Did you know that cloud services have fundamentally changed the very approach to both the purchase and delivery of IT resources? In their turn, IT specs have changed the way of using the resources. This is about containers and how they behave in cloud infrastructures. The use of containers in production has increased by 300% since 2016, according to the report of the Cloud Native Computing Foundation. Container management systems should have appeared as a result. Kubernetes has spearheaded the trend.
Since K8s does not depend on the infrastructure, container management services boost the overall effectiveness of cloud environments. Kubernetes has been strongly associated with container orchestration. This is the reason why leading cloud providers build their own container management services to combine Docker and Kubernetes for containerized apps. What are the main Kubernetes container engines and how to choose the right one? We address the questions in the present post.
The Roots: Why K8s Acquired Engines
All available Kubernetes management services are quite infant-like, having histories of only three years or so. However, one of the solutions appeared earlier. That was the engine closest to Kubernetes as such since both products originated from the same source – Google. We mean GKE (Google Kubernetes Engine) that was introduced in 2015.
But before taking a look at the main rivals of GKE, the reason why Kubernetes management services have had to appear is worth mentioning. The thing is that addressing container cloud capacities implies two basic approaches. The first one makes a company execute everything with its own expertise. The approach provides full control over the container environment. At the same time, it requires specially trained in-house staff capable of dealing with container orchestration.
The second one offers container orchestration services from cloud providers. They allow reducing the time needed to maintain container systems. In other words, the centralized services can free up your IT engineering resources that can be forwarded to other business tasks. Today, almost all corporate workloads move to clouds. And the majority of companies choose container orchestration services (aka Kubernetes engines) from their cloud providers.
The concept of a centralized cloud-native Kubernetes engine implies a special module with an interface that facilitates launching schedulers, load balancers, nodes, and network routes. The engines work with all cloud environments to simplify container deployment. They have advanced capabilities to run the K8s architecture. The main difference between the solutions is in their administrative infrastructures.
Kubernetes Engines: Main Players
Many cloud providers support K8s but the most popular engines are Amazon EKS, Google GKE, and Microsoft AKS. Azure Kubernetes Service and Amazon Elastic Kubernetes Service appeared in 2018 when engines from other cloud providers saw the light. Hence, the two main rivals of Google were staying on an equal footing with all the competitors. However, both EKS and AKS are burgeoning vigorously to outperform whatever other services besides GKE. Google’s engine is still considered to be the strongest solution with the most comprehensive functionality.
Despite its maturity, GKE is not a leader in popularity, nonetheless. According to stats, Amazon Elastic occupies more than half of the entire Kubernetes orchestration market. Google is in second place and Microsoft closes the top three leaders. Let’s introduce all of them with brief snapshots:
This is a ready-to-use container management architecture. GKE is famous for its smooth UX having perfect integrability with the Google Cloud Platform. The engine demonstrates outstanding performance in terms of fail-free operation. Technically speaking, this is an exclusive marketplace for containerized apps. The Gvisor service network provides extra reliability for containers. Besides, the solution enables localization for multi-cloud environments.
Both the rich functionality and simplicity in use make GKE the best choice for those who are irrelevant to any particular cloud provider. It is legitimate to claim that GKE sets the tone for other Kubernetes management services (which is no surprise in the light of the engine’s genesis).
GKE does not require time-consuming configuration since embedded tools for logging and monitoring are available. The service uses Google’s Stackdriver for performance management. You can monitor information about all workloads and resource consumption with Google Cloud Console. The full control over cluster configurations is kept via GKE CLI. Just execute GKE clusters and get to launching workloads on the move.
The only shortcoming of the service is, probably, in fine-tuning all the details that is impossible since the ample automation of the service makes GKE do too many things on behalf of you.
Azure Kubernetes Service is a decent alternative to GKE with powerful development tools and Kubernetes updates. It is an obvious choice for those who already use Microsoft Azure. Even though AKS falls short of GKE, this is a good ready-to-use solution with logging, monitoring, and metrics. The Azure portal displays enough information about cluster workloads. Besides the portal, AKS has its own CLI with which your clusters can be supervised quite completely.
Relatively poor uptime remains the main problem of AKS. It does not mean, of course, that the engine is good for nothing (many large corporations keep using AKS, after all), but it is worth remembering that AKS is inferior to the main rivals in such a context.
If you use either Azure or other Microsoft tools such as 365 and Active Directory, choose AKS with no hesitation. Moreover, the service has enough to offer to different users: low price, free control plane operations, immediate Kubernetes updates, decent development tools with VS Code, and convenient serverless computing.
In terms of simplicity and automation, the engine from Amazon is the weakest in comparison with both GKE and AKS. But EKS is the right choice for those who are tied to AWS while seeking to control clusters completely.
In contrast to GKE and AKS where cloud providers are in charge of almost everything, EKS makes you do a lot manually. You have to thoroughly set up policies and IAM roles, install various components, etc. No data about clusters and workloads are available by default. Accessible tasks in both web interface and CLI are far from diverse.
Third-party instruments such as terraform-aws-eks and eksctl can partially fill the gap to automate the creation of clusters to some extent. A fully-fledged CLI is achieved with eksctl, for example. But this is not about native tools and the range of features is limited, therefore.
If you are looking for the widest possible freedom of choice in terms of adjustable options, EKS is here to help. Bare metal nodes are supported, by the way.
But the main advantage of EKS is backed by AWS, which is the most powerful and reliable cloud platform with numerous great services supported by a huge community of developers.
Comparison of Kubernetes Engines
Common properties and similar features can help compare Kubernetes engines. The functions are grouped in several main sections in descending order of importance.
GKE and AKS offer full support of Kubernetes clusters via the CLI tool. EKS has limited support that significantly impedes automation without third-party instruments. All three platforms support the Kubectl utility for a command-line interface.
GKE and AKS directly integrate their own monitoring tools. They both have up-to-date well-designed interfaces providing convenient monitoring of logs and tracking the resource consumption. Notifications are adjustable as well. Google Cloud Platform has the most user-friendly interface while the one from Azure is not seriously worse.
EKS needs monitoring functions to be set up independently through CloudWatch Container Insights. In general, CloudWatch is easily integrable and has all the necessary metrics. But its interface is quite obsolete and confusing. You’d better use some third-party solutions such as Prometheus.
Google offers the best auto-scaling option. A big number of nodes (up to 5000) is supported. Every function is accessible without being pre-adjusted. Moreover, you can immediately deploy GKE through Anthos. What you need to do is just determine the capacity of a virtual machine along with the number of nodes in a pool. All the rest can be confidently left for GKE.
AWS requires manual settings to a certain extent while Azure claims about auto-scaling with partial support (not for production).
GKE has Cloud Run for Anthos for serverless computing. You can benefit from the manageable serverless platform Cloud Run that provides the deployment of highly scalable workloads. The scale can be easily altered upon request. At the same time, you use resources of your own cluster instead of the infrastructure of Google Cloud Run. It does not affect your Kubernetes deployment, however. Hence, you have a special option for workloads to which serverless computing fits the best.
Virtual nodes make serverless computing possible in AKS. For more precise scaling, Kubernetes pods should be deployed via Azure Container Instances instead of fully-fledged virtual machines. In contrast to Cloud Run for Anthos, such a model does not run separately from the available Kubernetes workloads. In other words, you can use virtual nodes by assigning particular workloads to them.
EKS is integrated with Fargate – the container-based serverless platform from Amazon. Such a variant is similar to virtual nodes of AKS: you can deploy pods as containers’ instances rather than full VMs. However, you will need Amazon Application Load Balancer to use Fargate while Azure meets any sort of load balancer.
Tools For Developers
Google offers either Cloud Code or the VS Code extension to deploy, monitor, and control clusters directly in IDE. This implies a straightforward integration with Cloud Run and Cloud Run for Anthos.
Microsoft offers similar functionality with the Kubernetes extension in VS Code. But AKS has a unique function as well – Bridge to Kubernetes. It allows you to execute a local code as a service in a cluster. Replicating dependencies in a local environment remains unnecessary for launching and debugging at the same time.
Extensions from both Google and Microsoft meet any Kubernetes cluster. The basic functions are accessible through either kubectl or Kubernetes API. That’s why they can work with EKS clusters as well. But EKS has no separate tool for developers yet.
If you are looking for a container orchestration solution without reference to a particular cloud provider, the Kubernetes engine from Google seems to be the optimal choice. No extra adjustments are needed, there are numerous features and a user-friendly interface, simple maintenance and convenient CLI are offered as well.
AKS ranks second with its automation, serverless computing, useful development functions in VS Code, and a free control panel, to boot.
EKS has fewer automation features but if you want to adjust everything manually, the Kubernetes engine from Amazon is your variant. Truth be told, EKS is usually a tie-in with AWS while generating a poor customer interest as a separate product (just AWS as such explains why the share of EKS on the container orchestration market is so unduly large).
Before making a choice between the above-mentioned Kubernetes engines we recommend you to select a cloud provider first of all. Besides, consider how strongly you’d like to control various aspects of your cloud infrastructure.
But regardless of your choice, whether it is a cloud service for either enterprise workloads or an eCommerce website, the hands-on experience of Kubernetes experts would never hurt.
Contact us to integrate Kubernetes into your IT environment with a precisely fitted container orchestration service. We can create a tailor-made multi-cloud strategy for your company. Since we deeply understand the peculiarities of all Kubernetes engines, a middle-ground solution can be developed according to your individual needs for a reasonable price with an appropriate level of management.
Dmitry has 5 years of professional IT experience developing numerous consumer & enterprise applications. Dmitry has also implemented infrastructure and process improvement projects for businesses of various sizes. Due to his broad experience, Dmitry quickly understands business needs and improves processes by using established DevOps tools supported by Agile practices. The areas of Dmitry’s expertise are extensive, namely: version control, cloud platform automation, virtualization, Atlassian JIRA, software development lifecycle, Confluence, Slack, Service Desk, Flowdock, Bitbucket, and CI/CD.