LLM workloads move faster than traditional platform controls and can quickly become expensive, opaque, and hard to operate.
Case 06
LLM Infrastructure Runtime
LLM workloads move faster than traditional platform controls and can quickly become expensive, opaque, and hard to operate.
01
Evidence
Problem, constraints, architecture, result.
GPU/CPU placement, model latency, token cost, prompt boundaries, provider limits, data privacy, and fallback behavior.
Runtime layer with model routing, request budgets, telemetry, policy checks, provider abstraction, and operational dashboards around inference flows.
LLM usage becomes a controlled platform capability with observability and operating contracts instead of isolated API calls.
02
Snapshot
Field notes.
- 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.
03
Related
Nearby systems.
Cloud-Native AI Gateway
AI usage needs routing, policy, budget awareness, and provider resilience.
Result: AI becomes operable infrastructure, not an opaque API call.
Kanister Backup & Restore
Application-aware Kubernetes restores need more than volume snapshots and manual runbooks.
Result: Restore behavior becomes repeatable, reviewable, and easier to exercise before an incident.
GitOps: Argo CD & Flux
Teams need a clear delivery model before GitOps becomes another layer of operational confusion.
Result: GitOps decisions become explicit platform contracts instead of tool preference debates.
Available for meaningful infrastructure conversations