A serverless application with hundreds of Lambda functions connects to an Amazon Aurora PostgreSQL database. Each Lambda scale-out creates a new DB connection, increasing resource usage. You need to reduce the number of database connections without reducing Lambda scalability. What should you implement?

Amazon RDS Proxy is the correct solution because it pools and shares database connections, allowing many Lambda invocations to reuse a smaller set of connections to the Aurora PostgreSQL database. This significantly reduces the number of open connections on the database, preventing resource exhaustion while maintaining Lambda's ability to scale rapidly. Enabling provisioned concurrency for Lambda functions ensures that a certain number of function instances are always warm, reducing cold start times, but it does not address the issue of excessive database connections created by each instance. Similarly, setting reserved concurrency limits the maximum concurrent executions for a Lambda function, which could hinder scalability and doesn't solve the connection management problem. Cluster cache management for Aurora PostgreSQL optimizes caching within the database cluster but does not directly manage or reduce the number of client connections.