Serverless Computing: When to Use It
Serverless computing lets you run small units of code without managing servers. The cloud provider handles hardware, runtime, and reliability. You pay only for compute time, not for idle capacity. This can speed up delivery and simplify ops, but it also changes how you design apps. The trick is to match your workload to the model, not to assume it will solve every problem.
When to use it
- Event-driven tasks with irregular traffic that can spike and shrink.
- Stateless APIs and microservices that need to scale quickly without manual tuning.
- Prototyping and MVPs with tight timelines and limited maintenance.
- Background jobs and automation that run intermittently.
- Applications that must scale automatically while keeping admin work low.
- Lightweight data processing pipelines triggered by events.
When not to use it
- Long-running tasks that take minutes or hours to finish, or that keep a function busy.
- Very steady, predictable workloads with high baseline utilization.
- Apps that require ultra-low latency and minimal warm-up delay.
- Complex, stateful workflows needing fast cross-task memory or shared state.
- Scenarios with strict data locality, governance rules, or tight regulatory needs.
- Projects where vendor lock-in is a concern.
Getting started
- Begin with a small, single function that does one clear job.
- Store state outside the function (databases, queues, object stores).
- Compare total cost against traditional servers, including maintenance time.
- Add monitoring and tracing to track latency, errors, and cold starts.
- Consider a hybrid approach: core parts serverless, keep heavy or specialized work in containers when appropriate.
Serverless is a powerful tool for teams facing variable demand and fast iteration. Use it when workloads fit the model, and pair it with other patterns when needed.
Key Takeaways
- Serverless shines for variable, event-driven workloads and fast delivery.
- It’s less suited for long-running, latency-sensitive, or highly stateful tasks.
- Start small, monitor closely, and blend with other architectures as needed.