Choosing the Right Serverless Use Case

Serverless computing is a compelling cloud model promising scalability, reduced operational overhead, and cost efficiency. Yet, as discussed by industry experts Jeevan and Eric, serverless is not a silver bullet; it’s a mindset shift and a strategic architectural trade-off.

As Eric noted, “When you move to serverless, you’re changing the way you think.”

It represents an architectural inflection point, redefining how systems respond, scale, and evolve. It pushes enterprises to focus on behavior events, triggers, and reactions.

Choosing the right use case requires a thoughtful understanding of your workload, willingness to embrace event-driven asynchronous design, and a pragmatic evaluation of characteristics that suit or challenge serverless.

Understanding Workloads and Architectural Fit

Moving to serverless is not just about changing syntax or shifting infrastructure; it’s a fundamental change in how you think about architecture. Serverless is rooted in event-driven design, where applications react to discrete triggers rather than depend on fixed orchestration. This shift requires architectural intent, planning how events flow, how components communicate, and where state truly belongs.

“Architecture is a trade-off. You know what you’re trading off and what you’re paying for,” as said by Jeevan Dongre in our AntStackTV episode.

In this trade-off, serverless brings elasticity, speed, and reduced operational overhead, but it also demands thoughtful design, embracing asynchronous patterns, automating scale, and accepting limited control. The success of any serverless initiative depends on matching its strengths to the realities of the workload.

Selecting Use Cases: Smart Criteria

Serverless works best when it’s aligned with workload realities. The architecture thrives in unpredictable environments, as Eric observed, “If your load is spiky, that’s where Lambda shines because you don’t know what it’s going to look like.”

Here’s some key factors highlighted by Eric based on his experience.

Start small, lightweight, stateless functions that deliver visible value quickly. Proofs of concept validate architectural assumptions without heavy investment and demonstrate the speed advantage that serverless enables.

Avoid long-running or stateful workloads that stretch beyond execution limits. The 15-minute boundary for Lambda functions isn’t a technical limitation but an architectural signal; if a process runs longer, it likely needs a redesign or a different platform.

Serverless performs best when driven by events, API calls, data streams, scheduled tasks, or real-time triggers. These workloads align naturally with an asynchronous, decoupled model that scales on demand. Asynchronous should be the default posture; synchronous design should exist only when immediacy is essential.

Architectural planning is the differentiator. Techniques like event storming help visualize how events flow and where logic should reside, ensuring systems are built around behavior rather than infrastructure.

When Not to Use Serverless

As serverless experts, we can’t forget to remind that serverless isn’t suited for every workload.

Stateful or long-running workloads are better suited to containers or traditional compute. Serverless is optimized for short-lived, event-driven execution, not continuous processing.
Rapid prototypes should never move to production without review. Unchecked scaling of experimental code creates technical debt that undermines long-term agility.
Cold starts, latency variance, and platform dependency are operational realities. They don’t disqualify serverless but require design consideration and governance oversight.