Kubernetes is an open-source platform designed to efficiently manage containerized applications and services.

Originally developed by Google and now part of the Cloud Native Computing Foundation (CNCF), it has become the standard for deploying and operating containers in production environments.

You might ask yourself: what is kubernetes used for? In short, this platform simplifies the management of containerized applications by automating key tasks such as deployment, scaling, and failover.

By the end of this list, you will probably feel a little more inclined to adopt managed Kubernetes for container orchestration, which can be easily used on servers deployed at Latitude.sh.

Automation, obviously

Before Kubernetes and container orchestrators, deploying applications meant manually managing individual servers for each app, which involved setting up dependencies, configuring networking, and ensuring security.

This was time-consuming, skill-intensive, and prone to failure—if a server went offline, all configurations would be lost, resulting in downtime and extensive manual intervention to restore functionality.

Containers streamlined this by isolating applications from the underlying machine, allowing multiple applications to run on the same server without interference.

If one container crashes, it doesn’t impact others, creating a more resilient setup where applications remain operational even if individual components fail.

Kubernetes takes this further by automating container management and enabling applications to scale seamlessly across a cluster of servers.

Its Control Plane manages communication between nodes, automates deployment and scaling, and ensures high availability by handling load balancing and resource allocation.

This automation transforms infrastructure management, reducing complexity and enabling teams to deploy and maintain applications at scale with reliability and efficiency.

Service Discovery and Load Balancing

Service discovery is a built-in feature of Kubernetes that enables applications to find and communicate with each other dynamically within a cluster.

There is no need for hard-coding IP addresses nor manually configuring connections.

As you learned from the previous section, dynamic environments require this kind of setup since containers will constantly start, stop, or scale up and down according to demand.

What Kubernetes does is register each service in the cluster, making it easy for applications to find each other without needing direct intervention from the developers.

Additionally, service discovery works in concert with Kubernetes’ load balancing feature, which distributes incoming requests across multiple instances of a service, preventing any single instance from becoming a bottleneck or point of failure.

By routing traffic in this way, Kubernetes ensures that services remain responsive even as demand fluctuates.

Together with resource management capabilities, service discovery allows applications to communicate dynamically, helping to avoid a situation in which any individual service overloads.

Automatic Bin Packing

Every cluster has basic resources, such as CPU, memory, and storage. Whenever new workloads (containers, pods) are created in that cluster, Kubernetes assesses the resource requirements specified for each workload and finds the most suitable nodes within the cluster to host them.

This way, Kubernetes makes the whole infrastructure more efficient. It becomes less likely that any resources are unused or even overused.

It is a very sophisticated feature when you think about it. Kubernetes must analyze the capacity and current usage of each node so that it unites the ideal workloads in each case, and adapts everything accordingly should anything change.

This flexibility is important because, once again, as mentioned above, containers will constantly start, stop, or scale up and down according to demand.

This intelligent scheduling enables organizations to scale up efficiently as demand grows, and it’s particularly useful for running large-scale applications that require the consistent and balanced use of resources across multiple nodes.

Storage Orchestration

Speaking of intelligence, Kubernetes is also useful when it comes to storage orchestration. Be it cloud or on-premise, it allows applications to use and manage storage resources efficiently.

Many applications need to access and retain data even if their instances or pods are restarted. Thus, flexibility is a must.

With Kubernetes, developers can define and automate storage requirements at the application level, simplifying access to storage without needing to configure individual servers or manually manage physical volumes.

Kubernetes’ storage orchestration uses persistent volumes (PVs) and persistent volume claims (PVCs), which help abstract and automate the connection between applications and storage resources.

A persistent volume represents a piece of storage in the cluster that an administrator has provisioned, while a persistent volume claim allows applications to request storage in a way that’s abstracted from the underlying infrastructure.

This approach enables applications to seamlessly access the required storage without being aware of its physical location, making it easier to scale, relocate, or update applications as needed.

Kubernetes also integrates with numerous storage backends, such as AWS EBS, Google Cloud Storage, Azure Disks, NFS, and many on-premises systems.

This compatibility means applications can rely on a mix of storage options, from local storage to distributed and cloud-based systems, depending on the workload’s requirements.

Furthermore, Kubernetes can handle dynamic provisioning of storage, which automatically creates storage volumes on-demand when an application requests them. This removes the need for manual intervention, ensuring that storage is always available when needed.

Self-Healing

As if automating all the above processes wasn't enough, Kubernetes objectively supervises the status of each container in the cluster, managing their health and making decisions following previously established instructions.

Whenever a container fails or goes through any kind of problem that impacts performance, Kubernetes is proactive enough to restart it automatically without the need for permission from any administrators.

This is a great way to ensure uptime without the need of human action, which would end up slowing things down. It can even go beyond that. If necessary, Kubernetes can replace and reschedule containers across different nodes within the cluster in case a specific node becomes unhealthy or unavailable.

The resource leverages replica sets to maintain the desired number of instances for each application, so if one instance fails, a replacement is automatically created to meet the application's requirements.

Additionally, Kubernetes uses probes—specifically liveness and readiness probes—to check whether containers are ready to receive traffic or if they need to be restarted.

By automating these recovery actions, Kubernetes reduces the need for constant manual monitoring and troubleshooting, allowing teams to focus on development rather than infrastructure management.

Secret and Configuration Management

A lot was said about flexibility and automation but we can't leave security out of the equation.

Whenever dealing with sensitive data (API keys, passwords, other credentials), it is fundamental to make sure that those things are not accessible to people you don't want around.

In Kubernetes, secrets and configurations are managed independently from the application code, meaning sensitive information doesn’t need to be embedded within container images.

This separation significantly improves security by keeping critical data out of the application’s runtime environment and version control systems, where it could be exposed.

Kubernetes stores secrets and configuration details in a secure, encrypted format and allows only authorized containers to access this data.

By defining these values as Secrets or ConfigMaps, administrators can inject configuration data into containers at runtime, enabling applications to retrieve the necessary data without compromising security.

Additionally, this approach makes it much easier to update or modify configurations, as changes can be centrally made and immediately applied, without rebuilding or redeploying container images.

This separation of concerns simplifies configuration management and enhances flexibility, allowing applications to adapt to different environments more efficiently.

With this approach, administrators can securely rotate credentials, update API tokens, and make other adjustments without risking the exposure of such sensitive information.

Automated Rollouts and Rollbacks

With automated rollouts and rollbacks, deploying updates to applications is way simpler and safer.

Kubernetes enables gradual rollouts of new versions automatically. Instead of just deploying the new updates to all instances at the same time, it goes from portion to portion.

This way, it is possible to monitor the updated instances before deciding to proceed with the full rollout, which could be problematic in case something is wrong (compatibility issues, for instance).

Predefined health checks and performance metrics can indicate if something about the new version does not go well, and Kubernetes can even automatically revert to the latest stable version.

This recovery mechanism minimizes downtime avoiding possible disruptions that could get to the end user. And, again, it doesn't require human intervention to be done.

If you want to join the world of Kubernetes on bare metal, just create a free account, deploy your servers, and try it out.