Production clusters
Managed EKS, AKS or GKE clusters with network policies, namespaces and resource limits, defined in Terraform.
Data and DevOps
Kubernetes development.
Kubernetes schedules and manages containers across a cluster, handling rollouts, scaling and recovery for you. We use it when a system has enough services or traffic variation to justify the operational overhead.
A short call. Your first build, a fixed quote, and the timeline. No obligation.
What we build with Kubernetes
Where it fits.
Helm and GitOps delivery
Helm charts deployed through Argo CD or Flux, so every change is reviewed and reversible.
Autoscaling workloads
Horizontal pod autoscaling and KEDA event-driven scaling, including GPU node pools for model serving.
Observability stacks
Prometheus, Grafana and log pipelines with alerts tied to the signals that matter.
When it is the right choice
Choose Kubernetes when
- →You run many services that need consistent deployment and scaling.
- →You serve models or workloads that need GPU scheduling.
- →You want portability across clouds.
Worth knowing
Honest trade-offs.
- →It adds real operational complexity, so a small app is often better on a simpler container platform.
- →Security and upgrades need ongoing attention, as clusters are not set and forget.
Often paired with
DockerHelmTerraformArgo CDPrometheusGrafana
Related services
Services that use it.
Proof
Work built by the same team.
Closeum
13+ connected modules from calls through commissions
13+connected modules
5role types
32completed transactions
40+leads managed
SIIMS
Orders, production, warehouse, and dispatch on the same records
4operational roles
37backend tests passed
17hosted workflow tests passed
Ledgerline
603 tests passed in a dated financial-control verification
SAR 760Kcash reconciled end to end
603tests passed
14control findings fixed
3finance sources in one ledger
Common questions
Before you start.
Do we really need Kubernetes?
Often not at first. If you have a few services, a managed container service is simpler, and we move to Kubernetes when scale or complexity calls for it.
Managed or self-run cluster?
Managed in almost every case, since the control plane is handled by the cloud provider. Self-run is for special on-premise needs.
Can you serve AI models on it?
Yes, using GPU node pools and serving engines such as vLLM or Triton, with autoscaling and rollout controls.
What do you build with Kubernetes?
Service orchestration, Autoscaling, Zero-downtime releases, Multi-environment setups, and related work. We choose Kubernetes when it is the right fit for the product, the team and the budget.
Can you work with our existing Kubernetes code?
Yes. We can review what you have, fix what is fragile, and extend it, or build alongside your team.
Keep exploring
More than Kubernetes.
Other data and devops
Where we apply it
The Audit
Get my free AuditKnow what to build first.
Book a short call and show us one workflow. You leave with what we would build first, a fixed quote, and what should wait.