Kubernetes Consulting

Kubernetes is an open-source container orchestration platform used by engineering teams to run containerized applications reliably at scale. It standardizes how services are deployed and operated across clusters, helping organizations automate day-to-day tasks like scheduling workloads, handling failures, and scaling capacity in response to demand. Kubernetes is commonly adopted by platform, DevOps, and application teams building microservices or modernizing legacy applications for cloud and hybrid environments.

It typically runs on public cloud, on-premises, or edge infrastructure, and is managed through declarative configuration (YAML) and controllers that continuously reconcile desired state with actual state. Kubernetes integrates with CI/CD workflows and common cloud-native tooling for delivery, networking, and observability.

• Automated scheduling and scaling of containerized workloads across nodes
• Rolling updates and rollbacks for safer application releases
• Service discovery and traffic routing via Services and Ingress
• Self-healing through health checks and automatic rescheduling
• Configuration and secrets management for environment-specific deployments

There are many advantages to using Orchestration tools:

• Improve the utilization of CPU, memory, and storage usage by running many processes on a single machine
• Manage the entire lifecycle of the orchestrated workloads: pre & post initialization & termination
• Control the scale of workloads and the scale of their underlying infrastructure separately
• Centralized management of workloads and infrastructure

Kubernetes is an open-source container orchestration system used to deploy, scale, and operate containerized applications across clusters. It is commonly chosen when teams need consistent runtime behavior, automation, and resilience for multi-service workloads.

• Declarative desired-state management, enabling repeatable deployments and automatic reconciliation when runtime state drifts from configuration.
• Built-in scheduling, placing workloads based on resource requests, constraints, affinity rules, and taints/tolerations.
• Horizontal scaling and autoscaling, adjusting replica counts based on metrics and demand to improve efficiency under variable load.
• High availability primitives, distributing replicas across nodes and zones and supporting disruption budgets for safer maintenance.
• Self-healing behavior, restarting failed containers and rescheduling pods when nodes become unhealthy or unavailable.
• Rolling updates and rollbacks, supporting controlled releases with health checks and fast reversion to a known-good version.
• Service discovery and internal load balancing, providing stable service endpoints and routing traffic only to healthy backends.
• Config and secret management, separating environment configuration from images and integrating with external secret stores.
• Persistent storage orchestration via CSI, attaching volumes across cloud and on-prem backends to support stateful services.
• Extensibility through CRDs and operators, enabling platform automation for domain-specific workflows and managed services.

Kubernetes is a strong fit for organizations running multiple services that need portability across environments and a large ecosystem of networking, security, and delivery tooling. The main trade-offs are platform complexity and the need for mature governance around RBAC, upgrades, observability, and cost controls.

Common alternatives include AWS ECS, HashiCorp Nomad, and Google Cloud Run, which can reduce operational overhead in single-cloud or fully managed scenarios but typically provide less control or portability than Kubernetes.

Our experience with Kubernetes has helped us build the practical knowledge, patterns, and tooling needed to support clients running containerized workloads reliably in production.

Some of the things we did include:

• Provisioned and upgraded Kubernetes clusters across AWS, GCP, Azure, and on-prem environments using Infrastructure-as-Code with Terraform and Pulumi
• Standardized application delivery with Helm charts and GitOps workflows using Argo CD, including multi-environment promotion and rollback strategies
• Implemented autoscaling for both clusters and workloads (HPA/VPA and node scaling), tuning requests/limits to improve stability and reduce cost
• Built observability stacks for metrics, logs, and alerts using Prometheus and Grafana, with actionable SLO-driven dashboards
• Hardened clusters with RBAC, network policies, secret management, image scanning, and admission controls to reduce operational and security risk
• Designed high-availability and disaster recovery setups, including multi-zone clusters, backup/restore for stateful workloads, and tested failover procedures
• Migrated legacy services into Kubernetes with minimal downtime, breaking monoliths into deployable components and establishing safe rollout patterns
• Implemented service-to-service traffic management and mTLS in multi-cluster environments using service mesh tooling such as Istio
• Optimized cluster performance by tuning ingress, resource quotas, pod disruption budgets, and scheduling constraints for predictable deployments
• Created runbooks, operational playbooks, and enablement sessions so internal teams could confidently operate Kubernetes day-to-day

This hands-on experience across different infrastructures and workload types helped us accumulate deep Kubernetes expertise and deliver secure, scalable, and maintainable cluster setups that teams can operate with confidence.

Some of the things we can help you do with Kubernetes include:

• Assess your current Kubernetes platform and deliver a state report covering cost/utilization, reliability, security posture, and operational risks.
• Create a practical adoption roadmap with prioritized improvements, effort estimates, and an implementation plan.
• Provision and standardize clusters using Infrastructure-as-Code with Terraform or Pulumi for repeatable, auditable environments.
• Implement deployment workflows with Helm and GitOps using ArgoCD to improve release consistency and reduce manual changes.
• Harden clusters with security guardrails, RBAC, network policies, secrets management, and compliance-ready controls.
• Set up observability for clusters and workloads (metrics, logs, and alerts) to speed up troubleshooting and reduce downtime.
• Optimize cost and performance through right-sizing and autoscaling with Cluster Autoscaler, Karpenter, KEDA, HPA, and VPA.
• Improve resiliency with high-availability designs, backup/restore strategies, and disaster recovery runbooks.
• Operate and continuously improve Kubernetes day-to-day, including upgrades, deprecations, incident response, and workload lifecycle management.
• Enable your team with hands-on training, documentation, and best-practice playbooks to run Kubernetes confidently.