Serverless architecture changes how teams build, deploy and pay for software. Instead of running servers around the clock, you write small functions, and the cloud provider runs them only when they are needed.

This model has clear benefits. You pay only for the compute time you actually use. You don’t wait for servers to start, balance the load or install updates. In short, you write a task, and the platform executes it.

However, serverless is not a silver bullet. Cold starts, provider lock-in and limited control over the runtime make it a poor fit for some projects. In this article, we explain where serverless architecture shines and where you should choose something else.

From Microservices to Serverless Architecture

Serverless technologies build on the ideas of microservices. Both approaches split an application into small, independent modules. Both react to events, and both hide the infrastructure from the end user.

The main difference lies in how the modules run. A microservice is usually a long-running process that answers requests. A serverless function, by contrast, starts on an event, does one job and stops. Functions are often connected through queues, so a bug in one function doesn’t bring down the whole application. As a result, errors are easier to isolate and fix.

Scaling is another difference. With microservices, your team configures autoscaling for each service separately. With serverless, the provider scales every function automatically.

Still, a set of microservices is not automatically serverless. If you simply move microservice code to functions, you can easily miss the real advantages of the model.

How to Build a Project with Serverless Architecture

Serverless works like a construction kit. You don’t need to build all the planned features at once. Instead, you can start with a few functions and add new ones as the product grows. Meanwhile, the platform handles scaling for you.

Serverless also makes it easy to extend an existing application. Almost every platform offers REST APIs and message queues. Therefore, your team can build add-ons without touching the core of the system.

What Serverless Shares with Cloud and Microservices

Serverless combines the strengths of cloud services and microservices.

From the cloud, it inherits the business model. You don’t buy hardware, rent a server room or hire a dedicated system administrator. Instead, you pay for what you use.

From microservices, it inherits simple code and fast delivery. Each function does one thing. Developers can change or add functions without risking the whole project, and the system can grow horizontally almost without limits.

The Benefits of Serverless Architecture

Here are the main reasons companies choose serverless:

  • Lower entry barrier. Learning serverless basics takes less time than learning full DevOps. Consequently, it is easier to find the right specialists.
  • Automatic scaling. You don’t need to estimate capacity in advance, because functions scale with incoming traffic.
  • No infrastructure to maintain. There is no Kubernetes cluster to configure and no containers to monitor.
  • Pay-as-you-go pricing. Idle time costs nothing, which suits irregular workloads very well.

That said, configuration needs care. A mistake in a function that calls itself can generate a huge bill overnight, as one team learned the hard way. For this reason, always set budgets, alerts and concurrency limits.

Serverless vs Containers

Serverless functions and containers both work well with microservices. Yet they solve different problems.

Serverless saves time for teams that don’t want to manage infrastructure. Containers, on the other hand, give you full control and independence from a specific provider. The right choice depends on your priorities. For example, the ShoutOUT platform moved from Docker to serverless to reduce costs and solve scaling issues.

In practice, many companies combine both. They run the core system in containers and use serverless functions for events, integrations and background jobs.

Where Serverless Architecture Doesn’t Fit

Cold Starts

Cold starts are the most common complaint. To save resources, the provider shuts down functions that haven’t run for a while. When a new request arrives, the platform has to start the function again. This adds a delay that ranges from milliseconds to several seconds.

Fortunately, there are several ways to reduce the delay:

  • keep the deployment package small, so the function loads faster;
  • use features such as provisioned concurrency to keep some instances warm;
  • choose a runtime with fast startup, or use options like AWS Lambda SnapStart for Java;
  • monitor latency with distributed tracing tools such as AWS X-Ray.

Language Support

Each platform supports its own set of languages. For instance, AWS Lambda runs Node.js, Python, Java, .NET and Ruby natively. Other languages work through custom runtimes or containers. Therefore, check language support before you start the project.

Provider Lock-In

Serverless applications depend heavily on the provider’s services: queues, storage, databases and triggers. Moving an existing application to another cloud is difficult and expensive. In some cases, it is easier to rewrite the product.

Fragmented Applications

Serverless takes the modular approach to the extreme. On the one hand, you can release updates quickly, and an error in one function doesn’t stop the whole system. On the other hand, porting a large monolith to serverless is usually too costly. That’s why serverless often complements a monolith instead of replacing it.

Limited Control

Finally, you get less control over compute resources. You can’t connect to a server over SSH or fine-tune the operating system. For workloads that need special hardware or long-running processes, containers or virtual machines are a better choice.

Serverless Architecture on Different Cloud Providers

Every major cloud offers serverless computing, but the platforms differ.

AWS Lambda

Amazon Web Services offers the most mature platform and the largest ecosystem of triggers and integrations. In addition, the AWS Free Tier lets you start experimenting at no cost.

Azure Functions

Microsoft Azure Functions integrate tightly with the rest of the Microsoft stack. They are a natural choice for companies that already use .NET, Microsoft 365 or Azure DevOps.

Google Cloud Run Functions

Google Cloud runs functions on the same infrastructure as Google’s own products. It also includes Firebase, which remains popular for mobile app back ends. Keep in mind that Google regularly retires older products, so plan for upgrades.

Edge Platforms

Platforms such as Cloudflare Workers run code close to the user. As a result, they are well suited for low-latency APIs, personalization and request routing.

Where Serverless Architecture Is Going

Modern development requires teams to release features quickly. Serverless helps here, because a new function can go live in minutes. It also makes it easy to add features to an existing monolith without changing its core.

Serverless also handles traffic peaks well. Functions scale automatically, and you can isolate them from the main system if something goes wrong. This makes serverless a good fit for marketing campaigns, ticket sales and other events with sudden spikes.

In the future, serverless will play a bigger role in the Internet of Things, AI workloads and mobile applications. In these areas, event-driven code works better than large monolithic systems.

Nevertheless, the shift will be gradual. Companies won’t abandon their existing architecture overnight. To use serverless well, a team needs more than a few new tools. It needs a new way of thinking about application design.

Need Help with Serverless?

At Yotec, we help companies decide where serverless makes sense and build reliable cloud applications. Learn more about our custom software development services or contact us to discuss your project.

Related reading: AWS Lambda quick overview and microservices and APIs.