What Is Serverless Computing? How It Works, Benefits, Examples, and Security

What Is Serverless Computing? How It Works, Benefits, Examples, and Security

Building an application used to mean thinking about servers almost from the beginning. Someone had to choose the hardware, install an operating system, configure the runtime, apply patches, estimate future capacity, monitor performance, and decide what should happen when traffic suddenly increased.

Cloud computing removed some of that burden, but developers still often had virtual machines, containers, or infrastructure configurations to manage.

Serverless computing takes the abstraction further.

It allows developers to build and run applications without directly provisioning or managing the servers that execute their code. The cloud service provider handles much of the infrastructure, including resource allocation, scaling, maintenance, and the underlying execution environment. Developers concentrate more heavily on application code and business logic.

The name can be misleading because serverless applications absolutely use servers. The difference is that those servers are largely invisible to the development team.

This guide explains what serverless computing is, how serverless architecture works, the difference between FaaS and BaaS, common serverless use cases, advantages and limitations, and the security responsibilities that remain even when the infrastructure is managed for you.

What Is Serverless Computing?

Serverless computing is a cloud computing model that lets developers run applications and code without directly provisioning, configuring, or maintaining the underlying servers.

The cloud provider manages the infrastructure required to execute the workload and allocates computing resources when they are needed. Depending on the service, capacity can increase automatically when demand rises and decrease when activity falls. Many serverless services also use usage-based pricing rather than requiring customers to maintain permanently running server capacity.

Imagine an online retailer that needs to resize product images whenever a seller uploads a photograph.

With a traditional architecture, the retailer might maintain a server continuously so that it is ready whenever an image arrives. That server could spend much of the day doing very little.

With a serverless model, uploading an image can trigger a small piece of code. The cloud platform allocates the required computing resources, executes the code, creates the resized images, and releases those resources when the work is finished.

The development team does not need to maintain a dedicated server simply to wait for the next upload.

This event-based model is one reason serverless computing has become closely associated with cloud-native application development, automation, APIs, microservices, and real-time processing.

How Does Serverless Computing Work?

A serverless application usually revolves around events and triggers.

An event is something that causes a function or service to perform an action. It could be a user submitting a form, a file arriving in cloud storage, an API request, a database record changing, a scheduled time being reached, or an Internet of Things device sending data.

When the event occurs, the serverless platform allocates an execution environment and runs the required code. The application performs its task and returns a result or triggers another service.

The provider handles much of what would traditionally happen behind the scenes, including provisioning infrastructure, maintaining the runtime environment, allocating resources, and scaling capacity. Google Cloud and Microsoft both describe serverless systems as event-driven environments where computing resources are provided when code is triggered rather than requiring developers to maintain continuously running infrastructure.

Consider a simple contact form on a website.

When someone clicks Submit, that request could trigger a serverless function. The function validates the data, writes the information to a database, sends a confirmation email, and then finishes.

If ten users submit the form, the service handles those requests. If thousands suddenly arrive, the serverless platform can allocate additional capacity automatically, subject to the provider's service limits and configuration.

This ability to adjust resources according to demand is often described as automatic scaling or dynamic scalability. Some serverless services can also scale down substantially, including to zero active instances when nothing is running.

FaaS, BaaS, and Event-Driven Architecture

Serverless computing is often used as though it means the same thing as Function as a Service, but the terms are not completely interchangeable.

Function as a Service

Function as a Service (FaaS) allows developers to deploy individual pieces of code that execute when particular events occur.

Instead of deploying one large application, developers can create smaller functions with specific responsibilities.

One function might process an uploaded image. Another might validate a payment request. Another could generate a notification when a database value changes.

AWS Lambda is one of the best-known examples of FaaS. Microsoft offers Azure Functions, while Google Cloud provides serverless execution through services including Cloud Run and Cloud Run functions. IBM describes FaaS as central to serverless computing while noting that the broader serverless ecosystem extends beyond functions alone.

Backend as a Service

Backend as a Service (BaaS) provides ready-made backend capabilities that developers can use instead of building and maintaining them independently.

These services can include authentication, databases, cloud storage, hosting, messaging, and other backend functionality. Google Cloud identifies FaaS and BaaS as two major categories within serverless cloud computing.

A development team might therefore combine custom serverless functions with managed authentication, storage, messaging, and database services.

Event-Driven Architecture

Serverless systems work particularly well with event-driven architecture.

Rather than keeping one application process running continuously, different actions can occur in response to specific events.

For example, uploading an invoice might trigger one function to extract information, another to update a database, and another to notify the finance team.

IBM highlights serverless functions as a natural fit for event-driven and stream-processing workloads because small, stateless functions can respond independently to events generated by APIs, microservices, applications, or IoT devices.

This can make applications easier to divide into focused components, although it can also create additional complexity when many functions and services depend on one another.

What Are the Benefits of Serverless Computing?

Serverless computing became popular because it can remove significant infrastructure work from application development.

Less Infrastructure Management

Developers do not normally need to provision servers, install operating systems, or manually manage the physical infrastructure running each function.

The cloud provider handles much of that work.

This does not eliminate operations entirely. Teams still need to manage application configuration, deployments, permissions, observability, security, cost controls, and service dependencies. However, the amount of direct server administration can be significantly reduced.

Automatic Scaling

Serverless architectures are designed to respond dynamically to workload demand.

If requests increase, the provider can allocate more resources. When demand drops, the amount of active compute can fall again.

This can be especially useful for applications with unpredictable traffic, seasonal demand, scheduled workloads, or long periods of little activity.

AWS describes serverless technologies as capable of scaling from zero toward peak demand, while Microsoft emphasizes dynamic scaling for workloads whose traffic can change quickly.

Pay for Actual Usage

Traditional infrastructure may remain active and generate costs even when an application is idle.

Many serverless platforms instead use a pay-per-use or pay-per-execution model. Charges may depend on the number of requests, execution duration, allocated memory, computing resources, or other service-specific measurements.

For suitable workloads, this can reduce the cost of paying for idle capacity. However, serverless is not automatically the cheapest choice for every application. Frequently running or long-running workloads may sometimes be more economical using other compute models.

Faster Development and Deployment

When infrastructure management requires less attention, development teams can spend more time working on application functionality.

Individual functions can also be developed and updated independently in many architectures.

Google Cloud and AWS both identify reduced operational overhead and faster deployment as major advantages of serverless computing.

This fits naturally with modern DevOps and CI/CD workflows, where teams aim to test and deploy application changes frequently and consistently.

Better Developer Productivity

Serverless computing shifts a portion of infrastructure work to the provider.

Developers can focus on code, business logic, application features, and user experience instead of spending as much time maintaining servers.

That does not necessarily make an application simple. Distributed serverless systems can introduce their own architecture, debugging, security, and observability challenges. The productivity benefit comes primarily from reducing direct infrastructure administration.

Common Serverless Computing Use Cases

Serverless computing works particularly well when applications respond to events, experience variable traffic, or perform tasks for relatively short periods.

APIs and Web Application Backends

A serverless function can respond to an HTTP request and perform application logic without requiring a continuously running web server.

Combined with an API gateway, serverless functions can support backend services for websites, mobile applications, and microservices.

AWS provides an example architecture using AWS Lambda with Amazon API Gateway for web application business logic, while IBM identifies API backends as a common serverless use case.

Image and Video Processing

Media processing is highly suited to event-driven computing.

Uploading an image to cloud storage can trigger a function that resizes it, creates thumbnails, changes its format, scans metadata, or stores an optimized version.

The function only needs to run when new content arrives.

Google Cloud includes image and video processing among its current serverless use cases.

Real-Time Data Processing

Serverless functions can respond to streams of information as they arrive.

Examples include application logs, website activity, IoT sensor data, financial events, and analytics pipelines.

One event can trigger processing that enriches the data and forwards it to another service for storage or analysis. Google Cloud and Microsoft both identify real-time and stream processing as important serverless scenarios.

Scheduled Tasks and Automation

Not every serverless workload needs a user to trigger it.

Functions can execute according to a schedule.

An organization might generate a daily report, clean temporary data every night, check unused cloud resources, process backups, or perform routine administrative tasks.

AWS demonstrates scheduled batch processing using EventBridge, Step Functions, and Lambda, while Microsoft lists scheduled workflows among common serverless use cases.

Microservices

Serverless computing can support microservices architecture, where an application is divided into smaller services with clearly defined responsibilities.

A payment service, notification service, account service, and image-processing service might all run independently and communicate through APIs or events.

IBM identifies microservices as one of the most common serverless use cases because small services fit naturally with independent functions and automatic scaling.

CI/CD and DevOps Automation

Serverless functions can participate in build, testing, deployment, and infrastructure automation workflows.

For example, committing new code could trigger tests, generate an artifact, update an environment, or initiate another stage of a CI/CD pipeline.

Google Cloud specifically identifies CI/CD and DevOps workflows as serverless use cases.

Serverless vs Containers, PaaS, and Virtual Machines

Serverless computing is one of several ways to run applications in the cloud.

With a virtual machine, the customer generally has substantial control over the operating system and runtime but also has more responsibility for maintenance, patching, scaling, and capacity.

Containers package applications with their dependencies and provide more portability and control. However, teams still need to operate the container environment directly or use a managed platform to do it.

Platform as a Service (PaaS) removes much of the underlying infrastructure management but typically provides a continuously available application platform rather than the highly event-driven execution model associated with serverless functions.

Serverless goes further in abstracting infrastructure. The provider manages the execution environment and automatically provides resources when the function or service needs to run. Google Cloud compares serverless with PaaS, containers, and VMs primarily through differences in administrative burden, pricing models, maintenance, and scaling.

This does not make serverless universally better.

A development team that needs detailed operating system control, specialized hardware, long-running processes, predictable continuous workloads, or highly customized networking may prefer containers or virtual machines.

In practice, many cloud architectures combine several models.

What Are the Limitations of Serverless Computing?

Serverless architecture removes some problems but introduces others.

Cold Starts

A cold start can occur when a function has not executed recently and the cloud platform needs to prepare a fresh execution environment before running it.

That startup process can introduce additional latency.

Cold starts may not matter for asynchronous tasks or background processing, but they can become important for latency-sensitive applications. Cloudflare, Google Cloud, and IBM all identify cold starts or startup delay as a potential serverless limitation.

Vendor Lock-In

Different cloud platforms use different APIs, configuration models, triggers, identity systems, monitoring tools, and managed services.

An application deeply connected to one provider's serverless ecosystem can therefore be difficult to move elsewhere.

IBM and Google Cloud both identify vendor lock-in as a potential disadvantage of serverless architecture.

Less Infrastructure Control

The same abstraction that makes serverless convenient also means developers have less control over the underlying servers and execution environment.

This can create limitations for specialized workloads and may make certain performance investigations more difficult.

Debugging and Observability

A serverless application may contain dozens or hundreds of independently executing functions, API calls, event triggers, queues, storage services, and databases.

A problem may move across several components before producing an error.

Traditional debugging methods may therefore be insufficient. Centralized logging, tracing, metrics, monitoring, and correlation become particularly important in distributed serverless architectures.

Not Ideal for Every Workload

Serverless is often best suited to short-lived, event-driven, intermittent, or highly variable workloads.

Applications that execute continuously for long periods, require persistent local state, depend on specialized infrastructure, or need extremely predictable low latency may be better suited to another compute model. Google Cloud and IBM both highlight limitations around long-running workloads, execution constraints, and latency-sensitive applications.

Is Serverless Computing Secure?

Serverless computing can reduce some infrastructure security responsibilities, but serverless does not mean securityless.

The cloud provider manages the underlying infrastructure, but customers remain responsible for areas such as application code, identities, permissions, data, secrets, dependencies, configuration, and how functions communicate with other services.

IBM describes serverless security through the cloud shared responsibility model, with providers supplying infrastructure protections while customers remain responsible for securing application code and data. It also identifies IAM, SIEM, threat detection, and DevSecOps as relevant controls.

IAM and Least Privilege

Serverless functions frequently need permission to read storage, query databases, publish messages, access APIs, or retrieve secrets.

Giving every function broad administrative permissions makes development easier, but it can dramatically increase the impact of a compromise.

Each function should receive only the permissions required to complete its job.

This makes identity and access management (IAM) and least privilege especially important in AWS Lambda, Azure Functions, and Google Cloud serverless environments.

Protect Secrets and Credentials

Database passwords, API tokens, encryption keys, and other credentials should not be hardcoded into serverless functions or deployment packages.

Instead, sensitive values should be stored in an appropriate secrets management or key management service and retrieved securely by authorized workloads.

Google Cloud's own serverless integration example uses Secret Manager when a serverless service needs credentials for external APIs.

Secure Event Sources and APIs

Functions are often triggered by HTTP endpoints, message queues, storage events, databases, schedules, and other cloud services.

Security teams need to understand which sources are allowed to invoke each function.

Public APIs should use appropriate authentication, authorization, input validation, rate limiting, and monitoring. API gateways can provide another control layer for serverless application traffic. IBM specifically notes security, OAuth, rate limiting, logging, and routing among capabilities associated with API gateways.

Monitor Short-Lived Workloads

Serverless functions may execute for only a few seconds.

That makes good logging and monitoring essential because the workload may disappear long before someone begins investigating an incident.

Organizations should collect function logs, authentication events, configuration changes, API activity, errors, unusual invocation patterns, and relevant cloud audit logs in a central monitoring environment.

Secure the Development Pipeline

Serverless code still contains dependencies, libraries, packages, secrets, and configuration.

Security therefore needs to begin during development rather than after deployment.

DevSecOps practices can integrate dependency checking, secret scanning, static analysis, infrastructure configuration checks, access-policy review, and other controls into CI/CD pipelines. IBM specifically identifies DevSecOps as an important practice for securing serverless technologies throughout the development lifecycle.

Frequently Asked Questions

What is serverless computing in simple terms?

Serverless computing allows developers to run code and applications without directly managing the servers underneath them. The cloud provider manages the infrastructure, scaling, and execution environment while developers focus primarily on the application.

Are there really no servers in serverless computing?

Servers still exist. The term "serverless" means developers do not normally provision, configure, patch, or directly manage those servers. The cloud provider handles the underlying infrastructure.

What is an example of serverless computing?

AWS Lambda and Azure Functions are well-known serverless function services. Google Cloud also provides serverless application execution through Cloud Run and related function capabilities. These services can execute code in response to events without requiring developers to maintain dedicated servers.

What is the difference between serverless and FaaS?

Function as a Service is a major part of serverless computing, but serverless is broader. The serverless ecosystem can also include managed databases, storage, messaging, API gateways, event processing, and backend services where infrastructure management is abstracted from the developer.

Is serverless computing cheaper?

It can be cheaper for workloads that run intermittently or experience variable demand because customers often pay according to actual executions or resource consumption rather than continuously running capacity. It is not necessarily cheaper for every workload, particularly applications that run continuously for long periods.

What is a cold start in serverless computing?

A cold start is the additional delay that may occur when a cloud platform needs to initialize an execution environment before running a serverless function that was not already active. The effect varies between providers, configurations, and workloads.

Is serverless computing secure?

Serverless applications can be secured effectively, but the cloud provider does not handle every security responsibility. Organizations still need to protect application code, data, IAM permissions, secrets, APIs, dependencies, event triggers, and monitoring.

Conclusion: Serverless Removes Server Management, Not Responsibility

Serverless computing changes how developers think about cloud infrastructure.

Instead of provisioning servers and keeping them running in anticipation of future demand, teams can build applications around functions, managed services, and events. The cloud provider handles much of the underlying infrastructure, while automatic scaling and usage-based pricing can make the model particularly effective for variable or event-driven workloads.

Its advantages are significant: less infrastructure administration, automatic scaling, faster development, flexible resource use, and strong support for APIs, microservices, automation, data processing, and cloud-native applications.

But serverless is not the right answer to every workload. Cold starts, provider dependencies, reduced infrastructure control, distributed debugging, and workload-specific cost considerations all need to be evaluated.

Security also remains essential. AWS Lambda, Azure Functions, and Google Cloud serverless workloads still depend on secure IAM permissions, secrets management, API protection, monitoring, application security, and well-designed cloud architecture.

If you want to go beyond the basic serverless computing model and understand how to protect functions, permissions, APIs, secrets, and event-driven workloads across the major cloud platforms, explore our Serverless Security For AWS Lambda Azure Functions And GCP course.