• When a container crashes at 3am, does it restart automatically or does someone have to log in and start it manually?

  • Are your services deployed to a single instance with no automatic failover -- so a hardware failure takes down the whole application?

Running containers in production without orchestration is running containers on borrowed time.

Kubernetes handles the problems that become painful when you run multiple containers across multiple servers: scheduling containers to available nodes, restarting them when they crash, distributing traffic across healthy instances, scaling the number of replicas up and down based on load, and rolling out updates without downtime. Without orchestration, all of these become manual operations.

We design and implement Kubernetes infrastructure on AWS EKS, Google GKE, Azure AKS, or self-managed clusters. Cluster setup, workload migration from existing infrastructure, RBAC configuration, networking, storage, autoscaling, and the operational practices that make Kubernetes manageable rather than a second job.

  • Managed Kubernetes on EKS, GKE, or AKS -- or self-managed with kubeadm for environments with specific requirements

  • Horizontal pod autoscaling configured for your traffic patterns -- scale up under load, scale down to reduce cost when traffic drops

  • Zero-downtime rolling deployments with configurable rollout speed and automatic rollback on health check failure

  • RBAC and namespace configuration so every team has access to what they need and nothing they don't

RaftLabs designs and builds Kubernetes infrastructure on AWS EKS, Google GKE, Azure AKS, and self-managed clusters -- workload migration, RBAC, autoscaling, networking, and the operational practices that make Kubernetes manageable. For teams running containerised applications that need orchestration. Most projects deliver in 6 to 12 weeks at a fixed cost.

Vodafone
Aldi
Nike
Microsoft
Heineken
Cisco
Calorgas
Energia Rewards
GE
Bank of America
T-Mobile
Valero
Techstars
East Ventures

A container running on a single server with no orchestration is a single point of failure waiting for its moment. When the server restarts, the container doesn't come back unless someone manually starts it. When traffic spikes, the container is constrained to the resources of one machine. When you deploy a new version, you take the old one down and bring the new one up -- with a window where nothing is serving traffic. These are not theoretical problems. They are the operational reality of running containers without Kubernetes.

Kubernetes solves each of these problems by treating containers as workloads that should always be running at a specified replica count, on whatever nodes have available capacity, updated through a controlled rollout that keeps healthy replicas serving traffic throughout. The operational investment is real -- Kubernetes is not simple -- but the problems it solves are also real, and for applications with multiple services and variable traffic the tradeoff is typically correct.

What we build

Kubernetes cluster setup

Managed cluster provisioning on EKS, GKE, or AKS with Terraform so the cluster configuration is version-controlled and reproducible. Node pool configuration for different workload types -- compute-optimised for API services, memory-optimised for data processing. Cluster version upgrade planning with documented procedures. Multi-availability-zone configuration so a single AZ failure does not take down the cluster.

Workload containerisation and migration

Dockerfile development for applications that aren't already containerised. Kubernetes manifest development -- Deployments, Services, ConfigMaps, Secrets -- with resource requests and limits set from profiling rather than guessing. Migration of existing workloads from VMs or bare metal to Kubernetes without service interruption. Helm chart development for applications with multiple deployment configurations across environments.

Autoscaling configuration

Horizontal Pod Autoscaler configuration based on CPU, memory, or custom metrics from your application. Cluster Autoscaler to add and remove nodes based on pending pod demand -- so you're not paying for idle capacity during low-traffic periods. Vertical Pod Autoscaler recommendations for right-sizing resource requests. Load testing to validate scaling behaviour before production traffic hits the new configuration.

Networking and ingress

Ingress controller setup (NGINX, Traefik, or AWS ALB Ingress) with TLS termination and certificate management via cert-manager and Let's Encrypt. Network policy configuration to restrict pod-to-pod communication -- services can only talk to what they need to. Service mesh evaluation (Istio, Linkerd) for teams that need mutual TLS and traffic management between services. DNS configuration for internal and external service discovery.

Storage and stateful workloads

PersistentVolume and StorageClass configuration for stateful applications that need durable storage. StatefulSet deployment for databases and queues that need stable network identities and persistent storage across pod restarts. Backup strategy for persistent volumes with tested restore procedures. Operator-based deployment for PostgreSQL, Redis, and Kafka where appropriate -- so day-two operations like failover and scaling are handled by the operator rather than manually.

Security and RBAC

Role-Based Access Control configuration: namespaces per team or environment, roles that grant minimal required permissions, service account management for workloads that need to call the Kubernetes API. Pod Security Standards enforcement to prevent privileged containers and host network access. Image scanning integration in CI to block deployments of images with known critical vulnerabilities. Secrets management via Kubernetes Secrets with external secret operator or HashiCorp Vault.

Have a containerisation or orchestration project?

Tell us your current infrastructure, what you're running, and what operational problem you're trying to solve. We'll scope the Kubernetes setup and give you a fixed cost.

Talk about your Kubernetes project

Frequently asked questions

Kubernetes is overkill for a single-service application running on one or two servers with stable traffic. It adds operational complexity that isn't justified when the problems it solves -- multi-replica scheduling, auto-healing, auto-scaling -- either don't apply or could be solved more simply. Kubernetes is the right choice when you run multiple services that need independent scaling, when traffic is variable enough that auto-scaling saves meaningful cost, when you need zero-downtime deployments across multiple instances, or when you're targeting a cloud provider that offers managed Kubernetes at a cost that makes it simpler to operate than a VM fleet. We give you an honest answer on whether it fits before scoping anything.

EKS (AWS) is the natural choice if your existing infrastructure is on AWS -- it integrates with IAM, ALB, EBS, and EFS without extra configuration work. GKE (Google Cloud) has the most mature managed Kubernetes offering and is the right choice if you're already in GCP or want features like Autopilot (fully managed node pools). AKS (Azure) is the choice if your organisation is Azure-first. Self-managed Kubernetes makes sense when you have on-premise infrastructure or compliance requirements that restrict cloud provider options. The choice follows your existing cloud presence and compliance requirements, not Kubernetes capability differences.

Kubernetes releases a new minor version approximately every four months and supports each version for about 14 months. Managed services (EKS, GKE, AKS) handle the control plane upgrade. The node pool upgrade -- replacing nodes running the old version with nodes running the new version -- requires draining old nodes (evicting pods to other nodes) and provisioning new ones. With multiple replicas per workload and a properly configured PodDisruptionBudget that guarantees at least one replica stays running during eviction, node upgrades complete without service interruption. We establish the upgrade process and PodDisruptionBudget configuration during initial cluster setup.

A cluster setup covering a single environment with containerisation of existing workloads, autoscaling, networking, and RBAC typically runs $25,000 to $60,000. A multi-environment setup (dev, staging, production) with full workload migration, service mesh, and GitOps-based deployment workflow typically runs $60,000 to $120,000. Fixed cost agreed before development starts. Ongoing AWS/GCP/Azure infrastructure costs are separate and depend on your workload size.