Overview
Container Management is the end-to-end management of environments in which applications run as container images packaged with tools such as Docker. It spans everything from building and storing images to setting up Kubernetes clusters, from operating deployment pipelines to tracking security policies, capacity and version upgrades. In Indexline's managed service model, platform design, installation and day-to-day operation are consolidated under a single point of responsibility, while application teams can focus on their own code and business goals.
The importance of container technology comes from making the environment an application runs in portable and repeatable together with its code. The "it worked on my machine" problem disappears; development, test and production environments run the same image. Kubernetes orchestrates these images across tens or hundreds of servers, providing automatic scaling, self-healing and zero-downtime rolling updates. As a result, deployment frequency increases while error rates and recovery times decrease.
The technical ecosystem is broad and changes quickly. Kubernetes releases a new minor version roughly every four months, and each version is supported for a limited period; changes such as the removal of dockershim in Kubernetes 1.24 have made it necessary to move nodes to CRI-compatible runtimes such as containerd and CRI-O, or to the cri-dockerd adapter. On top of this, deployment tools such as Helm, Argo CD and Flux; network plugins such as Calico and Cilium; registries such as Harbor; and Prometheus-based monitoring components all work together. Keeping these components compatible and up to date requires dedicated expertise in its own right.
The risks of leaving a container platform unmanaged often accumulate silently. Clusters that are not updated remain on versions that are out of support and miss critical security patches. Images that are never scanned for vulnerabilities, over-privileged service accounts and exposed API endpoints widen the attack surface. Workloads without defined resource limits affect one another and cause unexpected outages, while idle nodes and incorrectly sized pods inflate the cloud bill unnecessarily.
Indexline treats container management not as a one-off installation project but as a continuously operated platform service. After assessing the existing application portfolio and team maturity, the right Kubernetes distribution is selected for an on-premise, cloud or hybrid environment; GitOps, image security, observability and backup are designed into the architecture from the start. After go-live, 24/7 monitoring, planned version upgrades, capacity and cost optimization, and regular reporting continue under an SLA guarantee.
How It Works
Discovery and Assessment: Existing applications, Docker usage, deployment processes and infrastructure inventory are reviewed. Container suitability, dependencies and team skills are reported.
Platform Architecture: Based on the on-premise, cloud or hybrid target, the Kubernetes distribution, cluster topology, network plugin, storage classes and namespace strategy are designed.
Cluster Setup: Clusters are built repeatably with IaC tools such as Terraform and Ansible. A highly available control plane, ingress and certificate management are configured.
Image and Registry Management: Dockerfiles are optimized, and multi-stage builds and minimal base images are adopted broadly. A private registry, vulnerability scanning and image signing are put in place.
CI/CD and GitOps: Application and infrastructure definitions are kept in a Git repository. An automated, auditable deployment pipeline is built with Helm or Kustomize packages via Argo CD or Flux.
Security and Policy: RBAC, Pod Security Standards, network policies, secret management and policy enforcement with Kyverno or OPA Gatekeeper are applied. Hardening is carried out according to the CIS Kubernetes Benchmark.
Observability and Backup: Cluster and application metrics are monitored with Prometheus, Grafana and a central logging infrastructure. Cluster resources and persistent data are backed up regularly with Velero.
Managed Operations: 24/7 monitoring, incident response, planned Kubernetes version upgrades, capacity planning and monthly cost and performance reporting are delivered under the SLA.
Key Features
Kubernetes Lifecycle Management
Indexline takes on cluster setup, node pool management, certificate renewal and planned upgrades to supported versions, eliminating the risk of being stuck on an unsupported version.
GitOps-Based Deployment
With Argo CD or Flux, the Git repository becomes the single source of truth. Every change is traceable, approvable and can be rolled back in a single step.
Image Security and Supply Chain
Image vulnerability scanning with tools such as Trivy, a Harbor-based private registry and image signing with Cosign ensure that only verified images reach production.
Policy and Access Control
Security standards are enforced automatically through RBAC, namespace isolation, network policies and Kyverno or OPA Gatekeeper rules.
Automatic Scaling
Capacity is adjusted dynamically to demand with the Horizontal Pod Autoscaler, Cluster Autoscaler and KEDA for event-driven scaling.
Multi-Cluster Management
Clusters in different environments and locations are managed from a single pane through central management layers such as Rancher, ensuring consistent policy and access.
Observability Package
Prometheus, Grafana, central log collection and alerting rules come ready out of the box. Performance visibility is provided at pod, node and application level.
Backup and Disaster Recovery
Cluster resources and persistent volumes are backed up with Velero; restore scenarios to a different cluster or location are tested regularly.
Benefits & Advantages
Deployment times drop from days to minutes; teams ship new features more often and with lower risk.
Because development, test and production environments run the same image, errors caused by environment differences decrease.
The burden of hiring and retaining Kubernetes experts disappears; platform operations are delivered under an SLA guarantee.
Planned version upgrades and continuous vulnerability scanning keep the platform current and secure.
Correct resource limits, right-sizing and automatic scaling keep infrastructure costs under control.
The self-healing architecture and zero-downtime version rollouts increase application availability.
With GitOps, every change is recorded, providing traceability for audit, compliance and KVKK (Turkish Personal Data Protection Law) requirements.
Industry Use Cases
Finance / Banking
Mobile banking back-end services were moved to Kubernetes; with GitOps and image signing, a weekly release cycle became daily deployment, and all changes were recorded for audit.
Retail / E-Commerce
During campaign periods, cart and payment services scaled automatically with HPA and Cluster Autoscaler; scaling capacity back down after peaks cut infrastructure costs by roughly 35%.
Healthcare
Integration services connected to the hospital information system were containerized on on-premise Kubernetes; patient data stayed in-country while version rollouts became zero-downtime.
Manufacturing
Lightweight Kubernetes clusters running on edge servers in factories came under management from a central console; application updates were rolled out to all plants from a single point.
Public Sector
A citizen services portal was migrated to a microservices architecture; CIS Benchmark hardening and network policies markedly reduced the number of findings in security audits.
Telecom
A fragmented estate of dozens of clusters was brought under central management; planned version upgrades left no unsupported Kubernetes versions, and incident response time was halved.
The Indexline Approach
The most common mistake in container transformation is trying to move every application to Kubernetes at once and in the same way. Indexline first assesses the application portfolio; stateless, frequently changing services move in the first wave, while a separate roadmap is drawn up for heavily stateful systems or those with licensing constraints. This delivers early wins quickly and lets teams adapt to the new way of working gradually.
We treat the platform itself like a product. The namespaces, CI/CD templates, observability and security defaults that application teams need are offered as a standard "golden path". Instead of dealing with infrastructure details every time, developers move forward self-service with approved templates, while the platform team safeguards consistency and security centrally. This model prevents the platform from sprawling even as the number of teams and applications grows, and markedly shortens the time it takes to bring a new service into production.
Security is not a layer added afterwards; it is the starting point of the design. The supply chain is verified from image source to deployment, RBAC is defined according to the principle of least privilege, pod-to-pod traffic is restricted with network policies, and secrets are kept in external vaults. This approach is aligned with Zero Trust principles and makes it easier to produce the audit evidence required for regulatory requirements such as KVKK.
The fast release cycle of the Kubernetes ecosystem is the most critical value point of the managed service model. Indexline first validates version upgrades on test clusters, identifies deprecated APIs in advance, and carries out production migrations in planned maintenance windows with a rollback plan. The compatibility of add-ons such as ingress controllers, network plugins and monitoring components with the new version is checked in the same cycle. This keeps the platform both current and predictable.
Finally, the value of a container platform must be measurable. Metrics such as deployment frequency, change failure rate, mean time to repair and resource utilization efficiency are reported regularly. In monthly reviews, cost, capacity and security findings are addressed together, and the platform is continuously improved in alignment with business goals.
Frequently Asked Questions
What is the difference between Docker and Kubernetes?
Docker is used to package an application together with its dependencies as a container image and run it on a single server. Kubernetes orchestrates these containers across multiple servers, managing scheduling, scaling, service discovery, self-healing and version rollouts. Images built with Docker run seamlessly on Kubernetes.
Will our Docker images keep working after Kubernetes 1.24?
Yes. dockershim, removed in Kubernetes 1.24, was the intermediary layer that allowed Docker Engine to be used directly as the runtime. Because images built with Docker comply with the OCI standard, they run the same way on containerd or CRI-O. The change mainly affects runtime configuration at node level; if you have monitoring or build tools that connect directly to the Docker socket, they also need to be reviewed.
Should we choose managed Kubernetes or our own installation?
Cloud provider services such as EKS, AKS or GKE take over control plane management and enable a fast start. Where data localization, cost or hardware control is required, on-premise deployments are more suitable. Indexline can manage cluster operations, security and application deployments in both models; the choice is made together based on workload and regulatory requirements.
How often should Kubernetes version upgrades be performed?
Kubernetes releases a new minor version roughly every four months, and each version receives patch support for about 14 months. Clusters therefore need to be upgraded at least once or twice a year. Upgrades should be done sequentially without skipping minor versions, removed APIs should be identified in advance, and changes should be validated in a test environment first.
Is it a good idea to run databases in containers?
Thanks to StatefulSets, persistent volumes and operators, databases can run on Kubernetes; however, this requires storage performance, backup and operational maturity. For critical, high-volume databases, a managed database service or dedicated servers are often more practical. The decision is evaluated workload by workload.
How is container security ensured?
Security is handled in layers: minimal base images and vulnerability scanning, image signing, running as a non-root user, Pod Security Standards, RBAC, network policies and external secret management. At runtime, suspicious behavior is detected with tools such as Falco, and logs can be forwarded to the SIEM.
Do we need to rewrite our existing applications?
Not always. Many applications can be containerized with small code changes; steps such as moving configuration into environment variables and writing logs to standard output are often enough. Monolithic applications can first be moved as they are, and then the parts that need it can gradually be split into microservices.
How are responsibilities shared within the service scope?
Indexline takes on the platform layer under the SLA: cluster health, version upgrades, security policies, monitoring and backup. Application code and business logic remain with the customer's teams. Responsibility boundaries and escalation steps are clarified at the outset with a RACI matrix.