AWS Lambda enabled us to quickly deploy and use functional code in a serverless manner, with the capability to rapidly scale out of the box.

AWS Lambda provides the capability to deploy and use serverless functions in a handful of programming languages, with many useful integrations with other AWS services. It enabled us to meet our performance benchmarks for large-scale applications right out of the box due to its capability for a high level of concurrency, and the integrations with AWS SQS and AWS API Gateway were particularly useful for our use cases.

The inherent limits set on the size of the function and the requirement of packaging dependencies along with function code has proven to be difficult at times, although these problems can be alleviated by using layers and Lambda Containers.

My overall experience with AWS Lambda is really very good. It saved me a lot of time since I do not need to write code, I can just use the interface to connect diifrent nodes.

The pricing of AWS Lambda is reasonable, and I like that I can activate and deactivate the service without deleting the project. Also, I like that I can use AWS Lambda in the AI project lifecycle to manage the pipeline and connect the model to up for deployment.

The documentations available for AWS Lambda on AWS are very limited and do not provide an implementation to an end-to-end project. Also, I do not like the limited control over the infrastructure as AWS Lambda does not allow me to control this point.

Lambdas have been great when used in case of lightweight computation, but needs improvement in heavy applications.

Lambdas support multiple languages and have clients for major AWS Tools like Opensearch, DynamoDB, SQS etc.

Lambda cold start is a major pain point. There have been multiple solutions given for this previously, but none of them have been satisfactory.

1. In my Enterprise, we use Lambda to keep in track of our Amazon RDS instance's password policy which needs to be changed once in every 30 days. 2. We set up lambda function using Python and utilize AWS Secrets Manager to store our password. 3. When the 30 days period occurs in the Secrets Manager, it automatically triggers our lambda function and changes our RDS password. 4. Since manual changing of passwords can be challenging and time consuming, automation through our Lambda function makes everything easy and cost-effective.

1. I like how I'm able to implement lambda functions with various languages of my choosing whether its Python, Java, Go, Powershell, Ruby or Node.js 2. I effectively save cost by using lambda for my deployments as I pay for the computation time during which my lambda function is running. 3. Since its serveless architecture, I don't have to worry about the underlying infrastructure or its maintenance. 4. It gives me event-based trigger which eases my deployments as it enables automated procedure based on my lambda functions. 5. It is helpful to perform various operations and resource administration activities like monitoring resource status, updating security patches, deploying applications, etc.

I didn't find anything to like as least abut AWS Lambda. One thing to mention is, the TTL for each lambda is 15mins so after that period so if its customizable means we will be able to do more deployments running a single lambda function.

We have been moving from tradicional EC2 servers into Lambda Serverless. They are soooo inexpensive, we payed hundreds and even thousands of dollars in tradicional servers, and with the same functionality in lambdas we pay $100 or $200 with more than 10,000 users. Also they are very easy to scale up for many users

It is a little difficult to understand the new serverless paradigm, but once you learn to design it, it is easy. In someways is difficult to maintain, because you end up with a lot of Lambdas