I remember the first few times I heard tech colleagues toss around the term “serverless” at various conferences and meetups. At first, it sounded almost too good to be true—like somehow all your infrastructure simply vanished into thin air, never to be worried about again. But let’s set the record straight: “serverless” doesn’t mean there are no servers involved. It just means you won’t be the one babysitting them.
Instead, you’re handing off that responsibility to cloud providers who promise to handle all the fiddly details behind the scenes. If you’re involved in automating processes—whether you’re working inside a company’s IT department or you’re part of an automation consulting team—serverless can be a compelling route. But how do you decide if it’s really the best approach for your operation?
Let’s walk through what serverless actually entails, why it might be a fit for certain workflows, and which pitfalls you’ll want to watch for. Because, as any experienced tech person will tell you, outsourcing a job doesn’t mean erasing the complexities altogether; you just trade one set of challenges for another.
When you opt for a serverless architecture, it’s not that your code is running on fairy dust. Servers still exist, of course—but they’re hosted and managed by a cloud provider like AWS, Azure, or Google Cloud. You only get charged for the compute time you actually use. That means if your function or service is invoked a thousand times per hour, you pay for those thousand invocations.
If no one calls your function for half a day, you don’t end up paying for idle CPU cycles. From an automation standpoint, this model has a lot of appeal. Often, automated tasks run sporadically—maybe once an hour, or only when certain triggers happen (like a new user signing up, or a data file arriving in a storage bucket). With a conventional server, you’d still pay for it humming away 24/7. But with serverless, you pay per execution.
Have you ever spent an entire weekend fiddling with server patches, software updates, or custom configurations, just to keep a stable environment running? That’s where serverless architecture shines. It takes a good chunk of that maintenance burden off your plate. You don’t need to schedule nights to update your operating system or fuss over adjusting capacity when traffic spikes. Your cloud provider manages the underlying hardware, scaling, and patching, freeing up your internal team for more strategic work.
In a perfect world, that’s a huge plus. You get to focus on writing your business logic, orchestrating your automations, and polishing the user experience, rather than dealing with the nitty-gritty of server upkeep. Whether you’re an in-house developer or an external consultant, that can translate to faster project delivery times and a whole lot less stress.
Let’s say your company is embarking on a project to automate a bunch of repetitive tasks—could be anything from generating daily sales reports to syncing data across different platforms. Here’s where serverless can shine:
One scenario I’ve seen repeatedly in automation consulting projects is the use of quick, lightweight functions for data transformation. You can configure these functions to fetch data from an API, clean it, and deposit it into a database or another application. If you’ve tried building such a pipeline on a dedicated server, you know you end up paying for that server to be up and running around the clock—even if the process only runs once in a while. Serverless stands out for these smaller but vital tasks.
Now, let’s hit the brakes for a moment and talk trade-offs. First, going serverless involves relinquishing some control. For example, if you’re particular about which operating system version your code runs on, or you have specific hardware needs, serverless might be less flexible than an environment you curate yourself.
Another concern is latency—specifically, something often referred to as a “cold start.” When the cloud provider hasn’t run your function in a while, it can take a moment to spin up. This delay might be only a few hundred milliseconds or a second, but if you’re building a real-time application that demands immediate responses, that lag can be aggravating.
Also, pay-as-you-go can be a double-edged sword. If your application is prone to frequent bursts of activity round the clock, you might end up with a pretty hefty bill. And because you’re tying your architecture closely to your provider, partial vendor lock-in is almost inevitable. It’s not always a huge deal, but it’s worth being clear-eyed about before you jump in.
In the old days, all your code lived on one or more servers you could log into directly to debug or tweak. With serverless, your code is scattered across multiple functions, each potentially running in its own ephemeral container. That can make testing and debugging a bit more elaborate. Instead of checking logs on a single system, you might be rifling through multiple dashboards or using specialized logging services to trace an error.
From a consultant’s perspective, you’ll want to ensure that your client’s team is prepared for this new type of complexity. While you save on server administration overhead, you might add a bit of architectural puzzle-solving. Your developers need to coordinate and maintain a larger set of moving parts.
When I talk with companies considering a serverless approach, I often find myself asking a few key questions:
Working through these questions helps you decide if serverless is just a fancy new trend or actually a practical tool that addresses your specific needs.
If you decide to jump on the serverless train, here are a few tips that might save you some hair-pulling moments:
Ultimately, serverless is a bit like letting someone else handle the yard work. If you hate mowing the lawn and trimming the hedges, it’s a lifesaver. But if you’re someone who loves controlling every detail—including which fertilizer to use—it might feel limiting. In an automation consulting context, serverless can streamline a wide range of scenarios where you only need compute power in short bursts, or your workloads spike unpredictably.
It also allows teams to focus their energy on writing code for the business side of things—like integrating systems or building front-end features—rather than spinning up and maintaining infrastructure. However, not all companies are a perfect fit. If you’re deeply regulated or have highly specialized performance requirements, you may want a more traditional approach where you can configure every facet of your environment down to the kernel level.