Serverless computing allows businesses to build agile and scalable applications while saving costs.
As it gains rapid traction, it is no wonder that it develops and expands, offering new features to expect in the near future.
This article will discuss serverless computing, why it is so popular, and what serverless computing trends we can expect soon.
What is serverless computing?
To better understand serverless computing trends, we must explain why serverless architecture is so great and why it hits the mainstream.
Let’s start with the basics.
Serverless computing is a model in which a server provider (AWS, Google Cloud, etc.) dynamically allocates the necessary resources to execute a piece of code. This model uses consumption-based pricing, as you only pay for the resources you use.
Serverless computing vs. conventional server-based computing
In traditional cloud services, developers must manually allocate and manage the resources needed for a web application.
Serverless computing requires zero manual provisioning. The system automatically scales resources based on the amount of code to be run.
So, serverless, in fact, is not server-less. It simply frees developers from server-related tasks.
For example, you don’t have to worry about expanding your server capabilities when deploying new features. Your server provider will automatically provide more resources when needed.
Serverless computing payment principle
Another difference is that with traditional server-based architecture, the server continuously runs regardless of use, and you have to pay standard fees. Serverless computing follows the “pay-as-you-go” principle. You only pay for the time consumed by your code.
So, basically, serverless computing is simpler and cheaper. But that’s not all. Thanks to its build, the serverless architecture allows businesses to scale seamlessly.
Serverless computing trends
Based on the popularity of serverless computing and its integration with other technologies, here are the following trends:
- Broader adoption of serverless computing
- Gaining more ground with ML and AI
- Extension to multi-cloud and hybrid environments
- Integration with edge computing
- Managing complex workloads
Let’s review these trends closely.
Trend #1. Broader adoption of serverless computing
Due to the benefits like scalability in serverless, cost reduction, and agility, the serverless architecture will definitely find new uses.
As of 2024, the serverless computing market has reached USD 24.51 billion and will hit the USD 27 billion mark this year. While it is also predicted to grow by 14% annually in the next five years, we expect wider areas of application and new use cases.
Traditionally, serverless computing was mainly used for stateless applications or applications that performed tasks without storing and maintaining their state in multiple instances. Now, developers of stateful applications pay attention to serverless computing to manage states within this architecture more effectively.
AWS and Microsoft Azure already have products that allow applications to maintain state while performing multiple functions.
Trend # 2. Gaining more ground with ML and AI
Serverless platforms help computing-hungry ML models run more efficiently, which is very important due to upcoming ML/AI challenges.
According to Deloitte’s 2025 report, the power-hungry ML and AI products will consume 4% of all electricity used by humanity in 2030. Another report issued by IBM points out that the cost of AI computing has climbed 89% between 2023 and 2025. Due to the high costs, some executives postponed or even canceled AI initiatives, especially in generative AI.
Serverless computing, with its on-demand operation and pay-as-you-go principle, has the potential to offer more power and cost-effective infrastructure for ML/AI computing.
It will be especially productive in running models that are dynamically triggered by users, such as voice recognition or image processing.
Trend # 3. Extension to multi-cloud and hybrid environments
New services, allowing users to transfer and run functions across different cloud and on-the-premise environments, are gaining popularity. We are now speaking about products like Knative and OpenFaaS.
This type of service will help businesses that run part of their operations on public clouds and keep sensitive data in-house.
Trend # 4. Integration with edge computing
Major cloud providers like AWS are already integrating their products for serverless and edge computing. In the case of Amazon, we are speaking about AWS Lambda@Edge and Azure IoT Edge.
Serverless computing allows certain applications to be run closer to the end user. It will help decrease latency in applications for IoT devices, gaming, and real-time analytics.
More robust and scalable infrastructure will be necessary due to the higher traffic loads expected in these domains in the next five years. For example, the number of IoT devices will double by 2030, reaching 40 billion, creating significant pressure on cloud infrastructure.
Trend # 5. Managing complex workloads
We may predict that serverless services will move from mere function execution to more resource-intensive tasks.
The integration with containerization technologies like AWS Fargate is a development to expect within this trend.
Serverless computing is expected to reduce or eliminate the limitations of more complex tasks, such as memory and concurrency limits and limitations on execution duration.
Why is serverless computing called Function-as-a-Service (FaaS)?
Serverless computing follows the Function-as-a-Service model, meaning that it executes code in the form of functions. They run separately, and you only pay for the time a function is computed.
Below, we’ll explain how this works and how it differs from legacy monolith coding.
Monolith coding
In traditional coding, every product was created as an interconnected code bundle, often called a monolith. The first versions of Amazon, Netflix, Siri, and Uber were examples of monolith coding.
Changing a feature required detangling the whole piece of code. That process was slow and long, so the products weren’t scalable.
Microservices
As monolithic products failed to keep pace with demand for new features, businesses needed a solution. So, they switched to microservices architecture, which laid the basis for modern-day serverless computing.
Microservices architecture is a type of product build in which all different features are programmed as separate pieces of code that are independently upgradable, maintainable, and deployable.
When Uber switched to microservice architecture, they built separate functions for passenger management, trip management, etc. At Amazon, they developed a single service for the Buy button on the product page, a separate service for the tax calculator, and so on.
These microservices are loosely connected, so a change in one of the features doesn’t affect others.
Serverless computing automatically allocates resources for each of the microservices or functions. So, a server runs when a microservice runs. And you pay for that time only.
As this development model brings a lot of advantages, the number of its offerings and applications expands.
Summing up
In serverless computing, the provider automatically allocates the necessary computing resources as some parts start running. After the function is executed, the server stops running. Therefore, users pay only for the time the tasks function. This principle caters to more agility and cost savings, while microservices architecture allows for better scalability and updatability.
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