Containers vs Serverless: Choosing the Right Compute Model for a New Service

When you search for containers vs serverless, you will find plenty of results listing their advantages and disadvantages, and then ending with "it depends". These results are good for covering technical knowledge, but they don't help much when you are actually building a new service and genuinely need to make a decision.

Here's a better way to think about it: look at how your application will actually be used, and not which technology option sounds more modern.

Before choosing between containerized applications and serverless computing, start with one question: what kind of workload will your service have?

In this blog, we will learn when to use containers, when to use serverless, and how to decide between them.

Containers vs Serverless: The Core Difference

Before deciding which one fits your application, let’s compare containers and serverless side by side.

The simplest way to remember the difference is this: containers give you more control; serverless gives you more operational simplicity.

Now, once you know the basic differences, the real question is which one fits your application.

What Should You Use for Your App: Containers or Serverless?

Before you pick a compute model, ask one simple question: will your application be busy most of the time, or mostly idle with occasional bursts of activity?

As a general rule:

• If your application is idle most of the time and only runs occasionally, serverless can be a more cost-efficient option.
• If your application runs constantly or handles heavy, predictable traffic, containers can become a cost-efficient option.

Why does this happen? Serverless charges generally depend on factors such as the number of requests, execution time, and memory used. This is feasible when requests are rare. But when an application is continuously working, those small per-request charges add up fast; sometimes it ends up costing way more than just running a container 24/7.

A simple example: consider an image processing service handling 50,000 images per day. Depending on execution time and memory requirements, running this workload on serverless can become more expensive than using a continuously running container.

So do not assume that serverless is always cheaper or that containers are always cheaper. Your traffic pattern, execution time, memory requirements, idle time, and scaling behavior matter.

Why Did Prime Video Move Its Workload from Serverless to Containers?

One of the best-known examples of this comes from Amazon. Prime Video's video quality checker was initially built as a serverless microservices setup. They used components such as AWS Lambda and AWS Step Functions, with many small functions constantly saving and reloading data with one another.

It worked fine during the initial testing phase, but the team found that the architecture did not scale efficiently as traffic increased. Each shift between functions added processing overhead and cost.

The team changed the architecture by combining processing components into a single program within a single container, keeping data in memory instead of reloading it constantly. The redesigned service was built on Amazon EC2 and Amazon ECS, and the team reported over a 90% reduction in infrastructure costs.

This example shows that serverless can be a poor fit when an application performs intensive processing with many small steps that constantly communicate with one another.

For a workload like this, containers can provide greater control over an application's use of memory, compute resources, and data.

The right compute model depends on the workload and not on which technology is more popular.

When Should You Use Serverless Instead of Containers?

Serverless is particularly useful for event-driven applications with unpredictable or intermittent demand.

Now, in this example, during the COVID-19 pandemic in 2020, Coca-Cola wanted a touchless way for customers to order from its Freestyle vending machines with their phones instead of the shared touchscreen.

The team built it on AWS Lambda, a serverless computing service, and had it running in about 100 days. This approach helped Coca-Cola launch quickly without having to manage the underlying servers.

Containers would have meant guessing capacity in advance for unknown demand. Serverless lets them launch quickly and pay only for incoming requests, making it a smarter choice for unpredictable workloads.

What Should You Consider When Choosing Between Containers vs Serverless?

Instead of comparing long lists of features, ask yourself the following questions.

Is my traffic steady or does it get busy only sometimes?

Steady traffic often favors containers, while unpredictable traffic or intermittent traffic can favor serverless.

Does my app have many connected steps?

If one request triggers many small functions, a container may be simpler and more efficient for tightly connected processing steps.

Do I need instant, predictable responses every time?

Serverless functions can have cold starts. If speed matters every single time, containers can provide more predictable startup behavior because the service remains running.

Might I need to switch cloud providers later?

Containers are generally more portable. Serverless tools are often tightly tied to a single cloud provider, which can make switching harder.

These questions can help you choose the right compute model based on your application's actual needs.

Can We Use Containers and Serverless Together?

Yes. You don’t always have to choose between containers and serverless for your entire application.

You can choose containers for steady, always-on services and serverless for occasional or unpredictable tasks such as notifications, image processing, or scheduled jobs. This hybrid architecture lets you choose the compute model based on the needs of each part of your application.

Using both lets teams match the compute model to each workload instead of forcing every component into the same architecture.

So, before you decide between containers and serverless, don't ask which technology is trending. Ask how your app will behave once real users start using it and let that answer guide your choice.

Conclusion

Choosing between containers and serverless is also a long-term architecture decision. Containers are often a better fit for steady workloads. Serverless can work well for event-driven applications, unpredictable traffic, and teams that want less infrastructure management. Consider how your workload behaves today and how it may change before choosing a compute model.

FAQs

1. Is Kubernetes serverless or a container technology?
Answer: Kubernetes is a tool for managing and orchestrating containers; it is not serverless. It automates much of the manual work, which is why people often confuse the two.

2. What is AWS Fargate? Is it serverless or containers?
Answer: AWS Fargate is a serverless compute engine for running containers. You still package your application as a container, but AWS manages the underlying servers and infrastructure for you.

3. Which is easier for a small team with no cloud experience?
Answer: Serverless is usually easier to start with; you write code and deploy it. Containers need more setup knowledge but give you more control later.

4. Are containers better than serverless?
Answer: Neither is universally better. Containers provide more control and portability, while serverless reduces infrastructure management. The better option depends on workload traffic, performance, and operational costs.

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