Serverless, or serverless computing, is an approach to software development that empowers developers to build and run application code without having to worry about maintenance tasks like installing software updates, security, monitoring and more. With the rise of cloud computing, serverless has become a popular tool for organizations looking to give developers more time to write and deploy code.

Despite its name, a serverless framework doesn’t mean computing without servers. In a serverless architecture, a cloud service provider (CSP) handles tasks like server management, back-end infrastructure, provisioning of servers, creating backups and more. Another advantage of serverless technologies is that they allow cloud providers to provision resources on an on-demand model. With serverless, billing only starts when code execution starts and ends when it ends.

Enterprise benefits of serverless

With the growth of cloud computing, individuals and companies need to use and store data differently than they did in the past. To accomplish this, enterprises are relying more than ever on cloud functions and reducing their dependence on on-premises infrastructure. Today, all leading CSPs, including Amazon Web Services (AWS Lambda), Microsoft Azure (Azure Functions) and IBM (IBM Cloud Code Engine) offer serverless platforms.

Along with other critical infrastructure technologies, like infrastructure as a service (IaaS), platform as a service (PaaS) and function as a service (FaaS), serverless helps organizations in several important ways:

• Increased focus on business logic: With serverless functions, developers can focus more on writing code and less on managing underlying infrastructure.
• Decreased stack implementation: Serverless environments outsource stack implementation, the operations going on behind the scenes when you write and deploy your code to a third-party provider.
• Reduced latency: In a serverless environment, code runs closer to the end user, decreasing its latency, which is the amount of time it takes for data to travel from one point to another on a network.
• Shorter DevOps cycles: Serverless simplifies DevOps by allowing developers to reduce the amount of time they spend defining infrastructure needed to deploy code.

How does serverless work?

Serverless represents the latest in a series of architectures for coding environments that have been evolving to allow programmers to focus more on what they do best—writing and deploying code. The three other architectures that are relevant to the development of serverless are bare metal servers, virtual machines (VMs) and containers. Here’s a closer look at each one.

• Bare metal servers: Bare metal architecture requires developers to configure and manage each server manually (as well as the environment where they are deploying code). In a bare metal environment, developers must install the operating system (OS), and manage patching, along with other routine and often time-consuming tasks.  
• Virtual machines: Virtual machines (VMs) are better at resource optimization than bare metal servers, and they are also better at handling idle times. But with a VM, developers are still responsible for setting up their own environment, installing their OS and patching.
• Containers: Popularized by Docker, containers allow deployment and application code to be packaged in a way that it can be run on any underlying infrastructure. In terms of deployment, containers are a much simpler architecture than bare metal or VMs, but aren’t ideal when it comes to scaling web apps.

Serverless and function as a service (FaaS)

In a serverless environment, function as a service (FaaS)—a service that allows customers to run code in response to events—is critical to freeing up developers from managing the underlying infrastructure. With FaaS and serverless computing, developers can exclusively focus on application development.

As we saw with bare metal, VMs and containers, hosting an app typically requires provisioning and managing a server, along with installing and managing an OS. With FaaS, the physical hardware, VM OS and web server software management are all handled by the CSP. FaaS allows developers to deploy serverless applications and workflows seamlessly, and the terms FaaS and serverless are often used interchangeably.

Serverless and platform as a service (PaaS)

Platform as a service (PaaS) is a cloud computing model that provides customers a complete cloud platform that’s more cost-effective and less complex than building and maintaining a platform on-premises. While both serverless and PaaS architectures keep the backend invisible to developers, the similarities end there. PaaS environments offer more control over deployment environments but also require more management.  Applications in PaaS must be manually configured to scale and can take longer to spin up than with serverless.

Serverless and Infrastructure as a service (IaaS)

Infrastructure as a service (IaaS) is a cloud service that delivers resources like compute, servers and VMs over the internet on a pay-as-you-go basis. IaaS lets users scale quickly, reducing the need for high, up-front capital expenditures that come with buying, setting up and maintaining on-premises infrastructure. In an IaaS model, users buy capacity upfront. In a serverless model, an event triggers app code to run. Essentially, IaaS charges users upfront for resources while serverless only charges users for the time code was actually executing.

Enterprise use cases for serverless

As cloud computing continues to expand, and businesses look for new ways to leverage the technology to create new business value, serverless use cases are growing rapidly.

Artificial intelligence (AI) and machine learning (ML)

The last few years have seen massive growth in business use cases for artificial intelligence (AI) and machine learning (ML) applications, especially in generative AI. Specifically, serverless helps enable something called event-driven AI, where a constant flow of intelligence informs real-time decision-making capabilities. Additionally, serverless can help solve the challenge of scalability when it comes to building new AI and ML solutions by allowing developers to focus on training instead of the underlying infrastructure.

Microservices

Microservices architectures are one of the most popular use cases for serverless. Microservices models are a cloud-native architectural approach in which a single application is composed of many loosely coupled and independently deployable smaller components or services. Serverless computing’s capabilities around small bits of code, automatic scaling, rapid provisioning and its on-demand pricing model make it ideal for microservices architectures.

Hybrid cloud

Hybrid cloud combines public cloud, private cloud and on-premises infrastructure to create a single, flexible, cost-optimal IT infrastructure. Serverless helps support enterprises who are adopting a hybrid cloud strategy by providing the agility, flexibility and scalability needed to accommodate fluctuating workloads across different cloud environments, including public, private and edge. 

Big data analytics

Serverless dramatically reduces the cost and complexity of writing and deploying code for big data applications. Serverless environments allow developers to focus on their code and business logic, rather than all the routine tasks of managing infrastructure. Additionally, serverless’ always-on capabilities mean data pipelines can be designed in a way to react to real-time changes in data and change application logic accordingly. Today, serverless helps developers build scalable big data pipelines without having to manage the underlying infrastructure.

Internet of Things (IoT)

Serverless’ event-driven capabilities, automation and high scalability make it ideal for the data processing required by Internet of Things (IoT) applications. Automated serverless functions are stateless and designed to handle individual events. Additionally, serverless’ flexible compute power allows developers building IoT applications to adapt to a wide range of connection and data processing needs when they are working with widely dispersed nodes. As the number of IoT devices grows, serverless is uniquely poised to assist developers in providing strong foundations for the event-driven data analytics critical to IoT applications.  

API gateways

An API gateway is software that takes an application user’s request, routes it to one or more backend service, gathers the appropriate data and delivers it to the user in a single, combined package. Serverless models are used as proxies to web application actions and provide HTTP method routing, client ID and secrets, rate limits, CORS, viewing API usage, viewing response logs and API sharing policies.

In a serverless model, the API gateway can be used to build REST API services and trigger code associated with each event. Events and functions in a serverless environment can be transformed into HTTP endpoints. These actions—known as “web actions”—can be collected into a full-featured API with an API gateway that adds an additional layer of security and customized support. Additionally, API gateways give developers the ability to access a variety of tailored integrations for authentication that can help reduce time-to-market while complimenting developer productivity.

Chatbots

Chatbots like IBM watsonx Assistant and Microsoft’s ChatGPT are well suited for serverless environments because of serverless’ pay-as-you-go pricing model that doesn’t require users to pay for resources they aren’t using. Serverless helps organizations of all sizes use chatbots for a variety of tasks, such as increasing customer engagement and automating services that previously required human input.

 Embarrassingly parallel tasks

Serverless runtimes are well suited to embarrassingly parallel tasks, which are compute tasks that can be easily broken up into smaller tasks and performed in parallel This is because in a serverless architecture, each parallelizable task can result in the invocation of an action. In a serverless environment, embarrassingly parallel workloads can be split into many sub-tasks, all running independently from each other. 

Examples of parallel tasks on a serverless runtime include Monte Carlo simulations, batch processing, video transcoding, processing objects on object storage, model scoring, web scraping and business data streams.

Learn more

As organizations of all sizes, and across a wide range of industries, seek to leverage cloud computing to help achieve their digital transformation goals, serverless computing is playing a pivotal role. By freeing developers from mundane tasks like installing and updating OS, provisioning servers and scaling infrastructure, serverless environments help developers to focus on business logic and writing and deploying code.

Explore Serverless on IBM Cloud