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What we do

Four practices. Every service has a clear scope and an honest list of what we don't do. Start with one and add others when they make sense.

Operate

Keep production healthy, day and night.

A failed Kubernetes node and its pods rescheduledThree worker nodes run application pods under one control plane. Node C fails and is marked not ready. Its three pods are rescheduled onto nodes A and B, so all eight pods keep running.CONTROL PLANENODE ANODE BNODE CNOT READY8 PODS · 8 RUNNING

Managed Cloud & Kubernetes

On-call, upgrades and scaling for your clusters and cloud accounts.

A policy gate rejecting a risky deploymentDeployments travel from a pipeline toward a Kubernetes cluster through a policy gate. Two compliant deployments pass into the cluster. A request for a privileged pod is stopped at the gate and denied.PIPELINEDEPLOYPOLICYCLUSTERDENIED · PRIVILEGED POD

Cloud Security & Compliance

Hardening, secure pipelines and evidence your auditors can work with.

Build

Give your engineers a platform worth shipping on.

A commit moving through the delivery pipelineA git push starts a pipeline with four stages: build, test, security scan and deploy. Each stage passes in turn and the change reaches production with a green check.GIT PUSHBUILDTESTSCANDEPLOYDEPLOYED TO PRODUCTION

Platform Engineering & DevOps

CI/CD, infrastructure as code and GitOps your engineers will want to use.

A virtual machine split into containers on KubernetesA single virtual machine runs web, API and worker components together. During the migration each component moves into its own container on a Kubernetes cluster, and the virtual machine is left empty.VM · APP-01WEBAPIWORKERKUBERNETESwebapiworker

Cloud Architecture & Migration

Designs and migrations planned in stages you can roll back.

Optimize

Spend less without running closer to the edge.

Kubernetes nodes consolidated and cloud spend bending downFour lightly used nodes are rightsized. Workloads are packed onto two nodes that now run at healthy utilization, and the other two are drained. On the chart, monthly spend leaves its rising trend and bends downward.NODE UTILIZATIONN1N2N3N4MONTHLY SPENDTRENDAFTER

Cloud Cost Optimization

Waste removed, commitments sized and every dollar given an owner.

AI Infrastructure

Run training and inference like any other production system.

A GPU node pool and the training-to-serving loopFour GPUs in a Kubernetes node pool run near full utilization. Below them a model moves through a loop of data, training, checkpoint, evaluation and serving, then back to data for the next run.GPU NODE POOLGPU 0GPU 1GPU 2GPU 3DATATRAINCHECKPOINTEVALSERVENEXT RUN

AI & LLM Infrastructure

GPU clusters, training pipelines and inference that keeps its latency.

Next step

Get a free cloud assessment

Spend 45 minutes with a senior engineer and leave with three concrete improvements, whether or not you work with us.

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Or email hello@infraok.com