.