Kubernetes
Kubernetes is our home field. Before the market wanted agents, we were building and running production clusters — multi-tenant, security-reviewed, autoscaled, and boring in the way infrastructure should be boring. It's the proof underneath everything else we sell: when we say your AI system will survive production, this is why.
We handle Kubernetes at every altitude: cluster architecture and provisioning, workload hardening that passes enterprise security review, GPU scheduling for inference fleets, cost engineering that routinely cuts infrastructure spend in half, and the operational practices — GitOps, observability, incident discipline — that keep the pager quiet. Whether you're standing up your first cluster or untangling your fifth, we've seen the failure mode before.
Cluster architecture and buildout
EKS, GKE, AKS, or bare metal — designed for your workloads, tenancy, and compliance boundary.
Security hardening
Pod security, network policy, RBAC, and supply-chain controls that pass real security review.
GPU and AI workload scheduling
Inference and training fleets with bin-packing, priority, and utilization you can defend to finance.
Cost optimization
Right-sizing, spot strategies, and autoscaling — the 50% infra-spend reductions we're known for.
Platform operations
Upgrades, monitoring, and runbooks that make cluster ownership a job, not a lifestyle.
Why does an AI consultancy lead with Kubernetes?
Because the deployment gap is an infrastructure problem as much as a model problem. Anyone can call an API; making agents run securely, observably, and at scale inside an enterprise perimeter is platform engineering. We had the platform muscle first — the agents landed on foundations that already worked.
Our cluster works but nobody trusts it. Sound familiar?
Very. That's usually missing guardrails, not missing genius: no network policies, hand-edited YAML drift, upgrades everyone fears. We bring GitOps, policy enforcement, and rehearsed upgrade paths — trust follows repeatability.
Is Kubernetes overkill for us?
Sometimes! If you run three services and a database, managed containers or a PaaS may serve you better, and we'll say so. Kubernetes earns its complexity with scale, multi-tenancy, or compliance requirements — we recommend the platform your problem needs, not the one our T-shirts advertise.
Put Kubernetes to work — in production.
One forward-deployed engineer, embedded in your stack, owning the outcome from discovery to production. Weeks, not quarters.
Book a deployment →