Containers vs. Serverless Computing

The five key differences between containerization and serverless computing:

  1. Cost — You must pay for serverless environments hosted in the cloud, whereas a container can be set up for free. However, containers have ongoing management costs, which can be expensive — even if no one is using the application.
  2. Longevity – Serverless functions live short, which also provide agility and flexibility. Containers are always running and spinning.
  3. Scalability – A container-based architecture will require you to determine the number of containers you'll deploy in advance. In serverless architecture, the backend scales automatically based on demand.
  4. Programming languages – Serverless is more limited and includes Python, Java, Node.js, and Go. Containers allow you to work with any stack you want, which may be important when considering microservice architecture.
  5. Testing – In serverless applications, testing is challenging in comparison to containers.

As we move into the year 2021, many developers are asking critical questions, including should I go serverless (FaaS) or use containers?

When used appropriately, these two application deployment technologies can help DevOps teams deploy applications quickly, efficiently, and more cost-effectively.

Whether you aim to minimize dependence on a single cloud service provider or want to boost application development speed, it's important to understand how containers and serverless architectures compare to one another based on your unique needs.

In this article, we'll explore this question, highlighting the core differences between these two options, as well as the benefits and use cases of each.

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What Are Containers?

A container is essentially a unit of software that packages code so that applications will run efficiently when changing computing environments. In a container, you will also typically find configuration files, binaries, dependencies, and the application. This means that containers consist of an entire runtime environment. This helps developers overcome obstacles related to differences in operating system distribution and infrastructure.

Containers also allow different teams to work on varying aspects of the application independently.

A single container can be used to run anything from a small microservice to a larger application. When deploying larger applications, however, container clusters may be required. An orchestrator such as Docker Swarm can manage these clusters.

Docker is synonymous with container technology, as it has been the most successful in popularizing it.

What Is Serverless Computing?

In the past, applications were deployed over big servers, and while this approach has been efficient enough, there are a few issues with it.

  • The responsibility for ensuring appropriate security updates to the server lies on you.
  • The same is true regarding the overall maintenance of the server. All managing and provisioning fall on you.
  • Even if you are not using any resources from the server, you will still need to pay charges.
  • Scalability is manual in terms of scaling up your usage on the server.

Overall, for small companies and individuals, the workload associated with the issues above is often overwhelming and significantly reduces productivity. This approach will also have an immense impact on the cost of delivery and the time to market.

This is where serverless computing comes to the rescue.

Serverless computing is a cloud computing execution model where on-demand machine resources maintain the servers for you. This means servers are still very much involved in the computing process — you just do not need to worry about manually spinning up servers. Instead, your cloud provider (e.g., Google Cloud) will handle this on your behalf.

Serverless platforms that offer compute services, such as AWS Lambda and IronWorker, will allow you to scale container-based workloads with ease. This means you can stop wasting time on trivial tasks so that you can focus on what it is you excel in — the building and deployment of applications.

The Benefits and Use Cases of Containers and Serverless Computing

There are times when containers are the best option, whereas serverless computing is ideal in other cases.

The core benefits of serverless computing include:

  • Deployment simplicity and a reduced time to market (you do not need to worry about infrastructure).
  • Only pay for the functions you run.
  • Automatic scaling.
  • The ability to support event triggers, making this option ideal for pipelines and sequenced workflows.

It is recommended that you use serverless computing when:

  • Your traffic pattern changes.
  • You are concerned with the cost of server maintenance, as well as the resources associated with your application.
  • You seek a more hands-off approach in terms of where and how your code is running.
  • You are pressed for time and want to launch a fully functional app in a matter of days.

The core benefits of containers include:

  • Applications can be made as big as you wish (serverless may be associated with size and memory constraints).
  • Easier to re-architecture a current application.
  • Greater control over variables such as admin policies and security.
  • Ability to test, monitor, and debug applications locally.
  • Less overhead and increased portability.

It is recommended that you use containers when:

  • You want to use an operating system of your choice and have full control over variables such as runtime version.
  • You are using software that has specific version requirements.
  • You are developing new container-native applications.
  • You are dealing with complex applications, especially in terms of refactoring.
  • You are already bearing the cost of traditional servers for machine learning or Web APIs. Containers will cost less.
  • Using platforms such as Docker when aiming to auto-scale.
  • You are moving applications between different host servers.
Containers vs. Serverless Computing

What About the Cons?

Both containers and serverless computing come with a list of cons. These need to be carefully considered based on what's best for your current circumstances.

Cons of serverless include:

  • Vendor lock-in, as changing your cloud service provider can force you to make major changes to your code. Of course, this can result in lost time and money.
  • Possible latency in regard to executing tasks. If speed is one of the greatest priorities (e.g., an e-commerce application), serverless may not be ideal.
  • The time limits of serverless functions are not a good fit for long-running apps, such as games.

Cons of containers include:

  • When changes are made to the codebase, you will need to package the container before deploying.
  • They are more expensive to run.
  • The possibility of scaling issues (e.g., as applications grow and more containers are added, monitoring can be challenging). It will also be difficult to scale storage and data.

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Looking at the Big Picture: Containers vs. Serverless Architecture

When comparing containers vs. serverless computing, there is certainly some overlap. However, they are not interchangeable. In certain cases, one is more optimal than the other.

Here are some ways in which these two technologies are similar. Both containers and serverless computing:

  • Allow you to deploy application code.
  • Offer greater efficiency than virtual machines.
  • Abstract applications aways from a host environment.
  • Require orchestration tools (e.g., Docker Swarm, Amazon Step Functions, or Kubernetes) when scaling.

The differences between containers and serverless computing include:

  • Host environments – While containers can run on any Linux server or certain versions of Microsoft Windows, serverless runs on specific host platforms only, like Azure Functions or AWS Lambda.
  • Self-servicing – Serverless functions generally require a public cloud. In contrast, when using containers, you set up your own on-site host environment or use a cloud service like ECS.
  • Cost – Serverless environments come at a cost, as they are hosted in the cloud. However, you only pay for what you use. While containers can be set up for free, using open source code, you will still have management costs. Containers are constantly running and you will be charged by the server, even if no one is using the application.
  • Running time – Containers can run for as long as you need them to, whereas serverless functions are designed to run for short periods before shutting down.

Bottom line: Many companies do not think in terms of serverless computing versus containers. Instead, they think about the ways in which these two options complement one another, often using both for development — even though they serve different purposes. That's because these two options can support one another's weaknesses.

Both options are used to develop microservices, but they do work best for different use cases. While these options are not technically competing platforms, one option may be best for you based on your unique circumstances. If your goal is to limit application management and the architecture is not important to you, serverless is the way to go. If you wish to deploy an application on a specific system architecture and seek greater control, then containers are your best bet.

Think of it this way — serverless is best when you are developing an app that needs to be ready to perform set tasks, but it doesn't always need to be running. This option is ideal when you need to develop quickly and cost minimization is crucial. In contrast, containers are the best option when you are creating long-running, complex apps where greater control is of the utmost importance.

How Iron.io Can Help

Regardless of which option you choose, Iron.io can assist you.

IronWorker is a container-based distributed work-on-demand platform. Run your containers with dynamic scale and detailed analytics, containerizing everything from push notification to ETL processing.

IronFunctions offers open-source serverless computing for any cloud, whether it be private, public, or hybrid. Designed for developers, Functions can be directly implemented into your application, and tedious jobs are managed — this helps reduce task time.

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