In Comparison: Cloud Run vs. Google Kubernetes Engine

May 14

Developers love containers because they enable them to rapidly deploy, patch, and scale applications. For this reason, Google Cloud has a number of services that you can use to run your containerized apps, but the top two are Cloud Run and Google Kubernetes Engine (GKE). Because they’re all managed container services, choosing the one that best suits your needs can be challenging. So in this post, we’ll cover the importance of both services and in which one of them is better for your use case.

Cloud Run

The first way to deploy containers on Google Cloud is using Cloud Run, while at the same time taking adventage of both serverless and containers. With Cloud Run, you can deploy scalable stateless HTTP containers quickly and easily. It is fully automated, and Google Cloud provisions and configures servers for you. This automation extends to auto-scaling where Cloud Run automatically scales up or down depending on the traffic you’re receiving. It even scales down to zero, which helps in ensuring you pay for it only when it is in use.

Cloud Run lets you build and deploy containerized workloads written in your language of choice, including Python, Java, Node.js, .NET, and so on. It is suitable for web app services, APIs, or other synchronous event-driven apps. It is worth mentioning that Cloud Run has another extension called Cloud Run Jobs that is suited for unattended batch jobs such as database migrations. This can be triggered manually on the command line or via the Cloud Scheduler.

Benefits of using Cloud Run

It is cost-effective, allowing you to pay only when the service is in use.

Easy to use, allowing you to package your app in a container and deploy it in a single command. Everything else is automated, therefore, you don’t have to worry about going through a steep learning curve to use it.
By taking care of health checks, monitoring, and logging, it allows you to focus solely on developing.
It allows you to build and deploy to your Kubernetes cluster since Cloud Run is built with Knative.

Limitations of Cloud Run

Cloud Run does not support running stateful workloads such as databases.
Because it is fully managed and automated, you have less control over the performance of your containerized workloads for special use cases.

Google Kubernetes Engine (GKE)

By definition, GKE is a scalable and managed service for running Kubernetes. Kubernetes is the industry standard for deploying and managing containerized workloads. GKE is managed in the sense that Google Cloud handles the underlying infrastructure on which your Kubernetes clusters run and the initial control plane is set up. However, it gives you the flexibility to add memory and vCPUs to run larger containerized workloads. For this reason, GKE is suited for multiple containerized microservices which are deployed dependently. GKE has two modes of operation with different degrees of management. These are:

GKE Standard – This is the original mode with a very hands-on approach to container deployment and management. With GKE Standard, you will have to handle cluster management and configuration yourself.
GKE Autopilot – This is a recent extension of GKE. Just as the name suggests, GKE manages the cluster and configures the nodes for you. As a result, it offers a hands-free experience.

Benefits of using GKE

GKE has a wide variety of use cases. Kubernetes allows you to deploy stateful applications using its StatefulSet controller.

GKE clusters are secure by default, highly available, and run on Google Cloud’s high-speed network. Your containerized workload will inherit these qualities.
GKE Standard gives you a lot of control over your clusters and nodes. You can fine-tune your clusters for zonal and regional locations to ensure a defined set of containers is running at a given time.
GKE clusters make your operations smooth with auto-scaling and automatic repair for failing nodes and upgrades to the latest versions of Kubernetes.

Limitations of using GKE

It can be expensive since you’re charged per VM rather than when the service is in use.

GKE standard is complex, requiring you to understand node configuration and cluster management, and of course, to write YAML files. Although this complexity is lower in GKE Autopilot.

How do Cloud Run and GKE compare?

Cloud Run and GKE are both cloud-native managed services for running containers on Google Cloud.

So, what is the main difference?

Cloud Run is the embodiment of a fully managed compute platform, all you have to do is package your app and its dependencies in a container and run it in one command. Google Cloud takes care of everything else, including server configuration, health checks, monitoring, logging, and so on. Simply put, Cloud Run is a Platform-as-a-Service.

Although GKE is also managed, this is done to a lesser extent than Cloud Run, giving you more control over node configuration and cluster management. GKE Autopilot, on the other hand, tilts towards a fully managed service, but you still have the freedom to add memory and vCPUs if needed. GKE provides an environment for running containerized apps, along with developer tools and SaaS to accelerate your development cycle. Also, the billing for these services is different. For Cloud Run, you only pay when the service is in use because it can scale down to zero, while for GKE, you pay per VM.

Cloud Run vs GKE, which one should you choose?

Before making a choice, you should know that there’s no one choice that is universally better than the other. It all depends on the use case and the results you want from your containerized app.

Use Cloud Run if:

You want to independently deploy a single stateless container or a service that can be broken down into multiple independently deployed containers. It works well for simple services, for example, a microservice that simply converts .doc files to .pdf files.

Use GKE if:

You want to run multiple containerized microservices dependently or if your service has specific memory and server configuration requirements, for example, in highly regulated environments. Use GKE if you are deploying a stateful service that stores and interacts with data hence requiring a database or some form of information storage.

Also, Cloud Run is the better option if you’re just getting started with deploying containers on the cloud and GKE is suitable if you have experience with it or you have dedicated teams for cluster management and node configuration.

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