AWS Lambda Reviews

We are a startup, and we are doing faster and cheaper storage for IT. We are going to offer our storage services in about two months, and we are starting with AWS. We do lossless compression using microservices. We do the compute in a lossless compression way similar to gzip, WinZip, or PKZIP, except that we are giving a discount to customers.

The product that we are developing is not yet in the market. We are doing alpha testing for select customers who are using AWS. The biggest advantage is that you get faster storage without doing a forklift upgrade, and you get 35% cheaper storage. So, you get 2X faster storage with a 35% to 50% lower monthly bill.

We use AWS Lambda to encode and decode data. I work on the encode and decode software. I am working with a cloud developer. He works on the Lambda deliverable and wraps my C code with his C++ wrappers. They get bundled together with no JS stuff.

The cool thing about AWS Lambda is that AWS does all the management. For compression, it is all about making the data small and then making it regular size again. We have an encode function and a decode function. AWS Lambda schedules each of those for us. It has a load balancer and all the fancy stuff, depending on the demand.

The most valuable part of AWS Lambda is that I only need to write the software. I need to write two functions, and my cloud developer turns them into two AWS Lambda instances. That's it.

One area of improvement is to include support for more programming languages. AWS Lambda does not support a lot of programming languages. You have to write the Lambda functions in a certain programming language. We are using C++. My developer knows a couple of other languages. Python is his favorite language, but Python is not supported in AWS Lambda.

I have been using AWS Lambda since it came out in 2016.

It is very stable.

Its scalability is great. We write an encode function, and my cloud developer turns that into an AWS Lambda instance. We then write a decode function, and he turns it into another AWS Lambda instance. We just hook it up and use an API gateway for doing the security check, encryption, and other things.

We did not require any support. Most of the stuff in the cloud is anyways self-serve.

The initial setup was straightforward.

They have a program for AWS customers called Activate. With the Activate program, you can get compute and storage credits. They gave us like $10,000 worth of credit over 18 months.

I would recommend using this solution. It was the first microservice. It doesn't have much overhead, and it does what it claims to do. 

I would rate AWS Lambda an eight out of ten. It is a good solution.

Our primary use case is for our financial institutions. We use it for many customers that we work with. We develop solutions for our customers and run them on AWS. We wanted to build the solution on the public cloud and out of all the public cloud providers, AWS is the best. It has a rich set of services. 

It's a serverless solution which is the best feature. It helps us because it offers free aspects. From the infrastructure perspective, it helps us manage costs. There is no overhead of estimating how much infrastructure we're going to need. We can focus on building the business functionality that we want to build. 

Lambda didn't use to support all the capabilities, if you wanted to initialize it before started serving the requests, it is not possible. In the most recent upgrades, they have added this feature. There is nothing missing. If there is something missing, AWS has other solutions that covers it. I look at Lambda as a piece of AWS, working in conjunction with other solutions.  

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

We don't have any issues with stability or scalability. We have offline and online users. There are thousands of online users. 

Their support is excellent. We have no complaints. It can be expensive, we bought the enterprise support. If you don't want to spend the money, you might run into issues but that isn't the case for us. 

Everything was set up in the public cloud. We didn't need to set up a lot of things. We didn't have to connect a lot of connectivities to external applications. If you are setting it up on hybrid solution, there is a lot of work that needs to go in. You need to understand what your organization's security standards are, how the different teams are going to access the infrastructure, etc. For hybrid, it will take a lot of time but for only the public cloud, it is quite easy. 

AWS is slightly more expensive than Azure. But from a maturity perspective, AWS is way more ahead than Azure. 

I would rate it a nine out of ten. It was missing features like initialization but they are available now in the latest release. 

It is good for smaller companies that don't have a lot of staff that will manage the infrastructure. It automatically scales based on the users and allocating the resources. From a cost perspective, it is quite cheap. It takes out a lot of overhead. If it's for a large company with a lot of experts, there are other good solutions. 

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.

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 enables server-less architecture for seamless orchestration. We use the solution for various orchestrations. This is very useful when you would need to perform orchestrations of the different applications together. Many organisations are using this solution for web and mobile applications at scale. 

The main features of this solution are the ability to integrate multiple AWS applications or external applications very quickly and organize all of them by leveraging server-less computing power of AWS. Additionally, it is easy to use and you can run various programming languages, such as Python, Go, and Java.

There is room for improvement in user-friendliness. When comparing this solution to others it is not as user-friendly.

We have been using this solution for over five years for various server-less computing on AWS. My current employer has pioneered on this technology and serving various global clients for years. 

This solution is a serverless architecture in which you do not have to manage any infrastructure which makes it very stable.

The solution is highly scalable. 

We have plans to increase usage because most of the workload and applications are using this solution.

The technical support is managed by AWS which is good.

Azure Logic App & Function App, Google Pub-Sub etc. 

Managed by AWS

The price of the solution is reasonable and it is a pay-per-use model. It is very good for cost optimization.

I would recommend this solution to others. If you are with AWS it is better to use the serverless architecture.

I would rate AWS Lambda a nine out of ten.

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.

We are using the solution for creating the functionality. We store files over S3 and trigger that functionality to deploy our application. We use the solution for serverless activity. Once we store the files inside our S3 bucket, we write specific scripts in Lambda for auto-configuration. Afterward, whenever we send any data inside the S3, it gets automatically picked and deployed in the particular server.

The solution's most valuable feature is its ability to work as a serverless application.

They should work on the solution's stability and pricing.

We have been using the solution for a year.

It is averagely stable.

It is a scalable solution. I rate its scalability as a five.

The initial setup took work. I have worked on the development activity of the solution. The cloud architect of the solution needs to learn Python programming, like coding activities, to update new scripts inside Lambda. It requires a team for the process. I came across some issues during the deployment process and was able to fix them within an hour. So, fixing the problems after raising a ticket while deploying the solution takes time. I rate the setup process as two or three for a medium-level expert. Further, I rate the process as a nine for an executive with knowledge of Python programming. If everything works well, it takes 45 minutes to deploy the solution.

We took help from an Integrator to implement the solution. We were a team of a business analyst, DevOps engineers, and one developer working on the setup. We looped in another person in case we faced any issues.

The solution's price is average, and I rate it as a five for pricing.

The solution doesn't require any maintenance if you are creating an instance on your own. In case of any issues with Lambda services, you can check with their AWS support team. Also, they have provided a support portal inside the dashboard. You can ask for their help. 

AWS Lambda is a good solution. If you are well versed with Linux, AWS will be more accessible. I rate the solution as an eight out of ten.

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.