How does AWS Lambda achieve scalability?

AWS Lambda achieves scalability primarily through its ability to automatically run code in response to triggers. This serverless architecture allows Lambda to scale seamlessly in response to incoming events. When an event occurs, such as a change in data in an S3 bucket, a new HTTP request through API Gateway, or a scheduled task from CloudWatch, AWS Lambda automatically provisions the necessary compute resources to execute the code.

As the number of events increases, Lambda can scale out to run multiple instances of the function concurrently, handling thousands of requests simultaneously without the need for manual intervention. This dynamic scaling capability enables applications to respond efficiently to fluctuating demands without pre-provisioning or managing servers.

Moreover, because it is event-driven, Lambda functions can remain idle and only consume resources when an event triggers them, which contributes to cost efficiency and further reinforces its scalability. Other methods, such as requiring manual scaling interventions or utilizing virtual machines, do not align with the serverless nature of AWS Lambda and can introduce delays or complexities not found in Lambda's architecture.