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

We have Implemented our authentication module for one of our clients along with AWS Cognito. This module caters to about half a million users and works fine without any lagging. Since deployment, monitoring is quite easy too, this saves and a lot of time and cost as well.

AWS Lambda is a platform where you can implement REST services, Data Processing and many other things easily. What I love the most is the integration with 'Serverless' platform which support managing lambda service implementation, debugging, deployment & monitoring quite easily.

So far I haven't come across anything that made me concern about working with AWS Lambda.

Love it! Use it every day! Highly recommended!

-Works every time without any issues -Easy code deployment with Python -Scales very effectively -Very cost-effective for testing POCs -Medium cost for production-level workloads -The architecture videos are great and the tutorials are good -Great integration with Step Functions

-Costs can get out of control if things start to fail and retry policies are repeat - Cost estimate is difficult while starting to write the code in Lambda vs servers

It allows the development of very heterogeneous applications thanks to its strong flexibility. Another strong point is the price which is calculated only for the CPU time that is used.

It is so flexible, but just as difficult to debug if something is working correctly and you cannot establish a base cost given its way of calculating costs.

Fantastic!

We have decided to use AWS Lambda to move some of the workload out of the servers that were not used often to process some of the workloads. I have never worked with Serverless approaches before, so AWS Lambda was my introduction to serverless world. AWS Lambda provides API in several programming languages, so it was very easy to jump straight into implementation and write some functions. It turned out to save a lot of money, because we could stop the server that was used sparsely. AWS Lambda has consumption based model so it was very cheap and superb effective.

I did not find any cons. Obviously it is not possible to write big services with AWS Lambda but some of the workload can be easily moved to Lambda to save on costs and maintenance.

We are very happy with the AWS Lambda service and it's costing criteria for only charging pay per use.

By using AWS Lambda we make the use of all resources that we are using, that means they are very cost effective. We only pay for the service we use and they are very cheap compared to other compute services like AWS EC2.

Sometimes it's difficult to create the Lambda function with the new AWS services and we are lacking with the expertise.

If you're building JAM stack (javascript, apis, markdown) applications, you're eventually going to need to create your own API. Lambdas are an obvious choice when developing them. They act as the middle piece that holds your logic between an API Gateway and a multitude of storage/databases/other dynamic services.

It's hard to pick one feature of Lambdas that are the best since they really only have one feature: they are event driven functions that can accomplish pretty much any task that you as a developer are capable of writing. As long as you have a known input and an expected output, Lambdas allow you to do the necessary work to get you there. This setup works particularly well for creating custom APIs.

Our team has yet to find a really satisfying developer workflow. It's easy to iterate within the AWS console, but that means your code is not under version control. Once you go the version control route, you have to deal with the (self-imposed) complexity of your deployment processes. In short there feels like there's a sudden steep learning curve around best practices and code reuse, but this is a problem of many AWS services in general.

Absolutely awesome, containers and functions are also integrated making it even more powerful.

Cost-effectiveness and ease of integration with Github pipelines and versioning make it so easy for us to deploy compute.

Not a real down-side, if run time can be increased to 30 minutes it will cover all of our payloads

We use AWS Lambda every minute, maybe even every second. There are multiple Lambdas running in our tech stack and architecture, connecting one service to another. Scalability and reliability are perfect.

Even though it's serverless, you still are required to update software for example when support for an older Node version is dropped. So you still have maintenance.

The overall experience with AWS Lambda has been good till now, the features that I like about i.e. The self reliant, and self scalable and money saving and easy to maintain. If I need trigger some jobs on event based, this is the most helpful thing and otherwise I will have to use the EC2 instance which is resource consuming and are good chances of being run idle.

The feature to self reliant is the best feature that I liked about the AWS Lambda. Currently we are using this for triggers to handle the events. It is working best as per the requirement of our business.

The deploying part of AWS Lambda is somewhat cumbersome and the testing it on dev server is something complex and it can be busy by just writing to the command and not using some JSON.

1. Compression on the run. 2. Scalable and still Cost Efficient 3. Platform as a Service, no need to configure underlying operation system 4. Multiple event triggering

1. Time limit on function execution. 2. Does not support editing for a file larger than 5 MB. Folder 3. No control over the environment.

Very low cost to execute functions and they have largely solved the cold start issues

Lambda is fantastic, cannot fault it. Keeps applications running at low cost.

Easy to set up, test, and deploy but need to be managed correctly to avoid security riks.

We have easily deployed AWS Lambda for various use cases and through different deployment options. It integrates well with AWS services allowing you to keep your coding and DevOps guidelines even when deploying serverless apps. The Pay-as-you-go pricing model makes it ideal for small projects or relatively small tasks that you want to execute after pre-defined events or API calls.

Since Lambda integrates well with AWS resources, it can be a source of security vulnerabilities in the case of an incorrect Identity and Access Management (IAM) setup. This issue is not a weakness of Lambda, but Engineers need to be cautious when setting up IAM Users and Roles to avoid this risk.

I can create a simple lambda function in minutes. The deployment process is pretty easy to automate.

For python applications, the dependencies management is a bit hard.

Overall experience using AWS lambda is good, It is way cheaper than complete AWS EC2 Instance but it all design as per the need.

One of the best thing about AWS Lambda is it support multiple triggers from queue in real time like HTTP request. It provides easy and scalable solution.

The feature which can be improve or add in AWS lambda is it takes too much time consumption for creating the container and which should be reduce.

Overall experience is rich with the instance response to custome code integrated to back end.

Most liked about this software is it's API integration with connectivity to database to store the important data.

Lease like is built in fault talaurance feature.

Super easy to integrate across Amazon / AWS suite of services and other integration points. Best in class documentation

Some limitations from initial platform deployment but AWS is adding more features every week

Serverless architecture, good for making API calls from integrated environments. Pay for what you use.

The default timeout is 3 seconds one must be cautious while configuring them.

It is a great software in the era of cloud computing. Moreover, it gives the developer, the freedom to choose any language they are comfortable with to be used in the lambda functions.

Amazon web service's serverless compute platform is great for cloud services that don't require a full-time server active. It is a very cost-efficient solution for many backend use cases. You only pay when there are calls answered by the system, unlike a traditional elastic compute platform which will charge you for the time your server is active.

A cloud practitioner or a software engineer is required to set up and even monitor the platform. Which is expected from any cloud service. But a simpler platform to set up and use a lambda function will be something I will look forward to.

I am using Lambda to horizontally scale some large processing we have. It is very convenient and I am using it as an alternative to run massive parallel processing on large EC2 instances

There are a few limits (ie lambda doesn't let to have a process run more than 15 min) however in my case they didn't impact

AWS lamda is a sever less service used in mid range organizations

It's can integrate with any of the AWS services and helps in building pipelines. It's a service less platform helps users to run code with out managing servers.

Issues while importing libraries and to implement task that take longer to run.