Should I Use Kubernetes?

In the DevOps world, container orchestration is often synonymous with Kubernetes, a platform for deploying and managing container-based workloads in production. Since its debut in 2014, Kubernetes has seen a meteoric rise in its adoption and popularity. Despite being a relatively new technology, it is now used by a vast number of organizations, from small start-ups to large enterprises, so much so that it has become a standard in the industry. 

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Kubernetes benefits from strong community support and boasts a rich ecosystem of tools and extensions. It is continuously being developed and improved, and judging by the current trends, Kubernetes is only going to get stronger in the future. 

In this blog post, we’ll discuss why you should consider using Kubernetes, scenarios where Kubernetes might not be the ideal solution, and explore some of its alternatives. Let’s get started!

When Should You Use Kubernetes?

Kubernetes offers several key features that cater to a wide range of application deployment and management needs. Here are the top three features of Kubernetes that make it particularly suitable for modern software development:

Microservices architecture

In recent years, the microservices approach (where software systems are developed as a collection of small, independent services) has become increasingly popular in software development. Kubernetes provides many abstractions and APIs that are particularly well-suited to the requirements and characteristics of a microservices architecture. Here's how Kubernetes simplifies the deployment and management of microservices:

  1. Containerization support: Kubernetes is designed to manage containers, which are ideal for microservices. Each microservice can be packaged into its own container, encapsulating its dependencies and runtime environment. This makes it easy to deploy and manage microservices consistently and reliably.
  2. Service discovery and load balancing: Kubernetes uses Service resources to streamline how instances of microservices discover and interact with each other. These Services provide stable DNS names for accessing a logical group of Pods and use their IP addresses to manage network traffic. This setup not only eases the discovery process within the cluster but also integrates built-in load balancing. 
  3. Automated scaling: Microservices often need to scale independently based on demand. Kubernetes can automatically scale services up or down based on load, ensuring that each microservice has the resources it needs without over-provisioning.
  4. Self-healing systems: Kubernetes continually monitors the state of each microservice and can automatically restart containers that fail, replace and reschedule containers when nodes die, and kill containers that don't respond to user-defined health checks. This resilience is crucial for microservices, ensuring high availability.
  5. Release management: Kubernetes can update a microservice in production without causing any service disruption. It gradually rolls out changes to microservice applications, ensuring that the service remains available and stable throughout the update process. 

Automatic scaling

One of the standout features of Kubernetes is its ability to auto-scale applications in response to fluctuating traffic demands. When production systems experience spikes or drops in traffic, Kubernetes seamlessly steps in to adjust resources. It achieves this through two key scaling strategies:

  • Vertical scaling: This strategy involves increasing or decreasing the CPU and memory resources allocated to existing Pods. When a Pod requires more resources to handle the increased load, Kubernetes can dynamically adjust these allocations within the limits of the host machine's capacity. This process allows for more intensive use of the available resources on a node, enhancing the Pod's ability to manage heavier workloads without the need for additional instances.
  • Horizontal scaling: In contrast, horizontal scaling involves creating additional Pod instances and distributing them across different nodes in the cluster. This approach is effective in maintaining performance levels by parallelizing the workload. Kubernetes automates horizontal scaling decisions based on metrics like CPU usage, ensuring that the scaling response is both timely and precise.

In addition to horizontal and vertical scaling, Kubernetes excels in cloud-based scalability, using a feature known as the Cluster Autoscaler. Using this feature,  Kubernetes can interface directly with the cloud provider to automatically launch new virtual machines when existing nodes are at capacity. This seamless integration ensures that the Kubernetes cluster can expand its resources to meet increasing demand without manual intervention, making it an ideal solution for applications hosted in the cloud.

Want to learn how to run containerized applications at scale with reliability and efficiency? Check out our blog post: Deploying, Maintaining, and Scaling Kubernetes Clusters

Resource optimization

When Kubernetes runs an application, it carefully selects the best node for the job, taking into account the application’s resource requirements and the resources available on each node. This intelligent decision-making ensures the most efficient use of the cluster’s resources.

Thanks to its container-based architecture, Kubernetes doesn't anchor applications to specific nodes within the cluster. This flexibility allows applications to move freely around the cluster. As a result, the various components of your applications can be optimally arranged, allowing for tight packing on the cluster nodes. This leads to improved utilization of each node's hardware resources.

The capability to dynamically relocate applications within the cluster allows Kubernetes to manage infrastructure resources far more efficiently than manual scheduling methods.

Want to learn the best practices that will help you minimize the costs and maximize the performance of your Kubernetes cluster? Check out our blog post: Optimizing Kubernetes Clusters for Cost & Performance: Part 3 - Best Practices

When Should You Avoid Kubernetes?

Kubernetes is a powerful solution for container orchestration, but it's not a one-size-fits-all solution. The following are some scenarios where its implementation may not be ideal. 

Simple applications

If you have a simple, monolithic application that doesn't require frequent updates in production, the complexity of Kubernetes may not justify its benefits. Kubernetes is optimized for managing multiple, often interrelated microservices and might be an overkill for simpler application setups.

Also, smaller teams working on uncomplicated projects might find the overhead of Kubernetes unnecessary. If your project can tolerate occasional outages during deployments, a traditional monolithic architecture might suffice. Kubernetes shines in environments where high availability and continuous deployment are critical.

Resource constraints

Running Kubernetes, especially in a cloud environment, can incur significant costs. For small businesses or projects with tight budgets, the cost of running a Kubernetes cluster might not be financially feasible compared to simpler and less expensive options.

In addition, Kubernetes is complex and requires a certain level of expertise to deploy, manage, and troubleshoot. Teams with limited personnel resources, especially those lacking Kubernetes expertise, might find it challenging to maintain a Kubernetes environment. The cost of training or hiring skilled personnel to manage Kubernetes might not be justifiable for smaller projects or teams.

Learning curve vs. project timeline

Kubernetes is a large and complex system with many moving parts. For teams new to Kubernetes, the learning curve can be steep and time-consuming.

When project timelines are tight, investing time in learning and setting up Kubernetes might not be feasible. In such cases, simpler solutions or more familiar tools might be more practical to meet project deadlines.

Interested in becoming a Kubernetes pro? Explore our comprehensive Kubernetes Learning Path, tailored to cater to various skill levels.

Alternative Solutions to Kubernetes

The world of container orchestration isn't limited to Kubernetes alone; there are several other highly popular container orchestrators available. Below, we discuss two of the most noteworthy open-source alternatives. In addition, we also cover traditional virtual machines, ideal for scenarios where containers might not be the most suitable solution.

Docker Swarm

Docker Swarm is a container orchestrator that comes installed with Docker by default. Since it uses standard Docker APIs, it integrates seamlessly into the Docker ecosystem. 

While Docker Swarm may not match Kubernetes in terms of advanced capabilities, it's relatively easy to get started with. If you are building your system around Docker, Docker Swarm might be a better fit. Its simplicity and direct compatibility make it an attractive option, especially for smaller-scale projects or businesses just beginning to explore container orchestration.

Apache Mesos with Marathon

Apache Mesos is an open-source cluster manager. It abstracts computing resources such as CPU, memory, and storage from the underlying physical or virtual machines, presenting the entire cluster as a single pool of resources.

By default, Mesos doesn’t manage containers; it’s more focused on the overall management and allocation of resources in a cluster. Marathon is a framework that runs on top of Mesos, and it adds container orchestration capabilities. This makes Mesos with Marathon a fully managed container orchestration solution.

Apache Mesos is a mature project and supports many important features, such as high availability, service discovery, and load balancing, to name a few. Its rich feature set makes it suitable for complex, large-scale deployments, although it hasn't achieved the same level of mainstream adoption as Kubernetes.

Virtual Machines

Virtual machines (VMs) are particularly well-suited for monolithic or legacy applications that are not designed for a microservices architecture. Such applications might have dependencies on specific operating systems or require isolation at the OS level, which VMs can provide. Each VM runs its own operating system, offering a higher level of isolation compared to containers. This can be crucial in environments where security and data isolation are paramount.


In this blog post, we learnt about Kubernetes’ effectiveness in managing a large number of containers, particularly when these workloads require high availability and scalability. However, it isn't always the best choice, especially for simpler, monolithic applications. Moreover, Kubernetes might not be the best fit for small teams or those new to Kubernetes, especially when facing tight project deadlines. 

It's important to remember that while Kubernetes is a powerful and widely used technology, it doesn't suit every application type or use case. Making the right choice between Kubernetes and simpler alternatives is key to the success of your project.

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