AWS Lambda Reviews

Usually, it is for small transactions. They're very atomic transactions. For example, we built a solution for an ad platform where an electronic ad runs for about 15 seconds a piece. Every time one of the ads runs, we have to record where did it run, how long did it run for, who was in the room, and how many people. There is a bunch of data around that. We typically send that transaction through an Amazon Kinesis pipe into a Lambda function, and then Lambda will take it and store it in S3 or target it to Redshift or put it in some kind of data store. That's one example of what we would use it for. That's a typical model for Lambda. 

It is serverless and scalable. It can scale infinitely. You don't have to worry about the size of the servers that you're pre-allocating. You don't have to build server scale-out models. Auto scale and other similar features are just inherent in Lambda. So, for atomic and fairly non-persistent transactional units of work, Lambda works very well.

My engineers work with it on a daily basis. I just don't have enough depth of knowledge about what kinds of edge cases they may have tried and found lacking. There may be some issues with some language support at one point or another because we couldn't get the underlying libraries in there. A lot of what we do is either in JavaScript, Python, or some of the non-compiled languages. I'm not sure if we've ever tried building a C# solution, for instance, in Lambda or a Java solution in Lambda. It doesn't mean those aren't its capabilities. I would rather refer to my engineers for where the boundaries are.

It has probably been five or six years since we've been migrating functionality from EC2 instances to Lambda.

Its stability is really good, and it is also highly available. The stability is inherent, but it also naturally gives you a high availability model because you don't have to have multiple EC2 instances running in, for instance, different regions. It is baked into the model. So, you can allow for inter-region Lambda functionality. It all becomes very highly available across Amazon's footprint.

It is endlessly scalable. In terms of its users, Lambda is typically baked into the middle of an application somewhere. Our ad platform solution is a fully-automated IoT solution. So, there are no people involved. The whole thing is automated from end to end. So, sometimes people don't even come into the equation.

We probably do or have dealt with their support, but that would be at the end engineer level. It is not something to which I would have much visibility. 

It is straightforward in my understanding. From the engineering perspective, I haven't gotten feedback that it is at all burdensome.

You're not paying for a server if you're not using it, which is another reason I like it. So, you're not paying if you're not using it. It scales, and you're charged based on usage. It all depends on the use case. Some can be extremely inexpensive if you have very low volume transaction rates. That way, you don't have to fire up and absorb the cost of the servers just sitting there waiting for a transaction to come through. You're only paying when you use it. So, depending upon the use model, Lambda could be highly efficient relative to an EC2 solution. You don't have to have things reallocated.

Understanding what your use model looks like is the key. All these cloud providers have so many different ways of implementing a solution that you really have to understand the near-term and long-term picture for that solution. What does it look like? When you're first building it, there might be a more expedited way to get it off the ground, but that may not scale properly, or your cost can get out of control. So, it is very important to align the right set of features within an AWS or Azure environment for not just getting the initial MVP built but also making sure that you're building it in a way that allows scaling and optimization of the cost model over time as the application scales. There's no one answer. The way you build the solution in the cloud is very dependent upon what the use case is.

From my perspective, not being at the engineering level, I would give it a nine out of ten. There is always room for improvement, but it has been a terrific advance over what was previously available just having to build everything in EC2.

I use AWS Lambda for event-driven computing and various other projects that benefit from its serverless architecture.

AWS Lambda supports event-driven computing, which is incredibly beneficial for our projects. Its scalability allows us to handle varying amounts of load efficiently, and it integrates smoothly with other AWS services to enhance our application workflows. 

Additionally, AWS Lambda is cost-effective, providing noticeable cost savings.

I would like to see improvements in AWS Lambda's stability and setup processes, as there were some complexities encountered initially.

I have been working with AWS Lambda for a substantial amount of time.

We have faced some stability issues with AWS Lambda, although it generally performs well.

AWS Lambda is scalable, and we have found its scalability beneficial for our projects.

I have not escalated any significant issues to customer support regarding AWS Lambda, but generally, AWS support is helpful.

Positive

I have experience with other compute services from different vendors, such as Azure.

The initial setup of AWS Lambda was somewhat complex and not entirely straightforward.

AWS Lambda has contributed to cost savings and performance improvements in our organization.

The pricing of AWS Lambda is reasonable, though there is always room for more flexibility.

I have evaluated solutions from other vendors, including Google and Microsoft Azure.

Overall, I would recommend AWS Lambda to others due to its capabilities. On a scale of one to ten, I would rate it quite high - and eight.

In our organization, we have a huge number of users using Lambda, approximately around 100. We are using Lambda based on several considerations like costs, and scalability and it provides us with high availability and scalability in our processes.

Lambda being serverless is a great feature that is appropriate for our use cases.

Memory limitation is one of the weaknesses of AWS Lambda and as a result, we have to use several Lambda, instead of just one.

Recently, I met with an Amazon employee, who is responsible for Lambda as a product. It appears Amazon has some plans with Lambda, so I don’t have to add something to the additional features.

I have been using AWS Lambda for a couple of years.

I would rate the stability eight out of ten because of certain bugs in the solution.

 I would rate the scalability eight out of ten because we have certain limitations in terms of memory and size. I see it as a gradual shift of mindset rather than limitation, where AWS Lambda is trying to incorporate everything according to the industry standards, which is everything is domain-driven design and suppression of microservices. Some people even have issues with the User Interface (UI).

We have a special collaboration with AWS, so we have regular meetings with AWS and Amazon, to report issues and they further report it to the concerned team. We have meetings, face-to-face and evaluation meetings with them and their solution engineers.

Positive

We previously used Azure and Volvo

The initial setup is straightforward. 

I implemented the solution  by myself because I was once a tech lead. 

I think the price is okay. However, if they add more functionality, they can have better prices. In fact, they should have better and more flexible packages for clients who have greater consumption of Lambda.    

I would rate six out of ten overall for AWS Lambda and all the Lambda solutions,. Because we don’t have the solution for each of our use cases. It is not available in various aspects and sometimes, it is not suitable.

As a platform team, we had to enable a light-weight ingestion platform ensuring the aspects of governance were baked into the platform and the business teams could accelerate their cloud adoption and only develop the business logic.

AWS Lambda improved our productivity and also enabled the business teams to build their ingestion systems at ease.

AWS Lambda's best feature is the lightweight framework that allowed us to bring down a lot of boilerplate code as part of platform capabilities and give the developers an opportunity to only write business-specific logic.

There were some timeout issues with AWS Lambda as the options provided didn't suit our business cases. In the next release, AWS Lambda should include integration with an IoT.

I've been using AWS Lambda for about a year.

AWS Lambda scales really well.

AWS Lambda is cost-effective, with a minimal maintenance cost.

I would rate AWS Lambda eight out of ten.

My primary use cases for AWS Lambda is collecting data from other systems through API, pre-calculations, and ETL.

The feature I found most valuable about Lambda is the fact that it's serverless.

I would like the layers to have a bigger volume. I would like to be able to add more. I don't want to be limited by the layer. 

In addition, writing temporary data to Lambda is not straightforward. Sometimes I use the S3 for that. I found it a little bit challenging. 

I have been using AWS Lambda for a couple of years.

My impression is that it is a stable solution.

I would rate the scalability of this solution a 10, on a scale of one to 10, with one being not scalable and 10 being very scalable.

I have never used Amazon's tech support.

I would say the initial setup was straightforward.

We required two technical staff to do the deployment and maintenance.

We have about 15 to 20 users of Lambda in our company and plan to expand.

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.

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.

We have some services deployed that we need to consume in Lambda because it's a faster and better implementation. We have a model that microservice in the product, which we implement in AWS. For that reason, we use Lambda.

The solution has improved our organization by providing us with better communication with the different services, dependencies, and implementations our clients require. The solution is faster and the consumption is really good.

There are some restrictions. For example, we have some services that if you have more than 60 seconds in consumption of this service, we don't use it, because they have that restriction with the gateway. Those services we implement with Fargate and with Docker.

The most valuable feature is that there is no need to implement it in a server because it is a service.

The 60 seconds limitation with the consumption of the service is really restrictive for a service and the solution can be improved by eliminating that.

I have been using the solution for one year.

The solution is stable.

The solution is scalable.

The initial setup is easy but you do need some experience.

The implementation was completed in-house.

The return on investment is good because it is a Cloud-based solution that doesn't require maintenance or a server on-premises saving you money.

I give the solution a seven out of ten.

One person with AWS knowledge is sufficient for deploying and maintaining the solution.

We currently have around 160 people using the solution.

All of the products we implement are in AWS so we do have plans to increase the usage of Lambda, Fargate, and all of the services in AWS to help speed up the implementations.

For anyone thinking about using the solution, you must make sure you have some experience in AWS or another cloud, maybe Azure, and have some experience with software development. If you don't have software development skills, maybe you have experience in deploying services on IIS or something like that.