Cloud & Infrastructure

Kubernetes Consulting Services

We build and operate Kubernetes for teams that genuinely need it, and say so plainly when a simpler platform will serve them better.

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How we work

Kubernetes is the right tool for a specific kind of problem: many services, run by more than one team, that need consistent scheduling, autoscaling and self-healing across a fleet of machines. Our kubernetes consulting services cover cluster design on EKS, GKE and AKS, Helm charts for application packaging, and the autoscaling and networking configuration that make a cluster behave predictably under real traffic.

We also do the less glamorous but more common work: migrating an application onto an existing cluster someone else designed, tightening RBAC so teams cannot accidentally affect each other's workloads, and setting up GitOps with ArgoCD so deployments happen through a pull request rather than a manual kubectl command.

A meaningful part of this work is telling clients when Kubernetes is not the answer. We would rather recommend ECS or Cloud Run for a small application and lose the more complex engagement than build a cluster that a two-person team will struggle to operate.

Fit

When Kubernetes is the right choice, and when it is not

Good fit

You are running ten or more services that need to scale independently, and the coordination overhead of managing them separately has become its own problem.
More than one team deploys to the same infrastructure, and you need namespace isolation and RBAC to keep them from interfering with each other.
You need deployment strategies like canary releases or blue-green rollouts that are difficult to build reliably without an orchestrator.
Your workload needs to run consistently across more than one cloud, and a Kubernetes API gives you one operating model instead of three.
You already have a platform team with the capacity to own cluster upgrades, security patching and incident response.

Consider something else

A two-service application run by a small team is usually a poor fit; ECS Fargate or Cloud Run deliver the same reliability with far less to operate and patch.
Teams without dedicated platform or DevOps capacity often underestimate the ongoing work of cluster upgrades, node patching and debugging scheduling issues.
A workload with a single service and predictable, low traffic rarely benefits enough from autoscaling and self-healing to justify a cluster's baseline overhead.
Early-stage products still finding product-market fit benefit more from shipping quickly on a simpler platform than from infrastructure built for a scale they have not reached yet.

What we build

What we build with Kubernetes

Cluster architecture
Node pool design, networking topology and multi-tenancy strategy on EKS, GKE or AKS, sized to actual traffic rather than a generic reference architecture.
Application packaging
Helm charts and Kustomize overlays that make deployments repeatable across environments, with values scoped per environment rather than duplicated configuration.
Autoscaling
Horizontal Pod Autoscaler and Cluster Autoscaler configuration tuned to real usage patterns, avoiding both under-provisioning during traffic spikes and idle capacity the rest of the time.
GitOps and deployment pipelines
ArgoCD or Flux wired to a Git repository so cluster state reflects what is committed, making deployments auditable and rollbacks a revert rather than a manual fix.
Access control
RBAC and namespace isolation so multiple teams or environments can share a cluster without one team's workload affecting another's.
Stateful workloads
StatefulSets and persistent volume configuration for databases and queues that need to run inside the cluster rather than as a managed external service.

Ecosystem

Libraries and tools we pair it with

Helm
ArgoCD
Prometheus and Grafana
cert-manager
Kustomize
Istio
Amazon EKS
Google GKE
Azure AKS

When Kubernetes Is the Wrong Call

A recurring pattern we see is a small team running two or three services on a Kubernetes cluster sized for a much larger organization. The cluster works, but every deployment, every upgrade and every debugging session takes longer than it would on ECS Fargate or Cloud Run, because the team is paying Kubernetes's operational tax without needing its actual benefits: multi-team isolation, complex scheduling, or portability across clouds.

Our honest advice for a two-service application run by a small team is almost always to use a simpler managed platform. Kubernetes earns its complexity at a scale and organizational shape that most early and mid-stage products have not reached, and moving to it later, once the need is real, is far less costly than running it prematurely.

Managed Kubernetes on EKS, GKE and AKS

None of the major managed Kubernetes offerings eliminate operational work; they remove the burden of running the control plane yourself. EKS integrates most naturally with an existing AWS account's IAM and networking, GKE tends to have the smoothest cluster autoscaling and the lowest control-plane management overhead, and AKS fits best where a team is already deep in the Microsoft ecosystem and wants Entra ID integrated into cluster access.

We pick the managed offering based on where the rest of a client's infrastructure already lives rather than by a generic ranking, since networking, identity and logging integration matter more day to day than small differences in the underlying Kubernetes distribution.

EKS: best fit inside an existing AWS account and IAM setup
GKE: strongest autoscaling and lowest control-plane overhead
AKS: best fit for teams standardized on Entra ID and Microsoft tooling

Services

Services that use Kubernetes

Industries

Where we use it

FAQ

Kubernetes FAQs

Is Kubernetes overkill for our two-service application?

For most two-service applications run by a small team, yes. ECS Fargate or Cloud Run give you container deployment, scaling and restarts on failure without the ongoing work of cluster upgrades, node patching and RBAC configuration. Kubernetes becomes worthwhile once you have enough services, teams or specific scheduling needs that its coordination benefits outweigh its operational cost.

How much ongoing engineering time does running a Kubernetes cluster actually take?

It depends heavily on cluster size and whether you use a managed offering, but plan for regular time on node and control-plane upgrades, certificate rotation, monitoring alert tuning and the occasional scheduling or networking incident. A managed service like EKS, GKE or AKS reduces this considerably compared with self-managed Kubernetes, but does not eliminate it.

Which managed Kubernetes service should we use, EKS, GKE or AKS?

Choose based on where your other infrastructure already lives rather than a generic feature comparison. EKS fits naturally into an existing AWS account, GKE tends to have the smoothest autoscaling and lowest management overhead, and AKS makes the most sense alongside an existing Entra ID and Microsoft 365 environment.

Can we migrate from Docker Compose or a single server directly to Kubernetes?

Yes, but we usually recommend confirming the need first. If the reason for the move is genuine multi-service scale or multi-team operations, migration involves converting Compose services into Kubernetes manifests or Helm charts, setting up ingress and TLS, and validating autoscaling behavior under real load before cutting over production traffic.

What happens if our team doesn't have Kubernetes expertise after the project ships?

We document cluster architecture, hand over Helm charts and GitOps configuration rather than manual changes, and can scope an ongoing support arrangement for upgrades and incident response. Teams taking over a cluster for the first time should expect a learning period, which is part of why we recommend Kubernetes only when the operational investment is clearly justified.

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