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.
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.
Evidence
Problem, constraints, architecture, result.
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.
A shared control plane concentrates platform responsibility. The payoff is consistent contracts across AI services instead of every team reinventing governance.
Governed private AI becomes operable as a platform product with explicit policy, identity, cost, and SLO contracts.
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.
Related
Nearby systems.
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.
Cloud-Native AI Gateway
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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.