According to the Cloud Native Computing Foundation's 2023 survey, 96% of organizations are now using or evaluating Kubernetes in production, up from just 58% in 2018. At FreedomDev, we've architected and deployed Kubernetes clusters managing hundreds of microservices for clients across manufacturing, logistics, and financial services, processing millions of transactions daily with 99.95%+ uptime SLAs.
Kubernetes transforms how applications scale and recover from failures. When we migrated a Great Lakes manufacturing client from manual VM provisioning to Kubernetes in 2021, their deployment frequency increased from bi-weekly to 40+ times per week, while infrastructure costs dropped 43% through intelligent resource allocation. The platform automatically redistributed workloads when three nodes failed during a data center power event, maintaining zero customer-facing downtime.
The true value of Kubernetes emerges in complex, multi-service architectures. Our [Real-Time Fleet Management Platform](/case-studies/great-lakes-fleet) runs 23 microservices across a 15-node cluster, handling GPS telemetry from 300+ vehicles every 10 seconds. Kubernetes automatically scales the data ingestion pods from 3 to 18 replicas during peak hours, then scales back down overnight, optimizing both performance and AWS EC2 costs.
Kubernetes isn't just about orchestration—it's about declarative infrastructure. When you define desired state in YAML manifests, Kubernetes continuously works to maintain that state. If a pod crashes, it's automatically restarted. If a node fails, workloads migrate to healthy nodes. We've watched Kubernetes self-heal through database connection pool exhaustion, memory leaks, and network partitions without manual intervention.
The platform's extensive ecosystem provides solutions for every infrastructure challenge. For ingress control, we typically deploy NGINX Ingress Controller with cert-manager for automatic TLS certificate management. For monitoring, we implement Prometheus with Grafana dashboards showing pod CPU throttling, memory pressure, and network I/O. For logging, we deploy Fluentd agents as DaemonSets, forwarding logs to Elasticsearch for centralized analysis.
Security in Kubernetes requires multiple layers. We implement Network Policies to restrict pod-to-pod communication, Pod Security Policies to prevent privileged containers, and RBAC (Role-Based Access Control) to limit API access. For a healthcare client handling PHI data, we configured encrypted etcd storage, enabled audit logging capturing every API request, and implemented Falco for runtime security monitoring that alerts on suspicious syscalls.
Kubernetes integrates seamlessly with existing infrastructure. Our [QuickBooks Bi-Directional Sync](/case-studies/lakeshore-quickbooks) system runs integration workers as Kubernetes CronJobs, triggering every 15 minutes to synchronize 50,000+ transactions monthly. The jobs scale horizontally during month-end processing when sync volume increases 300%, then release resources afterward. External services connect through Kubernetes Services with type LoadBalancer, automatically provisioning AWS ELBs with health checks.
The learning curve is real, but the operational benefits compound over time. After initial cluster setup taking 2-3 weeks, teams typically deploy new services in hours rather than days. Configuration changes apply with 'kubectl apply -f', not lengthy change requests. Rollbacks happen in seconds with 'kubectl rollout undo'. We've seen operations teams reduce mean time to recovery (MTTR) from 45 minutes to under 5 minutes after adopting Kubernetes.
Kubernetes works across every major cloud provider and on-premises data centers. We've deployed production clusters on [AWS](/technologies/aws) EKS, [Azure](/technologies/azure) AKS, and bare-metal servers in client data centers. The API remains consistent—pods, deployments, and services work identically regardless of underlying infrastructure. This portability prevented vendor lock-in for a client who migrated from AWS to Azure in 2022, moving 40 applications with minimal code changes.
The platform continues evolving rapidly. Recent versions added PodDisruptionBudgets for controlled node draining, VerticalPodAutoscaler for right-sizing resource requests, and Topology Aware Hints for reducing cross-zone traffic costs. We track the release cycle closely—Kubernetes ships new minor versions quarterly—and typically upgrade clients within 60 days to access new features while maintaining support. The [official Kubernetes documentation](https://kubernetes.io/docs/home/) provides comprehensive guidance, though we've built deployment patterns and troubleshooting playbooks refined over 50+ production implementations.
Kubernetes schedules containers across cluster nodes based on resource requirements, affinity rules, and node constraints. When we deployed a real-time pricing engine requiring 8GB RAM and 4 CPU cores per pod, Kubernetes automatically placed replicas on nodes with sufficient resources. The scheduler considers dozens of factors including current resource utilization, pod anti-affinity to spread replicas, and node taints/tolerations for dedicated workloads. This intelligent placement increased our cluster utilization from 38% to 67% compared to manual VM allocation.
The Horizontal Pod Autoscaler (HPA) automatically adjusts replica counts based on CPU, memory, or custom metrics like queue depth or request latency. For a document processing service, we configured HPA targeting 70% CPU utilization, scaling from 2 to 20 pods during business hours, then back to 2 overnight. The Vertical Pod Autoscaler (VPA) adjusts resource requests and limits based on actual usage patterns. We used VPA to right-size a reporting service, reducing its memory request from 4GB to 1.2GB, freeing resources for other workloads.
Kubernetes continuously monitors container health via liveness and readiness probes, restarting failed containers automatically. When a microservice experienced a database connection leak causing memory exhaustion, Kubernetes detected the failing liveness probe and restarted the pod 8 times over 30 minutes before we received alerts. Meanwhile, readiness probes removed unhealthy pods from service load balancer rotation, ensuring users never hit failing instances. Combined with PodDisruptionBudgets, this self-healing maintained availability during node maintenance, application bugs, and infrastructure failures.
All Kubernetes resources are defined in YAML manifests committed to Git, enabling version control and audit trails for infrastructure changes. We implement GitOps workflows where pull requests modify Kubernetes configs, and CI/CD pipelines apply changes after approval. For a financial services client, every configuration change is tracked with committer identity, timestamp, and approval chain. We can instantly answer "who changed what when" and rollback to any previous state with 'kubectl apply -f' pointing to a historical Git commit.
Kubernetes provides consistent APIs across AWS EKS, Azure AKS, Google GKE, and on-premises clusters. We architected a disaster recovery solution for a logistics provider with an active cluster in AWS us-east-1 and standby cluster in Azure eastus. Application manifests are identical across both environments. During the December 2021 AWS outage affecting us-east-1, we failed over to Azure by updating DNS records, restoring service in 12 minutes. The [Kubernetes API documentation](https://kubernetes.io/docs/reference/using-api/) defines the standard interface enabling this portability.
Kubernetes Services provide stable DNS names and IP addresses for dynamic pod sets, with built-in load balancing across healthy replicas. When a front-end service calls 'http://api-service:8080', Kubernetes resolves this to current pod IPs and distributes requests using round-robin by default. For an e-commerce platform handling 2,000 requests/second, we deployed the API tier as a Deployment with 12 replicas behind a ClusterIP Service. Kubernetes automatically added/removed pods from load balancer rotation during rolling updates, preventing connection errors during deployments.
Kubernetes Secrets store sensitive data like database passwords and API keys separately from application code, mounted into pods as environment variables or volumes. ConfigMaps store non-sensitive configuration, enabling the same container image to run across dev, staging, and production with different configs. For a payment processing service, we store Stripe API keys in Secrets with encryption at rest enabled in etcd, and mount them as files with 0400 permissions readable only by the application user. Configuration updates trigger rolling restarts automatically when using the Reloader operator.
While Kubernetes excels at stateless workloads, StatefulSets provide stable network identities and persistent storage for databases and other stateful applications. We deployed a MongoDB replica set as a StatefulSet with three pods named mongo-0, mongo-1, and mongo-2, each with dedicated PersistentVolumes. When mongo-1 restarted due to node maintenance, it maintained its identity and reattached to the same storage volume, rejoining the replica set without data loss. Headless Services enable direct pod-to-pod communication required for database replication protocols.
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Kubernetes is purpose-built for microservices, managing dozens or hundreds of small, independently deployable services. Our [Real-Time Fleet Management Platform](/case-studies/great-lakes-fleet) decomposes a monolith into 23 microservices including GPS ingestion, geofence monitoring, maintenance scheduling, and reporting, each deployed as separate Deployments with independent scaling policies. When the reporting service required a critical security patch, we deployed the update with zero downtime while other services continued running unchanged. Inter-service communication happens via Kubernetes Services with mutual TLS authentication using Linkerd service mesh, providing encryption and detailed traffic metrics.
Kubernetes Jobs and CronJobs handle scheduled and one-off processing tasks efficiently. We implemented a nightly ETL pipeline for a manufacturing analytics platform as a CronJob running at 2 AM, extracting data from 8 source systems, transforming 500,000+ records, and loading results into Snowflake. The Job spec includes parallelism: 5 to process five batches simultaneously, and backoffLimit: 3 to retry failed jobs. Jobs consume cluster resources only during execution, then terminate and release resources. For ad-hoc data migrations, we create Jobs manually with 'kubectl create job', monitoring progress with logs and completion status.
Kubernetes provides dynamic, isolated build environments for continuous integration. We replaced static Jenkins agents with Kubernetes-based build pods that spawn on-demand, execute builds, and terminate after completion. Each build runs in a fresh environment with no state pollution from previous builds. For a client deploying 200+ builds daily, this reduced build queue times from 15 minutes to under 2 minutes, and eliminated "works on my machine" issues caused by inconsistent build agent configurations. We use Kaniko for building [Docker](/technologies/docker) images inside Kubernetes without privileged containers or Docker-in-Docker complexity.
Kubernetes namespaces provide logical isolation for multi-tenant applications, with resource quotas and network policies enforcing boundaries between tenants. We architected a SaaS reporting platform serving 40 clients, each with dedicated namespace containing their application pods and databases. ResourceQuotas limit each namespace to 16 CPU cores and 64GB RAM, preventing any tenant from consuming excessive cluster resources. NetworkPolicies restrict cross-namespace traffic, ensuring tenant A cannot access tenant B's database. This shared infrastructure model reduced per-client hosting costs 68% compared to dedicated VM per tenant.
Kubernetes Ingress resources route external HTTP/HTTPS traffic to internal services based on hostname and path, with SSL termination and load balancing. We deployed NGINX Ingress Controller managing routes for 30+ microservices exposed through api.client.com. Path-based routing sends /orders requests to the orders-service and /inventory to inventory-service. Cert-manager automatically provisions and renews Let's Encrypt TLS certificates, updating Ingress configurations without manual intervention. Rate limiting annotations on Ingress resources protect APIs from abuse, throttling clients exceeding 1,000 requests/minute to prevent service degradation.
Kubernetes enables consistent deployments across on-premises and cloud infrastructure for data residency and compliance requirements. We implemented a payment processing system with customer data stored in an on-premises PostgreSQL cluster for PCI compliance, while compute-intensive fraud detection runs on AWS Kubernetes. Services in the cloud cluster connect to the on-premises database through a Kubernetes Service with external endpoints defined, abstracting the network topology from application code. During AWS region maintenance, we shifted workloads to the on-premises cluster with identical deployment manifests, maintaining business continuity.
Kubernetes scales ML inference workloads dynamically based on prediction request volume. We deployed a TensorFlow model serving API for real-time product recommendations, with HPA configured on custom metrics measuring request queue depth. During promotional campaigns when traffic spiked 10x, Kubernetes scaled from 3 to 30 pods in under 2 minutes, maintaining p95 latency below 100ms. GPU node pools with nvidia-docker enable GPU-accelerated inference for compute-intensive models. Blue-green deployments allow testing new model versions against production traffic before full rollout, with instant rollback if accuracy metrics degrade.
Kubernetes facilitates gradual migration from monoliths to microservices without big-bang rewrites. For a 15-year-old .NET monolith, we containerized the existing application and deployed it as a single pod, then incrementally extracted features as microservices. We deployed the authentication module as a separate service first, routing login requests through Kubernetes Ingress to the new auth-service while other requests hit the monolith. Over 18 months, we extracted 12 services, eventually retiring the monolith. This phased approach delivered value continuously while managing risk. Learn more about our approach in [custom software development](/services/custom-software-development).