What is containers as a service (CaaS)?
To succeed, a developer must efficiently package and transport different kinds of applications. Containers as a service (CaaS) can be a way of simplifying the deployment, operations, and maintenance of your infrastructure and containers along the way.
Containers as a service (CaaS) defined
Containers as a service (CaaS) is a cloud service model that provides a managed and automated platform for container orchestration. It’s a way of organizing and streamlining cluster operation.
Imagine all of your programs living in a virtual office space, then a manager who takes care of the office and keeps it from getting destroyed or overwhelmed; that’s what containerization is. You don’t need to deal with problems. Instead, you can focus on how your code runs.
CaaS effectively bridges the gap between the developer's finished container and a production-grade runtime, abstracting away the underlying infrastructure and allowing teams to focus on application logic instead of cluster management.
How does CaaS work?
CaaS works by providing a managed environment built around a container orchestration engine, with Kubernetes being the industry standard for a container orchestration engine. The process creates a separation of concerns between developers and the foundational infrastructure:
A developer's jobs: First, they have to be able to take all dependencies and their application and package these into a container image. This image is a portable blueprint of the application.
Once complete, a CaaS product takes over: After it's done being built and stored, all that's left is to apply management via an API or interface. A CaaS product such as Google Kubernetes Engine, can help handle everything else. Now, it's time to define the state for the program. Need three servers running the same program? This service can help.
Afterwards the new deployment is handled automatically. This may include:
- Putting applications onto nodes and managing transfers through connections
- Scaling with different kinds of metrics or resources
- Managing and automatically healing code in action
- Connecting services and resources
CaaS examples and uses
CaaS is a versatile platform that can serve as a base for architectures and development practices.
Microservice architecture
Can be an ideal platform for application and microservice deployments.
Legacy transfers
Can be a great way to package older applications with legacy app modernization.
Constant iteration
For development structures, a CaaS platform can be great.An automated system can build a new container image, run tests, and deploy it to the CaaS platform with zero downtime.
Scalable and reliable application support
CaaS automates traffic needs. The platform can add or remove container instances automatically to match user demand.
Hybrid and multicloud strategies
CaaS platforms built on open standards like Kubernetes can provide a consistent operational environment across on-premises data centers and multiple public clouds. This helps enterprises to run their containerized applications anywhere without having to re-architect them.
CaaS verses other cloud models
The "as a service" landscape includes several models, each offering a different level of abstraction and management. CaaS fits uniquely between infrastructure as a service (IaaS) and platform as a service (PaaS).
Cloud model | What you manage | Level of abstraction | Typical use case |
Infrastructure as a service (IaaS) | Operating system, middleware, runtime, application, data. | Low: Provides access to raw computing resources like virtual machines and storage. | Building custom infrastructure from the ground up. |
Containers as a service (CaaS) | Application and configuration. | Medium: Manages the cluster and OS, but gives you control over the container environment. | Orchestrating containerized applications at scale. |
Platform as a service (PaaS) | Application and data. | High: Manages nearly everything; you provide the source code and the platform runs it. | Rapidly deploying simple web applications and APIs. |
Function as a service (FaaS) (serverless) | Only the code function. | Very high: Runs individual, stateless functions in response to events. | Lightweight, event-driven data processing tasks. |
Software as a service (SaaS) | Nothing (you are the end user). | Complete: Delivers a finished software product over the web. | Consuming ready-to-use software like email or CRM. |
Cloud model
What you manage
Level of abstraction
Typical use case
Infrastructure as a service (IaaS)
Operating system, middleware, runtime, application, data.
Low: Provides access to raw computing resources like virtual machines and storage.
Building custom infrastructure from the ground up.
Containers as a service (CaaS)
Application and configuration.
Medium: Manages the cluster and OS, but gives you control over the container environment.
Orchestrating containerized applications at scale.
Platform as a service (PaaS)
Application and data.
High: Manages nearly everything; you provide the source code and the platform runs it.
Rapidly deploying simple web applications and APIs.
Function as a service (FaaS) (serverless)
Only the code function.
Very high: Runs individual, stateless functions in response to events.
Lightweight, event-driven data processing tasks.
Software as a service (SaaS)
Nothing (you are the end user).
Complete: Delivers a finished software product over the web.
Consuming ready-to-use software like email or CRM.
Frequently asked questions
What is a container?
Containers are meant to deliver your software anywhere and everywhere. Learn more about containers.
Do containers have benefits of portability?
Yes. In fact they give you access to many other benefits; such as, separation of responsibility, application isolation, batch processing, and hybrid cloud deployments.
Does Google Kubernetes Engine simplify service discovery?
Google Kubernetes Engine (GKE) simplifies service discovery through its seamless integration with Kubernetes' native mechanisms, such as Service objects and DNS. As a managed service offering, GKE enhances these capabilities with features like multi-cluster Services (MCS) and direct integration with Service Directory.
What are the benefits of CaaS for developers?
New code can be made and deployed faster, but resources aren’t managed or thought up over and over again.
Benefits of CaaS
Adopting a CaaS platform can provide several strategic benefits for enterprise development and operations teams.
Portability
Portability
CaaS helps workloads. If a container-based application is running on your standard CaaS framework, it can be edited easily.
Scalability
Scalability
You can adapt resources to what is needed.
Fast coding
Fast coding
This helps improve automation practices.
Improved maintenance
Improved maintenance
If a component fails, that is addressed by the platform and quickly fixed to maintain user functionality.
CaaS security
The provider handles the foundational work, which makes you rely on your app’s code, account access, firewall, and access rules. Security in a CaaS environment operates on a shared responsibility model. The CaaS provider is responsible for securing the foundational infrastructure. This includes protecting the container orchestration control plane, securing the physical data centers and network, and ensuring the integrity of the managed service itself.
The customer is responsible for the security of the components they control. This involves securing the application code, managing access to the cluster using Identity and Access Management (IAM), configuring network policies to control traffic between containers, and, critically, securing the software supply chain. This means using a secure container registry and scanning container images for known vulnerabilities before they are deployed.
Responsibilities for the IT team:
- Test application code
- Use and control IAM or Identify Access Management
- Software security
How to set up CaaS with Google Kubernetes Engine
Google Kubernetes Engine (GKE) is Google Cloud's premier CaaS offering. Setting up a basic deployment follows a logical, container-native workflow.
Step 1: Containerize your application.
First, write a text file for your application. This simple text file contains the instructions to build your application, its dependencies, and its runtime environment into a static container image.
Step 2: Store your image in a registry.
Build the container image from your text file and push it to a secure, private registry. Artifact Registry is Google Cloud's managed service for storing and versioning your container images.
Step 3: Create a GKE cluster.
Provision a Kubernetes cluster using the Google Cloud console or gcloud command-line tool. For a fully managed experience that automates node management and scaling, you can choose a GKE Autopilot cluster.
Step 4: Define your deployment.
Create a Kubernetes manifest file (for example, deployment.yaml). In this file, you declaratively define your application's desired state, specifying the container image to use, the number of replicas you want, and the required CPU and memory resources.
Step 5: Deploy your application to the cluster.
Use the Kubernetes command-line tool, kubectl, to apply your manifest file to the cluster. GKE's control plane receives the request and schedules your application's containers to run on the cluster's worker nodes.
Step 6: Expose your application.
To make your application accessible to users over the internet, create a Kubernetes Service manifest of type LoadBalancer. When you apply this manifest, GKE automatically provisions a cloud load balancer and assigns it a public IP address, routing external traffic to your running containers.
Related products and solutions
Google Kubernetes Engine (GKE)Google Cloud's managed CaaS platform that provides a production-ready, scalable, and secure environment for running containerized applications with Kubernetes.
Cloud RunA fully managed serverless platform that runs stateless containers. It can be viewed as a more abstracted form of CaaS, ideal for individual services and applications where you want to completely offload cluster management.
Additional resources
- This video from Google Cloud Skills Boost provides a foundational overview of containers and how Kubernetes works as an orchestration engine
- This helpful guide explains the key differences between other major cloud service models, such as IaaS, PaaS, and SaaS
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