What is Serverless Computing and how does it work?

What is Serverless Computing and how does it work


What is Serverless Computing and how does it work?

Serverless computing is a relatively new approach to building and running applications in the cloud. It’s a way of abstracting away the infrastructure layer and focusing on writing code that…

What is Serverless Computing and how does it work?

Serverless computing is a relatively new approach to building and running applications in the cloud. It’s a way of abstracting away the infrastructure layer and focusing on writing code that responds to events. In this way, developers don’t have to worry about managing servers or handling scaling, which can save a lot of time and effort. Serverless computing allows developers to focus on writing code that solves business problems, rather than worrying about the underlying infrastructure. This article will explore what serverless computing is, how it works, and why it’s becoming increasingly popular in the tech industry.

Definition of Serverless Computing

Serverless computing is a cloud computing model that allows developers to write and run code without worrying about the underlying infrastructure. In this model, the cloud provider dynamically manages the infrastructure required to run the application, including servers, storage systems, and network resources. Unlike traditional computing models where you have to manage and provision servers, in serverless computing you only pay for what you use, as the service provider takes care of scaling up or down based on demand. This makes it easier for developers to focus on writing code rather than managing infrastructure, which can be time-consuming and costly.

Characteristics of Serverless Computing

Pay Per Use Model

The pay per use model in serverless computing means that users only pay for the specific amount of resources they use, rather than paying for a fixed amount of computing power and storage. This is in contrast to traditional computing models where users have to pay for a minimum level of computing resources regardless of whether or not they fully utilize them.

In serverless computing, users are charged based on the number of times their code executes and the amount of time it takes to run. This can lead to significant cost savings as users only pay for what they actually use. Additionally, this model encourages efficient coding practices as developers are incentivized to write code that runs quickly and uses minimal resources.


Auto-scaling is a critical feature of serverless computing that allows the system to adjust the resources allocated to an application according to its current demand. Essentially, it enables the serverless infrastructure to automatically increase or decrease the number of servers or computing resources needed for an application based on its usage patterns. This means that when the demand for an application increases, more resources are automatically added, and when demand decreases, resources are reduced as well. This helps ensure that your application is always running optimally and efficiently without any manual intervention. Auto-scaling is one of the key benefits of serverless computing because it allows organizations to avoid over-provisioning and paying for excessive computing capacity.

Event-driven Programming Model

In serverless computing, the event-driven programming model is used to trigger application functions in response to certain events or actions. This programming model allows developers to write code that responds to specific events, such as new data being uploaded to a database or a user clicking a button on a web page. These functions are executed only when needed and are not continuously running, which helps to optimize resource usage and reduce costs.

The event-driven programming model is crucial in serverless computing because it enables the architecture’s pay-per-use model. By only executing functions when they’re needed, businesses can avoid paying for idle resources, which is common in traditional computing models.

Additionally, the event-driven programming model allows for fast and scalable application development. Developers can quickly create new functions that respond to specific events without worrying about infrastructure management. As a result, serverless computing has become increasingly popular for building modern applications that require flexibility and rapid iteration.

However, this approach also requires careful planning and implementation as the application code must be designed around its triggered events. Additionally, developers must manage stateless applications that do not store any user data between function invocations.

Stateless Applications

Serverless computing architectures are built around the concept of stateless applications. In this context, stateless means that the application does not maintain any persistent data between transactions.

Instead, each transaction is treated as a new and independent event. The application processes the transaction and returns a response, but it doesn’t retain any information about the previous transactions.

This approach allows for a more scalable and efficient use of resources. Since there is no need to maintain persistent connections or state information, serverless applications can be easily scaled up or down based on demand.

Stateless applications are also easier to deploy and manage, as they do not require complex setup or configuration processes. This makes them ideal for use in cloud environments where resources are shared and distributed across multiple nodes.

Third-party Services

In serverless computing, third-party services refer to the external services that are utilized to perform various functions of an application, such as data storage, authentication, and APIs. These services are typically provided by cloud service providers like Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform.

Using third-party services in serverless computing allows developers to focus on writing code for their application’s core functionality rather than building and maintaining the infrastructure required for these auxiliary features. This approach helps to reduce the complexity of an application’s architecture and enables developers to rapidly develop and deploy their applications.

Third-party services also provide scalability benefits in serverless computing by allowing developers to leverage the provider’s infrastructure for handling varying levels of traffic without having to manage it themselves. Additionally, most cloud service providers offer consumption-based pricing models for their third-party services, allowing applications to scale up or down according to demand while only paying for what they use.

How Does Serverless Computing Work?

Overview of Traditional Computing Architecture

Traditional computing architecture refers to the traditional way of building applications where the application code is run on dedicated servers or virtual machines (VMs). In this architecture, developers have to manage the infrastructure, including scaling, patching, and monitoring. They need to estimate the amount of server capacity required for the application to run and provision servers accordingly. This approach involves high fixed costs as well as operational overhead.

The traditional architecture has a client-server model where clients send requests to servers, and servers respond with the requested data. This model typically involves setting up a load balancer in front of multiple servers to distribute incoming requests evenly across them. The load balancer helps in ensuring high availability and fault tolerance by redirecting requests to healthy servers if any server goes down.

In traditional computing architecture, developers are responsible for managing the entire infrastructure stack, from hardware to operating systems and middleware. This approach requires significant expertise in infrastructure management and maintenance from developers.

Moreover, traditional computing architecture is not well suited for applications with unpredictable traffic patterns because it requires setting up enough server capacity in advance to handle peak loads, leading to over-provisioning during non-peak hours.

Overview of Serverless Architecture

Serverless architecture is a cloud computing model in which the cloud provider manages the infrastructure and automatically provisions and scales resources as needed. The name “serverless” refers to the fact that developers do not have to manage servers, operating systems, or infrastructure, but instead focus on writing code for their applications.

In serverless architecture, applications are broken down into small functions that are executed in response to events triggered by user actions or system events. These functions run in stateless containers that are created and destroyed as needed, based on the demand for resources. This allows for efficient resource utilization and cost savings since users only pay for the resources they use.

Serverless architecture relies heavily on third-party services such as databases, messaging queues, and storage services. These services are integrated into the application using APIs or pre-built connectors, allowing developers to focus on writing business logic rather than infrastructure code.

One of the main benefits of serverless computing is scalability. Because resources are provisioned automatically based on demand, applications can scale up or down quickly without any additional effort from developers. This makes serverless architecture ideal for applications with unpredictable workloads.

However, there are also some drawbacks to serverless computing. For example, because functions run in stateless containers, they cannot store data between invocations. This can make it challenging to build certain types of applications. Additionally, while third-party services simplify application development by handling infrastructure tasks such as scaling and security, they can also introduce additional complexity and potential points of failure in an application.

Benefits and Drawbacks of Serverless Computing

Benefits of Serverless Computing:

  • Reduced Costs: Serverless computing offers a pay-per-use pricing model, which means that you only pay for what you use. This can result in significant cost savings as you don’t have to pay for idle resources.
  • Scalability: One of the key benefits of serverless computing is the ability to scale automatically to handle spikes in traffic. As your application grows, serverless platforms can scale seamlessly without any additional effort from your side.
  • Faster Time-to-Market: With serverless computing, developers can focus on writing code instead of managing infrastructure, which results in faster time-to-market and increased productivity.
  • Reduced Operational Overhead: Since the infrastructure is managed by the cloud provider, there is less operational overhead for developers to worry about. This means that developers can focus more on writing code and delivering new features.

Drawbacks of Serverless Computing:

  • Cold Start Issues: One of the major drawbacks of serverless computing is the issue with cold starts. When a function is invoked for the first time or after a period of inactivity, it takes longer to execute as the runtime environment needs to be initialized.
  • Vendor Lock-in: Since serverless computing relies heavily on third-party services and APIs provided by cloud providers, there is a risk of vendor lock-in. Switching providers or migrating applications can be challenging.
  • Limited Control over Infrastructure: With serverless computing, developers have limited control over the underlying infrastructure, which can be a concern for some use cases that require fine-grained control over resources.
  • Debugging and Monitoring Challenges: Debugging and monitoring serverless applications can be challenging due to their distributed nature and event-driven design.

Overall, serverless computing offers several benefits such as reduced costs, scalability, faster time-to-market, and reduced operational overheads but also has some drawbacks such as cold start issues, vendor lock-in concerns, limited control over infrastructure, and debugging challenges.