Serverless Computing When and How to Use It

Serverless computing lets you run code in the cloud without managing servers. You write small functions that respond to events or requests, and you are billed only for the compute time you use. The platform handles provisioning, scaling, and failure recovery, so you can focus on the software you build and the experience you deliver.

This model fits many modern apps. It shines when traffic varies, when tasks are short and independent, or when you want to move quickly from idea to production. It can also reduce operations work and shorten the time to market. You pay for what you use, not for idle capacity, which helps with budgeting during uncertain periods.

When to use

  • Highly variable API and event workloads
  • Short, independent tasks such as resizing images, validating data, sending notifications
  • Microservices that can run as separate functions
  • MVPs and experiments with low upfront cost

When not to use

  • Long-running tasks or those that need a lot of memory beyond typical limits
  • Apps needing ultra-low latency or steady, warm performance
  • Complex transactions across services that require strong consistency
  • Scenarios with strict data residency or compliance needs where control matters

How to approach adoption

  • Start with a single function tied to a simple trigger
  • Design for statelessness and idempotency
  • Decouple steps with queues or events to improve reliability
  • Build observability: centralized logs, metrics, traces
  • Plan for cold starts and consider strategies to reduce latency

Example scenario

A photo-sharing app uses one function to generate thumbnails when a user uploads an image and another function to index the image for search. A separate API endpoint handles requests, delivering fast results while the heavy work runs in the background.

Starting small doesn’t mean staying small. As you learn traffic patterns, you can split work into more functions, add asynchronous queues, and layer in more services.

Considerations

  • Vendor lock-in can happen; aim for portability where possible
  • Keep data in durable storage you control, not inside the function
  • Estimate costs with traffic forecasts, and set budgets and alerts

Best practices

  • Keep functions small and focused
  • Use managed services to complement computation
  • Document limits, retries, and failure handling

Key Takeaways

  • Serverless is ideal for variable workloads and rapid iteration.
  • It’s less suitable for long-running or highly stateful tasks.
  • Start with a single, observable function and grow as you learn usage patterns.