The Evolution of Functions as a Service (FaaS) and Its Impact on Software Engineering

Functions Past, Present, and Future!

The Evolution of Functions as a Service (FaaS) and Its Impact on Software Engineering
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The software development field has experienced several paradigm shifts throughout its history, each bringing a new set of values and transforming the industry significantly. One of these transformative technologies, Functions as a Service (FaaS), has emerged as a crucial component in the modern application development arena. Let's explore the history of FaaS, its value proposition to contemporary software engineering practices, and what the future holds.

The Emergence of FaaS: Tracing the Origins

FaaS is a category of cloud computing services that provides a platform allowing customers to develop, run, and manage application functionalities without the complexity of building and maintaining the infrastructure typically associated with developing and launching an application. It is the logical conclusion of the evolution that started with Infrastructure as a Service (IaaS), progressed through Platform as a Service (PaaS), and led to what we now refer to as serverless computing.

The origins of FaaS trace back to the launch of Amazon's Lambda in 2014 at the AWS re:Invent conference. Lambda was initially introduced as a compute service to run code in response to AWS internal events such as changes to objects in S3 buckets, updates to DynamoDB tables, or custom events from mobile applications, websites, or other AWS services. This offered developers an entirely new way to execute and manage their applications: they could simply deploy discrete functions, and AWS would handle the rest, including the necessary resources, scaling, and even billing.

Google followed suit in 2016, launching Google Cloud Functions, and Microsoft unveiled Azure Functions later that same year. These FaaS platforms expanded the initial concept introduced by AWS Lambda, allowing developers to execute code in response to HTTP requests and a wider range of event triggers.

The Value Proposition of FaaS

Scaling and Cost Efficiency

The primary value proposition of FaaS is the ability to scale up and down automatically, depending on the demand for the function. Traditional servers require manual scaling, which could lead to over-provisioning (paying for unused capacity) or under-provisioning (not having enough capacity to handle the demand).

FaaS platforms, on the other hand, are designed to respond to real-time changes in demand. This auto-scaling capability is cost-effective as you only pay for what you use, and it enables the function to accommodate a virtually limitless number of requests.

Improved Developer Productivity

FaaS dramatically boosts developer productivity by abstracting away the server management aspects. This allows developers to focus on the business logic and function code instead of worrying about servers, capacity planning, and system maintenance. Moreover, since a typical FaaS application is composed of small, discrete, and modular functions, it promotes code reuse, making the development process even more efficient.

Event-Driven and Real-Time Processing

The event-driven architecture inherent to FaaS platforms is excellent for handling real-time file processing or data streaming. As soon as the event occurs (such as a file upload), the corresponding function is triggered to process it. This real-time processing ability opens new avenues for responsive and dynamic applications that weren't feasible or were challenging with traditional architectures.

Integration and Interoperability

Many FaaS offerings integrate seamlessly with other services provided by the same cloud vendor, facilitating data sharing, state management, and event communication. Furthermore, being HTTP-based, they can interface with any web-accessible service, promoting interoperability.

FaaS in Modern Software Engineering

In the context of modern software engineering, FaaS is an enabler for microservices and event-driven architectures. With FaaS, each function can be a separate microservice, simplifying the development process and making applications easier to understand, develop, and test.

The rise of FaaS also fuels the growth of the DevOps movement. The serverless nature of FaaS reduces the operations overhead, aligning with the DevOps principles of breaking down the barriers between development and operations.

Furthermore, FaaS plays a pivotal role in data processing and analytics, where functions can be triggered by events to process data and store it or pass it on for further processing. This makes FaaS a powerful tool for building real-time analytics applications and data-driven systems.

Kubernetes and FaaS: A Powerful Alliance

Kubernetes, an open-source system for automating deployment, scaling, and management of containerized applications, has become the de facto standard for orchestrating containers. When combined with FaaS, Kubernetes provides an extensible platform for building serverless applications. This allows developers to leverage the benefits of serverless architectures while maintaining the flexibility and control provided by Kubernetes.

Several FaaS solutions have been built on top of Kubernetes, capitalizing on its capabilities to provide a serverless environment within a Kubernetes cluster. Some of the notable Kubernetes-native FaaS solutions include:


Kubeless, a Kubernetes-native serverless framework, enables developers to deploy small bits of code (functions) without worrying about the underlying infrastructure. It leverages Kubernetes resources to provide auto-scaling, API routing, monitoring, troubleshooting, and more. With Kubeless, functions are treated as first-class citizens in the Kubernetes ecosystem and can be managed and scaled just like any other Kubernetes resource.


OpenFaaS (Functions as a Service) is an open-source serverless framework for Kubernetes which enables developers to run serverless functions anywhere Kubernetes runs. OpenFaaS makes it easy for developers to deploy event-driven functions and microservices to Kubernetes without repetitive, boiler-plate coding. It provides a unified user experience through its UI and CLI, along with a strong focus on developer productivity, ease of use, and operator-friendly tooling.


Knative is a Kubernetes-based platform that provides a set of middleware components essential for building modern, container-based, cloud-native applications. Knative Serving, one of its core components, offers a FaaS-like developer experience by providing on-demand scaling of applications, routing and network programming, and deployment features like rollouts and rollbacks.

Each of these FaaS solutions brings unique strengths, and the choice between them depends on specific project requirements and the nature of the applications being developed. Regardless of the choice, combining FaaS with Kubernetes offers an intriguing proposition: it provides the benefits of serverless architecture while preserving the powerful features of container orchestration, resulting in a potent solution for modern cloud-native application development.

As the adoption of FaaS continues to rise, new trends and trajectories are beginning to emerge, indicating the future direction of this technology.

Enhanced Developer Experience

A significant area of focus will likely be enhancing the developer experience. Today, although FaaS does abstract away much of the infrastructure management, there are still challenges that developers face. Cold starts, function composition, observability, and local testing are common areas of concern. In the future, we can expect to see solutions that address these challenges, simplifying the development process further and making FaaS even more developer-friendly.

Integration with Machine Learning (ML)

With the increasing prevalence of machine learning applications, we will likely see tighter integration of FaaS with ML platforms. FaaS is a perfect fit for many machine learning tasks, which are often event-driven and need to scale depending on the volume of data. As such, FaaS providers will likely enhance their capabilities to support machine learning workloads better, such as GPU support, integration with ML platforms, and tools to simplify the deployment of ML models.

Multi-Cloud and Hybrid FaaS Solutions

While FaaS offerings are typically tied to a specific cloud provider, the future is likely to see more multi-cloud and hybrid FaaS solutions. This trend is driven by businesses' desire to avoid vendor lock-in and to leverage the best offerings from each cloud provider. Such FaaS solutions will be capable of running across different cloud environments and even on-premises, providing businesses with greater flexibility and control.

Event-Driven Architecture (EDA)

As FaaS naturally fits into event-driven architecture, the adoption of FaaS will likely drive the adoption of EDA and vice versa. This will lead to an increase in the use of message brokers, event gateways, and other event-driven tools and technologies. Moreover, we can expect to see standardization around event formats and protocols, making it easier to build and integrate event-driven applications.

Edge Computing

With the rise of IoT devices and the need for low latency, there's a growing trend towards edge computing, where computations are performed closer to the source of data. FaaS is well-suited for edge computing because it can efficiently handle sporadic data and event spikes typical of many IoT applications. In the future, we could see more edge-based FaaS solutions, enabling real-time processing and decision-making at the edge.

The rapid evolution of FaaS indicates a bright future for this technology. As developers continue to unlock its potential and as the technology continues to mature, we can expect FaaS to play an increasingly vital role in shaping the future of software development and cloud computing.