Cloud & Infrastructure
Kubernetes
We build and run Kubernetes platforms for teams that genuinely need one — and we will tell you plainly when a managed PaaS or a couple of VMs is the cheaper, better answer.
Overview
Kubernetes is a container orchestrator: you describe the state you want — this many replicas of this image, exposed on this port, with these resources and these secrets — and a control loop works continuously to make reality match that description. If a container dies, it is replaced. If a node fails, its work is rescheduled elsewhere. If demand rises, more replicas appear. That declarative, self-healing model is the reason Kubernetes has become the default substrate for running containers at scale across every major cloud.
The core objects are worth naming because they are the vocabulary of everything else: pods (one or more containers scheduled together), deployments (declarative management of replica sets and rolling updates), services (stable networking in front of ephemeral pods), ingress (HTTP routing from outside the cluster), and configmaps and secrets (configuration and credentials kept out of the image). On top of those, the operator pattern and custom resource definitions let you teach the same control-loop model about your own domain — a database, a certificate, a message broker — so that operational knowledge lives in code rather than in a runbook.
We use Kubernetes where it earns its keep: multi-service systems, real scaling needs, several teams shipping independently, or a platform that has to run identically across environments and clouds. We are equally direct about the other case. Kubernetes is powerful and it is heavy, and for a large number of workloads it is straightforward over-engineering. Much of our Kubernetes work begins with the question of whether you should be running it at all.
Best for — Multi-service systems with real scaling and reliability needs, run by a team willing to own the platform underneath them.
Why teams choose Kubernetes
Self-healing by default
The control loop restarts failed containers, reschedules off dead nodes and keeps the declared replica count without anyone paged at 3am.
One platform, any cloud
The same manifests run on EKS, GKE, AKS or on-prem, so the platform is portable and environments stop drifting apart.
Scaling as configuration
Horizontal autoscaling and rolling or blue-green deploys are declarative settings, not bespoke scripts written and rewritten per service.
Why businesses choose Kubernetes
- When you genuinely run many services, Kubernetes replaces a pile of bespoke deploy scripts and cron-driven glue with one consistent, declarative model that the whole team can reason about.
- Its self-healing and autoscaling turn a class of 3am incidents into non-events, which is worth real money once uptime is a business requirement rather than an aspiration.
- Portability across EKS, GKE, AKS and on-prem keeps you off a single vendor’s bespoke platform, so the cost of moving or running multi-cloud stays bounded.
- The operator and CRD model means hard-won operational knowledge lives in version-controlled software that survives the person who wrote it, instead of in a wiki page nobody trusts.
What we build with Kubernetes
The capabilities this technology is genuinely strong at — and what we most often build with it.
Declarative desired-state config
The whole platform described as versioned YAML and applied through GitOps — the cluster is reconciled to what is in git, so the repository is the source of truth.
Deployments & rollout strategies
Rolling updates by default, blue-green or canary where a release needs a safer path, all with automatic rollback when health checks fail.
Horizontal & cluster autoscaling
Pods scaled on CPU, memory or custom metrics, and nodes scaled underneath them, so the fleet grows and shrinks with demand instead of running for peak all day.
Operators & CRDs
Custom controllers that manage stateful systems — databases, brokers, certificates — by the same reconcile-to-desired-state loop, encoding failover and upgrades as software.
Ingress & service networking
Stable service discovery in front of ephemeral pods, HTTP routing and TLS termination at the ingress, and a service mesh where mutual TLS and fine-grained traffic control are warranted.
Config, secrets & storage
Configuration and credentials kept out of images via configmaps, secrets and an external secrets store, with persistent volumes and storage classes for the workloads that need state.
Use cases
Microservices platforms
Dozens of independently deployed services with their own scaling, health and rollout behaviour, sharing one consistent runtime instead of one-off deploy pipelines each.
Multi-cloud and hybrid
A single platform that runs identically across clouds or across cloud and on-prem, so workloads move where cost, latency or compliance dictate without a rewrite.
Bursty and batch workloads
Event-driven and batch jobs that scale up hard under load and scale back to near nothing when idle, paying for capacity only while it is needed.
Internal developer platforms
A self-service layer where product teams ship to a paved road — standard manifests, CI/CD and guardrails — without each team reinventing infrastructure.
When Kubernetes is the right choice
- Right when you run many services that need independent scaling, deployment and failure isolation across a fleet of machines
- Right when the same platform must run identically across clouds or across dev, staging and production without per-environment drift
- Right when the operator pattern lets you encode real operational knowledge — failover, backups, upgrades — as software rather than tribal runbooks
- Wrong for a single app or a small team with modest traffic, where a managed PaaS, ECS or a couple of well-run VMs does the job at a fraction of the cost and cognitive load
- Wrong when nobody will own the platform — Kubernetes assumes an engineering function to run it, and an unowned cluster becomes a liability faster than almost any other infrastructure choice
Kubernetes: pros and cons
Strengths
- The industry-standard orchestrator with managed offerings on every major cloud (EKS, GKE, AKS) and an enormous ecosystem
- Genuine self-healing, autoscaling and zero-downtime rollouts once the platform is set up correctly
- The operator pattern and CRDs let you extend the control-loop model to your own systems and encode operations as code
- Portable and vendor-neutral — the same primitives behave the same way across clouds and on-premises
Trade-offs
- Operationally heavy: it assumes a platform team, and running it well is a standing cost, not a one-off setup
- Frequently massive over-engineering for a small team — a managed PaaS, ECS or plain VMs is often the right, cheaper answer
- Stateful workloads are markedly harder than stateless ones; databases and their storage, backups and failover need real care
- A deep learning curve with many moving parts — networking, storage, RBAC and upgrades each carry their own sharp edges
Control plane and the reconcile loop
A Kubernetes cluster is a control plane governing a set of worker nodes. The control plane holds the API server (the single front door and source of truth, backed by etcd), the scheduler (which decides where pods run), and a set of controllers that each watch for a difference between declared and actual state and act to close it. That reconcile loop is the whole idea: you never tell Kubernetes to do a thing, you tell it what should be true, and controllers work continuously to keep it true. Understanding that distinction is the difference between operating the platform and fighting it.
We design clusters around blast radius and ownership. Namespaces, resource quotas and RBAC divide the cluster so one team or one workload cannot starve or reach another. Stateless services are treated as disposable and scaled freely; stateful ones are given deliberate storage, backup and failover design rather than being dropped in and hoped over. The operator pattern is how we extend that model to bespoke systems — a custom controller that knows how to run your database or broker correctly, so the knowledge is in code that runs every reconcile rather than in a runbook that runs when someone remembers.
Scheduling, resources and autoscaling
Kubernetes performance is mostly a resource-management story. Every container carries requests (what it is guaranteed) and limits (what it may not exceed), and the scheduler bin-packs pods onto nodes based on those requests. Set them too high and you pay for idle capacity across the whole fleet; set them too low and pods get throttled or evicted under load. We size these from measured behaviour, not guesswork, and revisit them — most clusters we inherit are quietly over-provisioned by a wide margin, which is real money burning on empty headroom.
Above the individual pod, the horizontal pod autoscaler adds and removes replicas on CPU, memory or custom metrics, while the cluster autoscaler adds and removes nodes underneath. Tuned together with sensible readiness and liveness probes, pod disruption budgets and anti-affinity rules, the platform absorbs traffic spikes and node failures without manual intervention and without running peak capacity around the clock. The failure mode to avoid is autoscaling configured against metrics that do not actually track load, which produces expensive thrashing — so we validate that the signal is the right one before trusting it.
RBAC, network policy and workload hardening
Kubernetes is secure only when it is configured to be; the defaults are permissive because they optimise for getting started. We close that gap in layers. RBAC is scoped to least privilege so identities and service accounts can do only what they must. Network policies are default-deny, so pods talk only to the peers they are explicitly allowed to reach rather than the flat, wide-open pod network you get out of the box. Pod security standards restrict privileged containers, host access and root, so a compromised workload has far less room to move.
The rest is supply chain and secrets. Images are scanned for known vulnerabilities in the pipeline and pulled from trusted registries; secrets live in a managed store injected at runtime rather than baked into images or committed to git; and the control plane, etcd and node access are locked down and audited. None of this is exotic — it is the fundamentals applied consistently and wired into delivery, so each release ships defensible instead of being hardened after an incident makes the case for it.
Scaling out — and the honest cost of it
Horizontal scaling is where Kubernetes is genuinely strong. Stateless services scale to hundreds or thousands of replicas across a growing node pool with little more than a configuration change, rollouts happen without downtime, and failed nodes are drained and replaced without anyone touching a keyboard. For systems that must grow elastically and stay available while they do, this is precisely what you are buying.
Statefulness is where the honesty has to come in. Databases, queues and anything holding data are markedly harder on Kubernetes than stateless services — StatefulSets, persistent volumes, storage classes, backups and failover all have to be designed rather than assumed, and getting them wrong loses data rather than just uptime. For many teams the right architecture is a stateless workload on Kubernetes talking to managed data services outside it, and we will often recommend exactly that. The other real cost is human: a Kubernetes platform assumes people to run it. Before we scale anyone onto it, we make sure that ownership exists, because an unowned cluster does not scale — it decays.
Kubernetes integrations & ecosystem
The technologies we most often pair with it — each links to how we work with it.
How we deliver Kubernetes
We start with the question most vendors skip: do you actually need Kubernetes? We look at your services, traffic, team and roadmap and give a straight answer — sometimes it is a managed PaaS or ECS, sometimes it is Kubernetes on a managed control plane, and we would rather tell you the cheaper thing is right than sell you a platform you will resent running. Where Kubernetes is the answer, we default to a managed offering (EKS, GKE or AKS) so you are not also maintaining a control plane by hand.
From there the work is infrastructure as code end to end: cluster and networking in Terraform, workloads as version-controlled manifests applied through GitOps, RBAC and network policy in from day one rather than retrofitted, and observability — metrics, logs and traces — wired before the first real workload lands so you can see what the platform is doing. We build the platform your team can actually operate, document it in runnable form, and hand it over with the knowledge to run it, because we would rather you owned it than depended on us to keep it alive.
The service behind it
Delivered throughCloud EngineeringWhat we build with Kubernetes
The disciplines this technology most often shows up in — from a first build to taking over and stabilising an existing one.
How we deliver
- 01
Discover
We map the system, the constraints and the business it serves — including the parts nobody documented.
Architecture brief
- 02
Architect
Decisions get made, written down and defended before a line of production code exists.
Decision records
- 03
Build
Short cycles against working software. You see progress in the product, not in a status deck.
Shipping increments
- 04
Operate
Monitoring, incident response and iteration. The system is alive, so the engagement is too.
Runbooks & SLOs
Industries we use Kubernetes in
Domain knowledge changes what gets built. A few of the sectors we know before the first meeting.
Also in Cloud & Infrastructure
Related terms
Why teams choose us for Kubernetes
We run what we build
We operate Kubernetes in production ourselves, so our advice is shaped by 3am pages and real bills, not by slideware. That is why we will happily tell you not to use it.
Senior engineers only
Cluster design, RBAC, networking and operators are unforgiving of inexperience. The people who architect your platform are the ones who have run one before, not juniors learning on your infrastructure.
Honest about over-engineering
A large share of Kubernetes projects should never have been Kubernetes projects. We start by testing that assumption, and recommend the simpler, cheaper platform whenever it is the right one.
Handover, not lock-in
We build platforms your team can own, documented and reproducible, and we would rather leave you self-sufficient than turn a cluster into a dependency on us.
Typical timeline
- 01
Assessment & decision
One to two weeks reviewing workloads, team and requirements to decide whether Kubernetes is right — and, if not, what is. This is where we save you the most money.
- 02
Platform build
Managed cluster, networking, RBAC, secrets, CI/CD and observability stood up as infrastructure as code, with the paved road your teams will actually deploy onto.
- 03
Workload migration
Services containerised and moved incrementally, stateless first, with stateful systems designed deliberately or pointed at managed data services outside the cluster.
- 04
Hardening & handover
Autoscaling tuned, cost right-sized, security policies verified, runbooks written in runnable form, and the platform handed to a team equipped to own it.
How pricing works
- Kubernetes engagements are priced by scope and cluster complexity — number of services, stateful workloads, environments and clouds, and how much of the platform already exists versus needing to be built from scratch.
- The larger cost is not the setup; it is the standing operational load. We are explicit about that up front, including whether a managed platform or a simpler architecture would leave you spending far less over the life of the system.
- We work fixed-scope for a defined build (cluster, pipelines, a set of workloads migrated) and on a retained basis where you want senior hands on the platform ongoing — reviewing, tuning cost, and being available when it matters.
Hire Kubernetes engineers
Need Kubernetes capacity on your own team? We embed named senior engineers into your existing team — reporting to your leads, working in your rituals — so you add capacity without a hiring cycle.
Hire Kubernetes engineersCommon questions
Do we actually need Kubernetes, or is it overkill for us?
For many teams it is overkill, and we will say so. If you run one or a few services with modest traffic and a small team, a managed PaaS, ECS or a couple of well-run VMs is usually cheaper, simpler and more reliable for you than Kubernetes. It earns its complexity when you have many services, real and variable scaling needs, multiple teams deploying independently, or a hard requirement to run identically across clouds. We start every engagement by pressure-testing whether you are in that group — and are glad to talk you out of it when you are not.
Should we run our own cluster or use EKS, GKE or AKS?
Almost always a managed offering. EKS, GKE and AKS run the control plane for you, which removes a large, thankless class of work — etcd maintenance, control-plane upgrades, availability of the API server — that self-managed clusters make your problem. You still own your workloads, networking, security and cost, which is plenty. We only recommend self-managed or bare-metal Kubernetes when there is a specific reason the managed platforms cannot meet, such as particular hardware, data-residency or air-gapped requirements.
Can we run our databases on Kubernetes?
You can, but stateful workloads are meaningfully harder than stateless ones and the failure mode is lost data rather than just downtime. Persistent volumes, StatefulSets, backups and failover all have to be designed carefully, and operators help but do not remove the responsibility. For most teams we recommend running stateless workloads on Kubernetes and pointing them at managed data services — a managed Postgres or Redis — outside the cluster. Where a database genuinely belongs in-cluster, we use a mature operator and design storage and recovery deliberately.
What does it actually cost to run Kubernetes over time?
More than the compute bill, and that is the part people underestimate. Kubernetes assumes an engineering function to run it — someone owning upgrades, security, cost and incidents. That standing operational load is the real cost, and it is why an unowned cluster becomes a liability. On top of it, clusters are commonly over-provisioned, so resources are quietly burning money on idle headroom. We size honestly, tune autoscaling and requests to real usage, and factor the human cost into the recommendation — including telling you when a simpler platform would cost far less across the life of the system.
How do you keep a Kubernetes cluster secure?
In layers, because the defaults are permissive by design. RBAC is scoped to least privilege so identities can do only what they must; network policies are default-deny so pods reach only the peers they are explicitly allowed to; and pod security standards keep workloads from running privileged or as root. Alongside that we scan images for vulnerabilities in the pipeline, keep secrets in a managed store injected at runtime rather than baked into images or git, and lock down and audit control-plane and node access. It is the fundamentals applied consistently and wired into delivery, so each release ships defensible rather than hardened after an incident.
Building on Kubernetes?
A technical conversation with the engineers who would do the work. If we are not the right fit, we will say so on the call.