Serverless computing is a cloud execution model where the cloud provider dynamically manages the allocation and provisioning of servers. Despite the name, servers are still involved—developers simply do not need to manage, provision, or think about them. Code is deployed as individual functions that are triggered by events, and the infrastructure scales automatically based on demand, charging only for the actual compute time consumed.
The most common form of serverless is Functions as a Service (FaaS), offered by platforms like AWS Lambda, Azure Functions, and Google Cloud Functions. In this model, developers write small, single-purpose functions that execute in response to HTTP requests, database changes, file uploads, scheduled events, or messages from a queue. The cloud provider handles everything else—server provisioning, operating system patching, scaling, and high availability.
Serverless architectures offer compelling benefits including reduced operational overhead, automatic scaling from zero to thousands of concurrent executions, and a pay-per-use billing model that eliminates costs for idle resources. These advantages make serverless particularly attractive for event-driven workloads, microservices, APIs, data processing pipelines, and applications with unpredictable or intermittent traffic patterns.
However, serverless is not without trade-offs. Cold start latency can affect response times when functions have not been recently invoked. Vendor lock-in is a concern since serverless implementations vary between providers. Debugging and monitoring distributed serverless applications requires specialized tooling, and long-running processes may be more cost-effective on traditional infrastructure. Understanding these trade-offs is key to deciding when serverless is the right architectural choice.