How does serverless differ from traditional server-based models?

Serverless computing differs from traditional server-based models primarily in how infrastructure is managed, how applications scale, and how costs are structured. In traditional setups, developers provision and maintain servers (physical or virtual machines) to host applications. This includes tasks like configuring operating systems, applying security patches, and managing scalability through manual or automated scaling rules. For example, running a web app on an AWS EC2 instance requires setting up the server, installing dependencies, and handling load balancing. Serverless shifts this responsibility to the cloud provider: developers write code (often as single-purpose functions) that run in response to events, while the provider manages the underlying infrastructure. AWS Lambda, for instance, automatically provisions resources when a function is triggered (e.g., by an HTTP request) and shuts them down when idle, eliminating the need to manage servers directly.

Operationally, serverless reduces overhead but introduces trade-offs. Traditional models require continuous resource allocation—like paying for an EC2 instance 24/7, even during periods of low traffic. Serverless follows a pay-per-execution model, where costs are tied to the number of requests and compute time (e.g., paying $0.20 per million Lambda requests plus $0.00001667 per gigabyte-second of memory used). This can save costs for sporadic workloads but may become expensive at scale. Additionally, serverless platforms enforce limits, such as maximum execution durations (e.g., 15 minutes for Lambda), making them unsuitable for long-running tasks. Traditional servers offer more flexibility here, as developers control runtime environments and can host stateful applications (e.g., databases) that require persistent storage, which serverless typically avoids in favor of stateless, event-driven designs.

Use cases further highlight the differences. Serverless excels for event-driven workloads like processing file uploads, handling API requests, or running scheduled tasks (e.g., a function resizing images when uploaded to S3). Traditional servers are better suited for applications with steady traffic, low-latency requirements (avoiding serverless “cold starts”), or complex architectures needing full environment control (e.g., custom software with specific dependencies). For example, a high-traffic e-commerce site might use EC2 instances for its checkout system to maintain consistent performance, while using Lambda for ancillary tasks like sending order confirmation emails. Choosing between the two depends on workload patterns, cost considerations, and the need for infrastructure control versus operational simplicity.