Undoubtedly, cloud-native software development is not only an architectural advantage. Nowadays, cloud-native solutions are regarded as the most effective way to develop enterprise-level applications. Being a software engineer, developer, or a startup company founded in the USA, creating a modern application implies containerization. However, going further, you will eventually face a question “Do I need Docker or Kubernetes?”

The question stems from the lack of clarity regarding the technology stack. It is not about technology selection per se; the point is to comprehend the interaction of two different levels of infrastructure. Docker is a containerization platform. On the other hand, Kubernetes is an orchestration platform.

Being a backend engineer trying to improve local development process or platform engineer designing a reliable microservices architecture in cloud environments (AWS, Azure, and others), you should understand the key differences between Docker and Kubernetes to develop production-ready code.

1. Key Challenges Addressed by Docker vs. Kubernetes

Speaking of the challenges addressed by these platforms, one should pay attention to the similarities between Docker and Kubernetes. Docker is a platform for containerization. Prior to Docker introduction, every software engineer had to deal with the well-known challenge “it works on my machine”. Despite the flawless work of your application on your local machine, it fails immediately after deployment to the production server due to the incompatibility of the operating system, missing dependencies, and many more.

Thanks to Docker, you get the ability to isolate and package your application and dependencies into a portable container with shared kernel and independent user space. Thus, the behavior of your application does not depend on the deployment environment.

Orchestration Across a Server Cluster

While Docker solves the challenge of creating and running containers, Kubernetes (K8s) resolves another challenge related to orchestrating thousands of containers across numerous machines.

Let us suppose that your containerized application fails overnight. Unfortunately, Docker cannot detect the issue and start a new container, route traffic, and scale up the number of containers. However, Kubernetes allows doing so.

Kubernetes makes your application resilient, scalable, and automated.

2. Architectural Primitives & Deployment Models

From the explanation provided above, one can see that using these platforms implies the knowledge of fundamental architectural concepts. As far as Docker is concerned, the main primitives include Images and Containers. The developer prepares a plain-text Dockerfile which explains how to configure the environment for application deployment. After docker build, a read-only Docker Image is created. After docker run, a corresponding Container is launched.

Unlike Docker, Kubernetes introduces the concept of Pod and wraps containers into it. In this way, Pod becomes the next-level primitive. Pod is the smallest unit of deployment in Kubernetes and is regarded as an application process. Pod is an isolated process in the Kubernetes cluster that consists of one or several containers running in the same network space and utilizing the same storage volumes.

To orchestrate these Pods, the developer applies Deployment and Service primitives. The Deployment primitive indicates the desired number of Pod replicas that must be running at a certain moment. The Service primitive helps to configure routing to these Pods.

3. Scalability, High Availability, and Self-Healing

Another difference lies in scaling, high availability, and self-healing of an application under various conditions including traffic spikes, failure of a physical server, and others. Assume your application is running in a Docker container in a virtual machine. One day, the application experiences a traffic spike four times higher than the initial load. To scale up the application, you have to log into the server, execute a corresponding Docker command, and experience downtime.

Unlike Docker, Kubernetes provides a set of features to achieve high availability and scalability of applications. With the help of Horizontal Pod Autoscaler (HPA) feature, Kubernetes constantly monitors various metrics such as CPU usage and memory consumption. Upon traffic spike, Kubernetes automatically scales up and launches additional Pod replicas to cope with the increased load. When the traffic returns to the initial level, Kubernetes scales down.

Furthermore, Kubernetes is capable of self-healing. Kubernetes constantly checks the health of your application and starts the required pods automatically. In case of any crashes of the containers within Pod or memory leaks, Kubernetes restarts the problematic containers. In case of breakdown of a physical server due to hardware problems, Kubernetes reschedules Pods to a healthy node.

4. Networking, Service Discovery, and Configuration Management

Networking with Docker is simple but rather localized. Docker creates a private virtual bridge network and assigns a private IP address to each container. While being helpful in case of connecting containers running on the same machine using port mapping, connecting containers running in different cloud networks or even physical servers poses a challenge.

To address the issue, Kubernetes introduces the concept of flat networking. Each Pod gets a unique and routable IP address regardless of the physical machine it runs. Now you do not need to think about port mapping!

To enable communication between containers, Kubernetes introduces sophisticated service discovery mechanisms. Instead of knowing the IP addresses of all backend and database Pods, you configure Kubernetes to route traffic to your services using persistent names.

Configuration data management in Docker and Kubernetes differs as well. While in Docker configuration values are either hard-coded into the image itself or provided as environment variables.

With Kubernetes, you decouple your application code from operational configuration via ConfigMaps and Secrets. This way, you can easily update the environment variables, database connections, and other secrets without rebuilding the application container.

5. Comparison of Docker and Kubernetes: Swarm vs. K8s

Comparing Docker and Kubernetes in terms of orchestration capabilities implies comparison of Docker Swarm (embedded orchestration mechanism) and Kubernetes. Docker Swarm uses the same CLI and configuration files as Docker. Hence, the learning curve is relatively low. For a small engineering team running a few interrelated services, Docker Swarm may be a good choice.

While discussing the enterprise software industry, Kubernetes outperforms Docker Swarm in orchestration. Kubernetes dominates the market (~96%) as the major cloud providers (AWS, Azure, Google Cloud) provide managed Kubernetes environment (EKS, AKS, GKE).

While Docker Swarm aims at simplicity and fast adoption, Kubernetes is a complex platform with advanced support of complex global products with advanced traffic routing, security, multi-cloud redundancy, and many more.

Strategic Guide: When to Use What?

In conclusion, the choice of Docker and Kubernetes depends on the system scale, DevOps capabilities, and deployment strategy.

Use Docker alone (or Docker Compose) in the following cases:

You develop and test applications locally on your machine to maintain consistency.

You develop an MVP, hobby project, or a traditional monolith application.

Your system consists of less than five services and scaling is done manually.

You do not have DevOps engineers in your team.

Use Kubernetes in the following cases:

You design and maintain a microservices architecture with dozens or hundreds of services.

High availability, self-healing, and automatic scaling of applications during traffic spikes are critical for your business.

Zero-downtime deployment strategies (rolling updates, blue/green deployments, canary rollouts) are needed.

Your system implements multi-cloud or hybrid-cloud architecture.

Frequently Asked Questions (FAQ)

Does Kubernetes replace Docker?

No, Docker and Kubernetes complement each other in the software delivery lifecycle. Docker packages your application and dependencies into a container image. Kubernetes orchesrates this image across your production cluster.

Is Kubernetes possible to use without Docker?

Yes, it is! While Docker was used as a default runtime in Kubernetes previously, nowadays, Kubernetes is developed according to the Open Container Initiative (OCI) standard. That means that Kubernetes requires an OCI runtime like containerd or CRI-O. In other words, while building your images with Docker, Kubernetes uses a lightweight runtime to execute your images.

What is Docker Compose? How does it differ from Kubernetes?

Docker Compose is a powerful tool for defining and running multi-container Docker applications on a single machine. Docker Compose is perfect for local development and testing because you can run frontend, backend, and database containers simultaneously using a single command. However, Kubernetes is a cluster orchestrator which manages multiple servers.

Why is Kubernetes difficult to learn compared to Docker?

Docker is really intuitive as it has an architecture similar to the traditional OS. That is why Docker is easy to grasp. On the contrary, Kubernetes is a distributed OS of a cluster. Therefore, it requires an absolutely different approach to networking, logging, security, and abstract primitives (Pods, ReplicaSets, Ingress, and so on).

Is Kubernetes overkill for a small startup developing its first application?

Usually, yes. Kubernetes cluster is definitely an overkill for startups in the early stages. Managing production Kubernetes requires much administrative activity and engineering efforts. Startups looking for product-market fit would be better off containerizing their application code using Docker and deploying it to managed cloud platforms such as AWS Fargate, Azure App Service, and Google Cloud Run.