AWS Lambda Reviews

There a variety of use cases for this solution but it is mostly used for data processing type transformations from one source to a target. In instances when Lambda works well, we are planning to increase usage. 

Deployment and maintenance of Lambda requires two people but this may increase depending on the size of the deployment.

Lambda has improved our organization by making it possible to transform data.

Lambda has limitations on the amount of memory you can use and is not a good solution for long running processes. If the amount of data is reasonably small, it will run and transform successfully. If you have tons of data coming in, it stops working and you have to restart it.

The orchestration and coordination between different Lambdas could also be improved.

I have used this solution or a couple of years.

This is a fairly stable solution. 

This solution has some limitations on scalability due to the problem with coordination between the different Lambdas. Is it not very straightforward and you have to do a lot of testing.

The initial setup was fairly straightforward.

I would advise others to use it in those circumstances where it works well and to be aware of the problems with coordination and orchestration. Make sure that it doesn't have to process tons of data.

I would rate this solution a seven out of ten.

Our company uses the solution as a function engine to deploy triggered events for customers. We have a team of twelve developers and our deployments have 1,000 to 2,000 customers during peak times. 

The solution runs on the latest cloud technology so it is easy to deploy cloud-native projects. 

The solution is very useful for managing triggered events. 

It can be a bit difficult to switch between accounts when creating services for customers. 

I have been using the solution for one year. 

The solution is cloud-based so of course it is stable as long as the cloud is available. 

The solution is scalable. 

We have not needed technical support. We utilize available documents that contain content for solving any issues. 

The solution is serverless computing so there is no setup. You just launch the service, write your code, and run it. 

The solution is part of the AWS subscription model that is paid annually. There is no charge for monthly usage. 

There are many calculation criteria for AWS services or licenses. It is difficult to say whether the solution reasonably priced or expensive. Each company weighs price against the benefits and efficiencies provided by the solution. 

AWS is the pioneer of cloud computing and they lead the industry in innovations. Other providers follow in AWS' footsteps to implement similar features and service levels. 

AWS has differentiated the use cases for serverless computing. Lambda is used for basic capabilities like triggered events and Fargate is used for extended capabilities. 

I started using Lambda for a specific use case and I continue to use Lambda and Fargate for use cases as recommended by AWS. 

I definitely recommend the solution and rate it a ten out of ten. 

AWS Lambda is mainly used for automation. To simplify, in AWS Lambda, I define a function, and I can invoke that function or feature whenever I want and on schedule.

For example, if I have data to collect daily from different services, I schedule the AWS Lambda function to do that, and it does the job. That's one of the use cases of the tool.

Another use case for AWS Lambda is when you have multiple servers running and need to shut down the servers at night. You can configure an AWS Lambda function that would shut down the servers every night on schedule. You can also use it as a backend and invoke it through API, so you deploy your backend from an AWS Lambda function, then link it with the API gateway, then you can invoke your function through the API.

What I like best about AWS Lambda is that it's feature-rich, and I appreciate that. I also like that it's stable and supports many languages.

What could be improved in AWS Lambda is a tricky question because I base the area for improvement on a specific matrix, for example, latency, so I'm still determining if I can be the judge on that. However, room for improvement could be when you're using AWS Lambda as a backend, it can be challenging to use it for monitoring. Monitoring is critical in development, and I don't have much expertise in the area, but you can use other services such as Xray. I found that monitoring on AWS Lambda is a challenge. The tool needs better monitoring.

Another area for improvement in AWS Lambda is the cold start, where it takes some time to invoke a function the first time, but after that, invoking it becomes swift. Still, there's room for improvement in that AWS Lambda process.

In the next release of AWS Lambda, I'd like AWS to improve monitoring so that I can monitor codes better.

I've been using AWS Lambda for one year and a half.

AWS Lambda is a stable product.

I have yet to contact the AWS Lambda technical support team because I haven't experienced any difficulty with the tool.

One of the reasons my company went with AWS Lambda is that it's serverless, so you don't need to manage any server because AWS will handle it. This means that a lot of responsibilities will be taken care of by AWS, which helps you avoid many problems.

Another reason for choosing AWS Lambda is its stability. Latency is also not a problem on AWS Lambda, allowing you to use different services in your AWS account securely. For example, when you want to reach the AWS billing service, you can do it within the environment of your AWS account without an external tool, which makes it very secure. AWS Lambda also helps you reach other AWS accounts securely because it depends on roles rather than search keys or passwords.

The initial setup for AWS Lambda is straightforward. It's effortless to start using AWS Lambda.

Price-wise, AWS Lambda is very cheap. It's not free, but it's not that expensive.

I'm using the latest version of AWS Lambda because when a framework or tool is deployed on a public cloud, it's always the newest version, and you don't get to choose the version.

I'd tell first-time users of AWS Lambda, or anyone looking into using the tool, that you can deploy every application within AWS Lambda, and it's very easy to set up and deploy. You can also use it with any application regardless of language because AWS Lambda supports many languages such as Go, Bison, Node, GS, and .NET. The tool is very code-agnostic, and not coded with programming languages, so you can still use Docker images to start your AWS Lambda function, for example.

My rating for AWS Lambda is nine out of ten because of how hard AWS worked on having good latency, security, and an intuitive start. The tool supports different languages and is stable, so I gave it a high rating.

I'm a customer of AWS Lambda.

AWS Lambda's most valuable feature is serverless architecture.

There are sometimes issues following an update.

I've been using AWS Lambda for about five years. 

AWS Lambda is stable.

AWS Lambda is scalable.

The initial setup was straightforward and took only a few minutes.

AWS Lambda's cloud version isn't expensive, and I'd rate its pricing as five out of five.

I would rate AWS Lambda ten out of ten.

Lambda makes the administration of all our services related to Amazon really easy. 

The metrics and reporting for this solution could be improved. 

I have been using this solution since 2017. 

From a stability perspective, I would rate it a seven out of ten. There is some room for improvement when it comes to stability of the features. 

This is a scalable solution. I would rate it a nine out of ten in this regard. 

The initial setup is complex. 

I would recommend this solution to others. Overall, the solution has improved since we started using it in 2017. 

I would rate this solution a nine out of ten.

We use AWS Lambda for jobs that we just want to offload to a cloud function. We are an educational institution, and if I want to upload an exam I generated for all my students, then I just write the script on AWS Lambda. That is, we use it for independent jobs that we can offload on their own.

I like the pay-for-what-you-use feature. This is the main reason why we use AWS Lambda. I don't have to manage servers; I just have to configure Lambda and expose it to an API gateway.

AWS Lambda's cold start needs to be improved. It has to warm up first, and so, the response time is slower.

Another challenge I've noticed is that there is a limit to the environment variables such as the 4 KB limit. Although, the advice is to use parameters or other things to store the details when the limit has exceeded the data, this adds additional intensity to the application. If the size limits for environment variables can be revealed, it would be helpful. Even if we have to pay for it, at least we would know that we are not dealing with latency. So, I would like to see the size of the environment variables increased.

I've been using AWS Lambda for one year.

It's very stable.

AWS Lambda is scalable; I don't need to worry about it. At present, we have less than 10 people using this solution.

The initial setup is straightforward. It can be done on the console, and you can use the infrastructure as the code.

The deployment time can vary from 10 minutes to an hour depending on your needs and how comfortable you are with AWS Lambda.

AWS Lambda now supports multiple languages, but find out if the language you want to use to write your jobs is supported by it. If it is supported, then you are good to go.

Because AWS Lambda is scalable and does what it's meant to do, I would rate it at nine on a scale from one to ten.

AWS Lambda's best features are log analysis and event triggering and actioning.

AWS Lambda's GUI could be improved with a twist or tweak in its look and feel to make it more impressive.

AWS Lambda is reliable.

AWS's technical support is amazing - they can turn a case around in a single email.

The initial setup is straightforward as Lambda is a built-in feature of AWS services.

AWS Lambda is phenomenal with data, and I would rate it nine out of ten.

AWS Lambda is used to write developer codes in Python. It is a place we can run where we can run our codes.

AWS Lambda is interlinked with CloudWatch. When we have any errors we can directly go there and check the CloudWatch logs. Additionally, we can run it very fast and we can increase the RAM size and other components.

We don't have the inbuilt modules in AWS Lambda. If more modules were built into or integrated with AWS Lambda, that would help developers to code.

I have been using AWS Lambda for approximately two and a half years.

The solution is stable.

The scalability of AWS Lambda needs to improve.

We have more than 100 users using this solution in my organization.

I have not used the technical support from AWS Lambda.

I have not used other solutions prior to AWS Lambda.

AWS Lambda is a very inexpensive solution. They charge for the number of times we run it. If you run AWS Lambda for one time, they charge around 50 cents or 25 cents for the use. I don't know the exact price, but it's less than a dollar.

AWS Lambda is user-friendly, people can learn very fast. People should start using AWS Lambda instead of using their own servers. It is very cheap and is the best way where any student can learn.

I rate AWS Lambda an eight out of ten.