Production Path
Kubernetes-based AI platforms for private AI workloads.
We design Kubernetes-based AI platforms for teams that need private model serving, GPU-aware capacity planning, observability, security boundaries, cost control, and a Day-2 operating model that survives launch.
AI workloads need a real operating model, not just a cluster.
Platform architecture
We design cluster topology, environment boundaries, workload placement, networking, storage, and deployment patterns around private AI requirements.
Model serving and GPU planning
We define model-serving topology, GPU-aware scheduling, capacity planning, autoscaling expectations, and cost controls before demand surprises the platform.
GitOps and infrastructure-as-code
We use versioned infrastructure, declarative delivery, reviewable change paths, and rollback-friendly workflows so platform changes stay auditable.
Security and compliance boundaries
We design RBAC, network policy, image controls, secrets handling, audit logging, and runtime boundaries around sensitive workload requirements.
Observability and incident readiness
We define metrics, logs, traces, alerts, dashboards, runbooks, and ownership expectations for the AI platform and the services running on it.
Operational handoff
We document architecture decisions, runbooks, upgrade expectations, failure modes, and support boundaries so your team can operate the platform.
Need a Kubernetes platform that can run private AI?
Book a 30-minute AI Infrastructure Review. We'll map your data boundaries, platform risks, agent controls, and operating model before you commit to a build path.