What is serverless?

What is Serverless Computing?

The term "serverless" might sound confusing at first. Servers are indeed still part of the equation, and your code still runs on servers somewhere. The difference is that you no longer have to think about those servers. You don't provision them, configure them, patch them, or worry about whether they'll handle traffic spikes. The cloud provider takes care of all of that for you.

This shift represents an important change in how we build and run applications. Instead of maintaining infrastructure that runs continuously, you write functions that execute in response to specific triggers. This means that when nothing is happening, nothing is running, and you're not paying for anything.

This article dives into the world of serverless computing, explaining how it works, its benefits, and in which cases it would make sense to rely on it.

TL;DR

• Serverless computing runs your code only when needed, automatically scaling based on demand without managing servers
• You pay only for actual execution time (measured in milliseconds), not for idle server capacity
• Functions respond to events like HTTP requests, file uploads, or database changes
• Perfect for APIs, data processing, scheduled tasks, and applications with variable traffic
• Cold starts and execution time limits are the main trade-offs to consider
• Major providers include AWS Lambda, Google Cloud Functions, and Azure Functions

How Serverless Actually Works

Put in a few words, serverless computing is about running small pieces of code in response to events. It’s a more granular approach to computing, which means that instead of having an entire application running all the time waiting for requests, you have individual functions that “wake up” when they're needed.

Here's what happens behind the scenes: When an event occurs (e.g., someone uploads a file, a database record changes, or an API receives a request), the cloud provider sets up an environment to run your code. Your function runs, performs its task, and then the environment is taken down. It might sound like a rather complicated process, but the entire thing might actually just take a few hundred milliseconds.

If multiple events happen at the same time, the provider automatically runs multiple instances of your function in parallel. You don't need to configure load balancers or worry about scaling policies. The platform handles all of that automatically.

The Benefits of Serverless Computing

The benefits of serverless go beyond just not managing servers, even though it’s nice not to have to worry about them. One of the most significant advantages is the pricing model. Traditional servers cost money whether they're processing requests or sitting idle. With serverless, you pay only for the compute time you actually use.

This makes serverless incredibly cost-effective for applications with variable or unpredictable traffic. If your application serves five users one hour and five thousand the next, you're only paying for what you need. You don’t have to over-provision servers just in case traffic spikes.

Development speed is another major benefit. When they don’t have to worry about the infrastructure, developers can focus entirely on writing business logic. You can go from idea to deployed function in minutes rather than days.

The automatic scaling is also worth mentioning. Your application can handle one request or one million requests without any configuration changes. The platform scales up and down based on actual demand, which ensures users always get a responsive experience and don’t feel lag.

Understanding the Limitations of Serverless

While serverless has many benefits, it also has its downsides.

Cold starts are one of the most discussed limitations of serverless. When a function hasn't run recently, the provider needs time to initialize the execution environment. This might add a few hundred milliseconds (or more) to the first request. For some applications, this latency is acceptable. For others that rely on fast responses, it can be a deal breaker.

There are, however, strategies to minimize cold starts, like keeping functions "warm" with periodic pings or using provisioned concurrency features that some providers offer. It’s important to keep in mind, though, that these approaches add complexity and cost, reducing some of the simplicity that makes serverless attractive in the first place.

Execution time limits are another consideration. Most serverless platforms cap function execution at somewhere between five and fifteen minutes. This makes serverless unsuitable for long-running processes like video rendering or complex batch jobs that take hours to complete. For these scenarios, you'll need to look at other computing options or break the work into smaller chunks.

Finally, debugging serverless applications can be more challenging than debugging traditional applications. When your code runs across many distributed function invocations, tracing the flow of execution and understanding what went wrong becomes harder. Good logging and monitoring become essential rather than optional.

Real-World Applications

Serverless is a great choice in specific scenarios. API back ends are a natural fit because each endpoint can be its own function, scaling independently based on demand. If one endpoint receives heavy traffic while others are quiet, only that function scales up. This granular scaling can lead to significant cost savings compared to scaling an entire API server.

Data processing pipelines work exceptionally well with serverless, too. When files are uploaded, a function can automatically trigger to process them. Whether you're generating image thumbnails, transcoding videos, or transforming data formats, serverless handles the variable workload efficiently. During quiet periods, you're not paying for idle processing capacity.

Scheduled tasks are another excellent use case. Instead of maintaining a server just to run a backup job once per day, you can create a function that executes on schedule. The function only runs when needed, completing its task and shutting down until the next scheduled execution.

Real-time stream processing is also increasingly popular with serverless. As events flow in from IoT devices, application logs, or user activity streams, functions can process each event immediately. This enables real-time analytics and immediate responses to important events without maintaining an always-on processing infrastructure.

Building with a Serverless Framework

When you start building serverless applications, you'll likely encounter various serverless frameworks that make development easier. These frameworks help you define, test, and deploy your functions across different cloud providers.

A serverless framework typically provides a configuration file where you define your functions, their triggers, and any resources they need (like databases or storage buckets). The framework then handles the deployment process, creating all the necessary infrastructure and connections.

These frameworks also help with local development. You can test your functions on your own machine before deploying them, which speeds up the development cycle by a lot. Many frameworks support multiple cloud providers, allowing you to deploy where you want.

Working with AWS Serverless

When people talk about serverless computing, AWS Lambda often comes up first. Amazon Web Services pioneered the serverless model and remains one of the most popular platforms for serverless applications. Working with AWS serverless services means you get access to a mature ecosystem with extensive documentation and community support.

AWS Lambda integrates seamlessly with other AWS services. Your functions can respond to changes in S3 storage, process messages from SQS queues, handle API requests through API Gateway, or react to database updates in DynamoDB. This deep integration makes it straightforward to build complex applications using serverless as the glue between different services.

Setting up serverless with AWS is relatively straightforward. You can start with the AWS Console to create and test functions, then move to infrastructure as code tools as your application grows. The platform provides detailed metrics and logs, so it is easier to understand how your functions are performing and where problems might be occurring.

When to Choose Serverless

While there's no one-size-fits-all answer, choosing serverless is a good idea for the following cases.

• If you're building an application where traffic fluctuates significantly or comes in unpredictable bursts, serverless can save both money and operational headaches. The automatic scaling ensures users get a good experience regardless of load.
• Applications that can tolerate cold start latency are good candidates for serverless. If you're building background processing systems, scheduled jobs, or APIs where a few hundred milliseconds of extra latency on the first request doesn't matter, serverless might be ideal.
• Teams that want to minimize operational overhead often choose serverless. If you're a small team or startup that needs to move quickly, not worrying about servers means you can focus on building features that matter to users. However, if your application needs consistently low latency, runs long processes, or has a steady, predictable load, traditional servers might actually be more cost-effective for you.

Getting Started

Starting with serverless doesn't mean you have to rebuild your entire application. Many teams begin by moving specific features or services to serverless while keeping their main application on traditional infrastructure. This hybrid approach lets you gain experience with serverless while minimizing risk.

A common starting point is moving webhook handlers or scheduled tasks to serverless functions. These are often isolated pieces of functionality that don't require deep integration with your existing systems. As you get comfortable with the serverless model, you can gradually move more workloads.

The key is to start small, learn the patterns, and expand based on what works for your specific situation. Serverless computing represents a powerful tool in your development toolkit, but like any tool, it's most effective when used for the right job.

Learn all about serverless computing and more with our *comprehensive guide on tools and integrations for modern web development.*