Case 06
LLM Infrastructure Runtime
LLM Infrastructure Runtime: Problem: LLM workloads move faster than traditional platform controls and can quickly become expensive, opaque, and hard to operate. Constraints: GPU/CPU placement, model latency, token cost, prompt boundaries, provider limits, data privacy, and fallback behavior. Architecture: Runtime layer with model routing, request budgets, telemetry, policy checks, provider abstraction, and operational dashboards around inference flows. Result: LLM usage becomes a controlled platform capability with observability and operating contracts instead of isolated API calls.
- Problem
- LLM workloads move faster than traditional platform controls and can quickly become expensive, opaque, and hard to operate.
- Constraints
- GPU/CPU placement, model latency, token cost, prompt boundaries, provider limits, data privacy, and fallback behavior.
- Architecture
- Runtime layer with model routing, request budgets, telemetry, policy checks, provider abstraction, and operational dashboards around inference flows.
- Result
- LLM usage becomes a controlled platform capability with observability and operating contracts instead of isolated API calls.
Related topics: AI infrastructure, Kubernetes/EKS, GitOps, Terraform, observability, platform engineering, cloud architecture.