What Is Serverless Computing: CPU and GPU for Containers

Serverless computing might sound like a strange concept to newcomers, but it’s surprisingly easy to understand once you dive in. More importantly, it can genuinely save you both money and hassle.

By the end of this article, you’ll have a clear understanding of how serverless computing works, its benefits, and the many use cases where it truly adds value.

What is Serverless Computing?

To put it as simply and directly as possible, serverless computing allows developers to focus exclusively on building and running applications without needing to think about the underlying infrastructure.

Typically, applications run on servers that require constant attention.

Businesses or developers must manage, maintain, and monitor these servers, taking care of things like server uptime, security, scaling (adjusting resources based on traffic demands), and many other tasks that consume time and effort.

With serverless computing, the cloud provider (like Latitude.sh) takes care of all of this behind the scenes. Developers don’t need to manage any technical details directly—it’s all handled by the provider.

The term “serverless” can be a bit misleading because physical servers are still involved in running the applications. However, from the user’s perspective, it feels as if those servers don’t even exist. Get it?

In serverless computing, functions only run in response to specific events or triggers, which means they’re not running constantly. Examples of these triggers include user requests, scheduled tasks, or newly uploaded data.

This kind of setup enables automatic scaling, which is invaluable when handling sudden surges in traffic. There’s no need for manual adjustments.

It also means that when there’s no activity, serverless functions don’t run, leading to reduced costs—you pay only for what you actually use rather than keeping servers running continuously.

To sum it up simply: serverless computing lets developers focus on writing and deploying code without worrying about the underlying infrastructure, while the cloud provider takes care of the digital operations.

How Does Serverless Computing Work?

Now that you're aware of the basics, it's time to dive a little deeper into how these specific events and triggers automatically work.

To understand how serverless computing works, imagine a setup where applications are automatically launched in response to specific events or triggers, such as a user request, a database change, or a timed task.

Whenever a trigger (user request, database change, timed task) occurs, a function is executed in a contained, isolated environment.

This approach is particularly efficient because resources are allocated only as needed, which prevents wasted capacity and ensures that no excess infrastructure sits idly.

In most cases, serverless functions are stateless, meaning each instance operates independently within its own container.

These containers spin up quickly when triggered, complete the task, and then shut down, freeing up resources.

This process, known as automatic scaling, allows for seamless adjustment to any increase or decrease in demand—whether it’s a single request or a surge in activity.

With serverless computing, businesses can respond to real-time traffic patterns without any manual scaling efforts, providing an infinitely flexible and responsive infrastructure.

Serverless Computing Examples

The versatility of serverless computing has led to its adoption across numerous industries and applications. Here are a few prominent serverless computing examples:

Batch Processing: Serverless computing is ideal for running high-volume data processing tasks that don’t need to run continuously, such as video encoding, data transformation, and image processing. With serverless, you can process massive amounts of data in parallel without worrying about managing infrastructure—when the job is done, the resources automatically shut down.

Machine Learning Inference: For real-time predictions, serverless computing allows you to deploy trained machine learning models in containers powered by GPUs. This setup provides the computing power required for intensive inference tasks without the cost or complexity of managing dedicated hardware.

IoT Data Processing: Serverless is perfect for handling and analyzing large data streams from IoT devices. With the ability to respond instantly to real-time data, serverless functions can analyze incoming information, trigger alerts, or perform actions based on specific conditions, enabling efficient and responsive IoT solutions.

Continuous Integration (CI) Pipelines: Automating testing and deployment is streamlined with serverless computing. By using containers within a serverless architecture, developers can build CI/CD pipelines that automatically test and deploy new code, allowing rapid development without infrastructure overhead.

Web Scraping: Serverless computing is highly effective for automating data collection tasks, such as web scraping. The flexibility of containerized serverless environments lets you schedule scraping tasks as needed and scale them up or down depending on demand, all while keeping infrastructure costs low.

What Does It Mean to Be Serverless?

In the context of cloud infrastructure, what it means to be serverless is closely linked to flexibility and agility. A serverless environment eliminates the need for extensive infrastructure planning, empowering development teams to innovate faster, iterate more effectively, and minimize operational overhead.

Serverless with Latitude.sh

Latitude.sh’s serverless platform exemplifies the serverless computing model with a powerful infrastructure optimized for different types of workloads, letting users run resource-intensive applications on serverless containers.

Differently from traditional serverless options commonly found at hyperscalers, Latitude.sh serverless platform offers some key functionalities to increase operational and cost efficiency to its users: Dedicated GPU infrastructure: Unlike many serverless platforms that limit users to shared resources, Latitude.sh offers dedicated CPUs and GPUs, giving you consistent performance and control over containerized applications.

Per-minute billing: Latitude.sh uses a per-minute billing model, allowing you to scale resources up or down based on traffic demand and only pay for what you actually use.

Fast scalability: With Latitude.sh, serverless containers can scale automatically with traffic, reducing idle time and ensuring you always have the right resources allocated. Moreover, the platform also simplifies the deployment process with a Blueprint library, offering pre-built templates for even faster setups.

Whether you need containers for batch processing, machine learning inference, IoT data processing, or continuous integration pipelines, Latitude.sh delivers an efficient serverless solution tailored to a wide range of workloads.

If you want to try reliable and easy-to-manage serverless computing, create a free account right now.