Case 25 · Flagship

AI Infra Control Plane

An AI infra control plane connects policy, identity, audit, intent, MCP, FinOps, and SLO signals into one operating model for private AI on Kubernetes.

01

Evidence

Problem, constraints, architecture, result.

Problem

Private AI platforms often wire identity, policy, cost, and SLOs as scattered service configs. That makes ownership, audit, and platform-level governance hard to operate.

Architecture

The control plane unifies OPA policy, OIDC identity, audit trails, intent routing, MCP tool boundaries, FinOps signals, Redis state, and Prometheus SLO telemetry under Kubernetes ownership.

Trade-offs

A shared control plane concentrates platform responsibility. The payoff is consistent contracts across AI services instead of every team reinventing governance.

Result

Governed private AI becomes operable as a platform product with explicit policy, identity, cost, and SLO contracts.

02

Snapshot

Field notes.

Problem
Private AI platforms need shared governance across identity, policy, audit, cost, and SLO signals — not scattered service configs.
Constraints
OPA policy, OIDC identity, MCP tool boundaries, FinOps signals, Prometheus SLOs, Redis state, and Kubernetes ownership.
Architecture
Control plane that connects policy, identity, audit, intent, MCP, FinOps, and SLO telemetry into one AI infrastructure operating model.
Result
Governed private AI becomes operable as a platform product with explicit contracts instead of ad-hoc service wiring.
03

Related

Nearby systems.

Case 01

Automatic SaaS Restore System

Restores are high-pressure, manual, and easy to execute inconsistently.

Result: Recovery becomes a platform capability instead of an emergency script.

Case 02 · Flagship

Cloud-Native AI Gateway

AI usage needs routing, policy, budget awareness, and provider resilience.

Result: AI becomes operable infrastructure, not an opaque API call.

Case 03 · Flagship

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.

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