KubeWithAI helps organizations build secure, scalable, and production-ready Kubernetes platforms while enabling AI workloads through modern cloud infrastructure, GPU platforms, GitOps, automation, and observability.
Focused engagements that deliver operable systems — not slideware.
Design and operate production-grade Kubernetes platforms with multi-tenant guardrails, secure networking, and golden paths for developers.
Reference architectures for training, fine-tuning, and inference — from vector stores and RAG to LLM serving on Kubernetes.
GPU cluster design, scheduling, MIG/partitioning, driver operators, and fair-share for shared AI workloads.
ArgoCD/Flux-driven delivery, progressive rollouts, environment automation, and secure, auditable release pipelines.
AWS and Azure landing zones, workload replatforming, Terraform/OpenTofu foundations, and cost governance.
SLO frameworks, incident response, and end-to-end telemetry with Prometheus, OpenTelemetry, Grafana, and Loki.
Selected pragmatically for each engagement — no vendor lock-in, no hype-driven choices.
From enterprise platforms to AI infrastructure — engineered for teams that ship.
A focused practice built around outcomes, not billable hours.
Least-privilege, policy-as-code, and supply-chain hygiene baked in — not bolted on.
Platforms designed for the traffic, teams, and AI workloads you'll run in two years.
SLOs, telemetry, and runbooks so on-call is boring and incidents are short.
GPU scheduling, model serving, and MLOps designed alongside your platform, not after it.
Illustrative examples only. They represent the types of engagements KubeWithAI delivers and are not attributed to any client.
Consolidated a fragmented multi-cluster estate onto a hardened platform with paved-road namespaces, network policies, and centralized policy enforcement — cutting onboarding time for new services from weeks to days.
Replaced ad-hoc CI scripts with an ArgoCD-based delivery model: signed manifests, progressive delivery, and drift detection across environments — restoring deployment confidence for the platform team.
Designed a GPU-aware Kubernetes platform with fair-share scheduling, model artifact management, and inference autoscaling — enabling ML teams to iterate without fighting infrastructure.
Deep-dive into your platform, workloads, and team constraints. Honest findings, not a sales pitch.
Reference architecture and phased roadmap grounded in your operational reality.
Hands-on delivery with your engineers — Terraform, Kubernetes, GitOps, observability wired end-to-end.
SLOs, runbooks, and enablement so your team owns the platform after we hand it over.
KubeWithAI is an independent consulting company focused on helping organizations modernize infrastructure, build enterprise Kubernetes platforms, and deploy AI workloads securely at scale.
Our expertise spans Kubernetes, Platform Engineering, DevOps, GitOps, Cloud Infrastructure, Site Reliability Engineering, AI Infrastructure, GPU Computing, and Enterprise Automation.
Whether you're standing up a new Kubernetes platform, taming an existing one, or building infrastructure for AI workloads — a short conversation is usually enough to know if there's a fit.
Occasional field notes on platform engineering, GPU infrastructure, and running AI workloads in production. No spam.