Serverless architecture handles APIs by using event-driven, managed services to execute backend logic without requiring developers to manage servers. When an API request is made, it triggers a serverless function (like AWS Lambda or Azure Functions) through a gateway service (such as API Gateway or Azure API Management). The gateway routes the request to the appropriate function, which processes the input, interacts with databases or other services, and returns a response. This setup abstracts server management, scaling, and infrastructure provisioning, allowing developers to focus solely on writing the API logic.
Operationally, serverless APIs automatically scale based on demand. For example, if an API endpoint experiences a sudden spike in traffic, the cloud provider spins up additional instances of the function to handle concurrent requests, then scales down when traffic subsides. This pay-per-use model reduces costs for low-traffic APIs and avoids over-provisioning. However, cold starts—the delay when a function initializes after inactivity—can impact latency. Providers mitigate this with techniques like keeping functions “warm” or offering provisioned concurrency. Statelessness is another key aspect: serverless functions don’t retain data between invocations, so persistent storage (e.g., databases, object storage) must be handled externally.
Common use cases include RESTful APIs for web/mobile apps, webhooks, and microservices. For instance, a weather app’s API might use AWS Lambda to fetch data from DynamoDB when a user requests a forecast, with API Gateway handling routing and authentication. Serverless also simplifies deploying API versions and stages, enabling A/B testing or gradual rollouts. While vendor-specific configurations can lead to lock-in, tools like the Serverless Framework or AWS CDK help maintain portability. Monitoring is handled via integrated tools (e.g., CloudWatch, Application Insights), though developers must implement logging within functions for debugging. Overall, serverless APIs trade some granular control for ease of deployment, scalability, and cost efficiency.