The serverless computing market is experiencing significant growth, as it helps companies to save time and money on application development. According to Grand View Research, the worldwide market for serverless computing was valued at approximately USD 24.51 billion in 2024 and is expected to expand at a compound annual growth rate of 14.1% between 2025 and 2030.

In this article, we explore the essence of serverless computing, define it, compare it to other computing methods, and discuss its future. We'll begin with definitions, then provide a brief overview of cloud computing and its evolution toward serverless computing, and conclude with future predictions.

What is serverless computing?

The term “serverless computing” might seem misleading because it indeed involves servers. However, developers don't need to manage the underlying infrastructure, as a serverless provider handles it. This shift has changed backend development services, allowing teams to concentrate on code and functionality rather than server management. Before serverless computing, cloud computing allowed developers to rent server space without owning physical servers.

Serverless computing is not opposed to cloud computing; instead, it's a part of it. Serverless cloud computing offers scalability, so a company doesn't need to over-purchase space fearing resource shortages. A business only pays for the space it uses.

Understanding serverless computing

The core components of serverless computing include serverless architecture, serverless functions, backend services, and event-driven execution. Below, we dive deeper into each of these components:

Serverless architecture

Serverless architecture changes how applications are designed, particularly for cloud-native applications. Unlike traditional server-based architectures that require managing and scaling hardware resources, serverless architecture abstracts these responsibilities. It allows developers to deploy applications as independent, stateless functions or services. This approach simplifies scalability, as the cloud provider manages the infrastructure and automatically adjusts resources to handle incoming requests or workloads.

Serverless functions

Serverless functions, often referred to as Function-as-a-Service (FaaS), are the building blocks of serverless architecture. These are small, single-purpose, and stateless pieces of code that can be triggered by various events or HTTP requests. Each function executes independently and can scale automatically, based on the demands placed on the serverless application. A key advantage of serverless functions is the ability to deploy and run them in response to events, unlike in traditional server-based architectures where resources are used constantly. They break down monolithic applications into microservices, which makes them more flexible, maintainable, and speeds up development cycles.

Backend services

A backend service in the serverless computing model is an external service that developers can use to provide the necessary functionality to their applications without managing the infrastructure. These services include databases, authentication, storage, messaging queues, and other third-party APIs. By using managed backend services, developers can offload the operational complexity and focus solely on coding the serverless application logic. Cloud infrastructure providers like AWS, Azure, and Google Cloud offer a wide array of managed services that can be integrated into serverless applications, allowing for an architecture that's easy to customize and build upon. We'll explore these aspects in more detail later.

Event-driven execution

Event-driven execution is a core principle of serverless computing, where actions within the application are triggered by pre-defined events. These events can originate from various sources, such as changes in a database, user-driven actions, scheduled events, or system occurrences. When an event occurs, the cloud provider automatically allocates resources to execute the corresponding serverless function. This leads to efficient resource usage, minimal idle time, and cost-effective execution, as resources are allocated and consumed only when needed.

Serverless platforms and providers

There are many providers offering serverless computing services. Here are some of the most popular ones, along with their features and common use cases.

Amazon Web Services

AWS serverless computing allows developers to build and run apps without managing servers. It provides automatic scaling, built-in high availability, and a pay-for-use billing model. AWS handles infrastructure tasks such as capacity provisioning, patching, and monitoring.

Key features: The foundation is AWS Lambda, an event-driven compute service integrated with over 200 AWS services. Other key services include Amazon API Gateway, Amazon DynamoDB, AWS Step Functions, Amazon S3, Serverless Framework, and machine learning services like Amazon Comprehend and Amazon Rekognition.

Use cases: AWS is used for web development of serverless apps, data processing at scale, batch processing automation, and indexing or storing documents and images automatically. It helps building event-driven serverless apps with Lambda and API Gateway or processing data triggered by S3 events.

Google Cloud platform

Google Cloud offers serverless computing primarily via Cloud Run and Google Cloud Functions. Cloud Run allows deployment of containerized applications with automatic scaling and pay-per-use billing. Google Cloud Functions is an event-driven function-as-a-service platform that runs code in response to HTTP requests, database changes, or cloud storage events without server management. A cloud function, while synonymous with a serverless function, is a more specific term that refers to pieces of code dedicated to performing single tasks within this framework.

Key features: Cloud Run supports multiple languages and frameworks, manages infrastructure automatically, and scales to zero when not in use. Cloud Functions provides automatic scaling and event-driven execution, ideal for lightweight, single-purpose functions.

Use cases: Google cloud platform allows developers to build websites with dynamic content, API backends, event-driven processing, and lightweight microservices triggered by cloud events.

Azure functions

Azure Functions is Microsoft Azure's serverless compute service that helps you to run application code on-demand without managing infrastructure, similar to Google Cloud Functions and Cloud Run. It allows developers to focus on writing the code that matters most, while Azure handles provisioning, scaling, and maintenance of the underlying resources.

Key features: Azure supports multiple programming languages and offers automatic scaling and flexible hosting through various plans. It provides easy connections with other Azure services and third-party options, works with development tools, and has built-in performance monitoring and diagnostics.

Use cases: It helps with building lightweight, serverless APIs, implementing microservices with independently deployable functions, supporting event-driven workflows, processing real-time data streams for AI, handling webhooks and external events, and executing scheduled tasks like data cleanup.

IBM platform

IBM Cloud Functions is a serverless platform built on the Apache OpenWhisk open-source project. It allows developers to build and run code without managing infrastructure, supporting event-driven execution and automatic scaling.

Key features: The IMB platform supports multiple programming languages, including Node.js, Python, Swift, Go, and Java; integrates with IBM Cloud services like Cloudant database, Watson AI, and Event Streams messaging. It is designed for cost efficiency with pay-as-you-go pricing.

Use cases: The IMB Platform is suitable for API backends, real-time data processing, IoT applications, chatbots and voice interfaces, and scheduled jobs such as database cleanup or notifications.

Cloudflare Workers

Cloudflare Workers is a serverless platform that runs JavaScript functions at the edge of Cloudflare's global network. It uses an isolate-based execution model, which allows fast startup and low memory usage compared to a traditional container-based serverless computing platform.

Key features: Cloudfare Workers runs code close to end users to reduce latency, supports complex logic across multiple domains and routes, integrates with Cloudflare's developer platform services like storage and AI, and provides a highly efficient runtime using the V8 engine.

Use cases: It handles edge computing tasks like modifying and responding to HTTP requests, building distributed applications with low latency, and integrating with other Cloudflare services for better performance and security.

Key benefits of serverless computing

Reduced operational overhead

Serverless computing removes the need for server management and infrastructure maintenance. Unlike traditional servers, the infrastructure, including operating systems, servers, virtual machines, containers are managed by the cloud provider.

Cost efficiency and cost savings

Reducing costs involves a pay-per-use pricing model, eliminating idle resource costs, and providing budget predictability. In serverless development, there's no need to overpurchase space to prevent nonfunctionality.

Scalability

A crucial aspect of serverless is the ability to “scale down to zero” when the function is not being used. If there are no events to process, the platform releases the allocated resources and you are not charged. This is the core of the “pay-as-you-go” cloud computing model: you only pay for the actual compute time used by your function, down to the millisecond in some cases.

Improved developer productivity

The emphasis is on code and innovation rather than infrastructure. The development team concentrates on generating ideas and writing code.

Reduced latency and performance gains

Serverless computing automatically scales resources in response to demand, which means that functions are readily available to handle requests without delay. Additionally, providers often deploy functions closer to users geographically, minimizing data travel time.

Limitations of serverless computing

Even though serverless computing is great for scalable development, it presents some challenges, including:

Cold start latency: This refers to the delay when functions are invoked after inactivity. The time taken to initialize them is called a “cold start.”

Vendor lock-in and portability: Serverless computing often depends on specific tools and APIs provided by a cloud service, making it difficult to switch to a different provider. This can complicate migration between cloud platforms.

Debugging and monitoring complexity: It's harder to see what happens behind the scenes in serverless applications because there isn't a traditional server to monitor. The event-driven and distributed nature of serverless functions makes testing and identifying issues more challenging.

Stateless constraints: Serverless functions do not maintain state between executions, as they are designed to be temporary and stateless. This necessitates external systems to manage any persistent data or state requirements.

Limited runtime and language support: Cloud providers often have restrictions on which programming languages and runtime environments are supported for serverless functions. These limitations might affect the choice of technology stack and how applications are built.

Monolithic vs microservice vs serverless applications

Serverless architecture is often compared to monolithic and microservice architectures in app development. Let's explore how these differ.

Monolithic apps

In monolithic architecture, developers build, deploy, and scale an app as a single unit. This approach closely ties application logic to backend infrastructure, simplifying initial development and deployment for small to medium-sized apps. However, as apps become more complex, monolithic architecture can slow development cycles and make it difficult to scale individual components. Each update or scaling action affects the entire app. By using serverless architecture, developers can break down monolithic applications into separate, cloud-managed functions, which improves flexibility and efficiency.

Microservice apps

Microservice architecture divides an app into various independently deployable services, each covering a specific business function. Services communicate through defined APIs and can be developed, deployed, and scaled independently. This allows teams to choose the best technologies and strategies for each service. At Ronas IT, we use microservice architecture for projects requiring stable, secure operations, often managing different parts of the app with separate databases. One example is our work on the Neobank application.

Serverless apps

Serverless architecture removes the need to manage server infrastructure. You define necessary server-side functions within a serverless provider like Google Cloud Functions or AWS Lambda, and these providers execute functions on demand from the client application. This architecture allows serverless applications — whether originally monolithic or microservice-based — to achieve broader scalability and resource optimization by deploying individual functions. However, remember that serverless and microservices, though they complement each other, are not entirely interchangeable. Serverless apps might not suit long-running or compute-intensive tasks due to execution limits and state management challenges.

Serverless architecture in the context of cloud services and hybrid cloud

To clarify the relationship between serverless computing and cloud services, serverless can be seen as an evolution of cloud technology. While cloud services minimize the physical aspects of server management, they still require users to oversee the setup and optimization of virtual resources, which includes tasks like configuring instances, managing security, updating software, and scaling resources to meet demand. In contrast, serverless computing automatically manages resource scaling.

Serverless can also be used in hybrid strategies and multicloud environments. Hybrid cloud computing combines on-premise and cloud storage, while multicloud refers to the simultaneous use of several cloud providers. In these environments, serverless can seamlessly integrate and connect on-premise and cloud-based services, providing a unified platform for executing functions that interact across environments.

Practical applications and use cases of the serverless approach

Speaking of the serverless computing role in application development, it can be helpful in the following cases:

Web and mobile backends

Serverless computing is well-suitable for developing scalable and cost-effective backends for web and mobile applications, handling API requests and user interactions efficiently.

Data processing and ETL pipelines

It streamlines data processing tasks like Extract, Transform, Load (ETL) pipelines, which involve extracting data from different sources, transforming it into a suitable format, and loading it into a database or data warehouse. This automation of data flow and transformation happens in real-time to support analytics and reporting.

Chatbots and voice assistants

The event-driven nature of serverless computing makes it perfect for building chatbots and voice assistants that require apps to handle interaction in real-time.

Real-time file and image processing

Serverless platforms efficiently manage tasks such as file conversions and image processing, responding to uploads or changes with minimal delay.

The future of serverless computing in application development

The future development of serverless computing is, first of all, connected with overcoming its limitations such as cold start latency. Let's see what other directions serverless computing will further evolve in.

Edge computing integration for ultra-low latency

Serverless functions are increasingly being run at edge locations, which are data centers located closer to users. This serverless environment reduces the distance data needs to travel, cutting down on delays in transferring information. By deploying functions at these edge locations, companies can offer faster responses for applications where speed is critical, like virtual reality or online gaming. Although these functions might face some limits, like shorter run times, they are great for applications that benefit from quick response times.

Emergence of stateful serverless applications

Traditionally, serverless functions do not store data between executions, which means they rely on separate storage solutions. Looking ahead, serverless systems are evolving to manage data within their workflows, allowing them to remember and maintain information across different executions. This evolution simplifies building serverless apps like shopping carts or session management, which need to keep track of user actions without calling an external database constantly.

Broader industry adoption and core application use

Serverless computing is not just for simple or background tasks anymore; it's gaining traction for essential business applications due to its ability to scale efficiently and cost-effectively. Industries such as finance, healthcare, and retail benefit from serverless computing's ability to quickly adjust to changes in demand while maintaining high security and compliance. Serverless technology speeds up development cycles and helps manage complex, data-heavy applications.

Improved developer tooling and ecosystem maturity

Advances in serverless computing tools are making development less complex. These tools include local emulators that replicate environments for easier debugging, CI/CD pipelines that automate building, testing, and deploying code, and improved monitoring solutions that offer real-time insights into performance and errors. Regarding security, reliable vendors already offer security tools to audit a serverless environment, for example, Google Cloud Security Command Centre, AWS Config, and Azure Security Centre. AI-driven analytics help identify issues and suggest improvements. By reducing the technical overhead, these tools make serverless development more accessible to more developers. Improved deployment processes and automated scaling make backend management simpler.

Mitigation of cold start latency

A cold start happens when a serverless function has to load from scratch, causing delays. The most effective strategies to reduce these delays include keeping some functions ready to go, optimizing the way code runs, and minimizing the code that needs to load initially. These steps make serverless a strong option for applications that need to perform well in real-time, like APIs and interactive websites.

Integration with AI and machine learning workloads

Serverless platforms are evolving to natively support AI/ML workloads, enabling scalable and cost-efficient deployment of intelligent features. This involves providing runtimes optimized for ML frameworks, support for GPU acceleration, and integration with managed data pipelines for training and inference. Serverless functions can be triggered by data events to perform real-time inference, such as image recognition or natural language processing, without provisioning dedicated infrastructure. Automated training pipelines can be orchestrated using serverless workflows, scaling resources dynamically based on data volume. This integration allows developers to embed AI capabilities directly into applications.

In general, the role of serverless computing in the future of app development can be characterized by its ability to help the building of faster, smarter, and more scalable applications.

Conclusion

The rise of serverless computing architecture has influenced digital transformation in app development, providing agile development of scalable, event-driven applications that are highly adaptable to fluctuating demands. This serverless solution model provides cost efficiency and operational agility, paving the way for a more streamlined development process. As the serverless model matures, integration with broader cloud security measures and enhancements in serverless frameworks will further solidify its position in the industry.