Serverless vs Containers

Serverless and containerization have been two of the biggest DevOps buzzwords in recent years, and for good reason. In the right use cases, both can improve performance and reduce costs. However, despite their popularity, not everyone understands the differences of serverless computing vs containers.

Here, to help you hit the ground running with both technologies, we’ll take a look at what each is, compare them, explain how they complement one another, and explore the critical topic of serverless vs containers security.

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What are Containers?

Containers are lightweight immutable units of software that include all the dependencies and code to run an application.

Containers run on top of “container runtimes” (sometimes called container engines) that can run on a wide range of operating systems and platforms. Because the container runtimes provide all the system resources a container needs, the operational complexities of deploying an application on a traditional operating system are minimized.

Containers are also highly portable. Anywhere the container runtime exists, teams can deploy a container image. Additionally, because containers include only what they need to run an application, containers are more lightweight and faster than alternatives like virtual machines.

The most popular example of a container platform is Docker. However, Docker isn’t the only container platform. For example, Linux Container (LXC) predates Docker and is still in use today. Additionally, there are many tools that complement containers, such as Kubernetes (K8s) which is used to orchestrate and manage container deployments at scale.

What is Serverless?

Serverless is a model of computing that runs code on-demand without the need to provision or manage infrastructure.

Despite what the name implies, there are servers involved in serverless computing. However, enterprises don’t have to worry about the server infrastructure at all. Instead, development teams simply deploy their code on a serverless platform, and are only charged when that code runs and consumes server resources.

Because enterprises pay only for the time they’re using server resources (e.g., CPU), serverless can be a great way to minimize the cost of deploying applications with large spikes and dips in usage. This is a fundamental shift from running bare-metal servers, virtual machines, or containers. There is no cost for any idle time, charges only occur when an app is actively running and using resources.

Additionally, operational complexity goes down because all the infrastructure is abstracted away by the serverless platform provider. DevOps teams simply focus on their code. Popular examples of serverless computing platforms include AWS Lambda, Azure App Service, and Google’s Cloud Run.

Common use cases

Now that we understand what serverless computing and containers are, let’s look at some of their most popular use cases.

Popular container use cases include:

  • Microservices. Containers are the building blocks of microservices architecture. Because containers are portable, lightweight, and easy to deploy, they are an excellent fit for creating loosely coupled microservices.
  • CI/CD. Containers provide DevOps teams a way to eliminate environment differences between dev, QA, staging, and production deployments. As a result, they are highly useful in continuous integration / continuous deployment (CI/CD) workflows.
  • “Deploy anywhere”. Most modern enterprises operate in hybrid cloud and multi-cloud environments. Whether enterprises need to run an application on-premises or across multiple clouds, containers can do the job.
  • Legacy application migration. In many cases, legacy monolithic applications need to be migrated to the cloud. Containerizing them makes this process easier.

Some of the most popular serverless use cases are:

  • APIs. Application Programming Interfaces (APIs) like REST APIs and GraphQL implementations are a widespread serverless computing use case. Because API transactions are short-lived and can quickly scale up and down, serverless provides a solid platform to build API backends on.
  • Data processing. Serverless can enable data processing from multiple sources using  simple functions. As a result, serverless computing works well for teams that need to process and analyze data at scale but want to avoid the infrastructure management.
  • IoT. Serverless computing provides an event-driven and straightforward way for IoT devices and external systems to communicate asynchronously.
  • Dynamic website content. One of the textbook functions of serverless is adding dynamic content and logic to static websites. For example, AWS Lambda is often used to add dynamic functionality to a static site hosted on S3.


Of course, these are just a sampling of what’s possible with containers and serverless computing. Generally speaking, containers are useful anywhere portable, lightweight, and immutable images need to be reliably deployed. Serverless computing is useful in a variety of applications where workloads are highly variable and minimizing infrastructure management effort is a priority.

Serverless computing vs containers: Differences and how they can complement one another

As we can see, serverless computing and containers have some high-level similarities. They eliminate complexity and make it easier for teams to deploy and scale applications. However, there are several important differences to consider, including:

  • Cost structure. With containers — whether running on corporate or  in the cloud — enterprises pay for them as long as they’re on. With serverless computing, enterprises only pay for what they use. For workloads with consistent demand, this may not make much difference. For highly burstable workloads, this can lead to significant cost savings with serverless.
  • Testability. With containers, teams can easily test your applications anywhere. With serverless, teams are limited to the cloud platform running the functions and can’t perform the same level of testing against serverless functions.
  • Deployment. To scale a container-based application up or down, containers must be deployed or scaled back somehow (e.g. using Kubernetes). With severless, ylcode simply executes on a “black box” platform a vendor provides.
  • Operational complexity. That “black box” paradigm with serverless can be a big benefit for teams looking to minimize operational complexity. There is effectively no infrastructure to manage with serverless. With containers, it’s possible to offload infrastructure management to a provider, but that isn’t always the case.
  • Vendor lock-in. Containers can “run anywhere” but with serverless, enterprises are highly dependent on the platform that runs your code. For example, using AWS Lambda functions makes an app more dependent on the AWS  platform, while with Docker containers can be deployed on any platform that can run Docker.

Despite the differences, containers and serverless computing aren’t necessarily mutually exclusive. For example, it’s possible to use Docker to containerize serverless functions. Additionally, platforms like Google’s Cloud Run are designed to deploy containers using the pay-per-use serverless model.

Understanding Serverless vs Containers Security

Like the technologies themselves, serverless vs containers security is a nuanced DevSecOps topic.

Serverless does eliminate many of the security concerns associated with infrastructure management, but there are still many important serverless security considerations involved. For example, insecure serverless privilege configurations can create vulnerabilities in applications. Additionally, more functions and protocols to enable serverless workflows mean more potential attack vectors to protect. The offloading of complexity also comes with a security tradeoff: because service providers handle so much of the infrastructure, visibility into serverless deployments is limited.

On the other hand, container security comes with its own unique set of challenges. For example, securely sourcing and deploying only trusted containers — and keeping them patched — can be an operational challenge. Additionally, Identity and Access Management (IAM) and container configuration management are important aspects of a strong security posture.

Improving your serverless and container security with CheckPoint

While both approaches to developing applications reduce complexity, they don’t eliminate the need for a strong security posture and you need to integrate security into your development processes. Following the principle of least privilege and adopting “zero trust” policies are an important part of keeping your infrastructure secure, but Develops teams also need the technology and domain expertise to implement the right security solutions.

CheckPoint Software is purpose-built to address these challenges. For example, CloudGuard provides end-to-end multi-cloud security for all enterprise cloud assets, including serverless and container-based deployments. CloudGuard offers features such as threat prevention, cloud security posture management, cloud workload protection (for containers and serverless apps), and intelligent threat hunting.

Next steps: Learning more about serverless vs containers security

If you’d like to get started improving your container or serverless security posture, sign up for a free instant security check today. The check can help you identify misconfigurations that can jeopardize security and compliance across cloud environments. Alternatively, if you’d like to try CloudGuard for yourself, you can sign up for a free trial.

If you’re interested in learning more about serverless vs containers security, the free 

Serverless Security Advantage eBook and Guide to Container Security are a great place to start.

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