Serverless computing is a cloud model where developers deploy code without managing the underlying servers or infrastructure. Instead, the cloud provider automatically handles resource allocation, scaling, and maintenance. Developers write functions or services that run in response to specific events—like HTTP requests, database updates, or file uploads—and the provider executes them on demand. For example, AWS Lambda, Azure Functions, and Google Cloud Functions are serverless platforms that let you upload code and define triggers, while the provider manages compute resources, security patches, and scaling.
A key aspect of serverless is its event-driven, pay-as-you-go structure. For instance, if you build an API that resizes images uploaded to cloud storage, a serverless function could trigger automatically when a new file arrives. The cloud provider spins up the necessary resources to process the request, scales horizontally if traffic spikes, and shuts down idle resources when done. You’re billed only for the milliseconds your code runs and the memory it uses. This contrasts with traditional cloud services like virtual machines, where you pay for reserved capacity even during idle periods. Serverless is particularly useful for sporadic workloads, such as batch processing, chatbots, or backend APIs with unpredictable traffic.
However, serverless has trade-offs. Functions typically have execution time limits (e.g., 15 minutes on AWS Lambda) and may experience "cold starts"—delays when initializing idle instances. It’s also harder to debug or monitor distributed functions compared to monolithic applications. While serverless reduces operational overhead, it can introduce vendor lock-in, as each platform uses unique triggers and tooling. Despite these limitations, the model simplifies deployment for tasks like data transformation, real-time notifications, or microservices, letting developers focus on code rather than infrastructure.