AWS Lambda Reviews

I have used AWS Lambda for deploying our mobile application to integrate it with services like S3 bucket, Bedrock, or SageMaker. It serves to connect our local systems to call servers for mobile application integration.

AWS Lambda operates as a serverless architecture, providing quick responses to events without requiring server management. It supports multiple runtimes like Python, Java, .NET, and Node.js. Additionally, the Lambda timeout service allows processes to run for up to fifteen minutes, enhancing time efficiency. AWS Lambda is useful for integrating with CI/CD pipelines and real-time scenarios.

AWS should provide more trial support for new users and customers. AWS should also provide basic access to perform some actions using different tools on the AWS platform. This would help customers understand the types of integration possible and the various tasks they can perform using AWS services.

I have used AWS Lambda for around one year.

AWS Lambda is very stable, providing good response times compared to other cloud services and requiring a stable network connection.

AWS Lambda is highly scalable. I rate its scalability ten out of ten. It can handle large datasets, and its integration with other AWS services enhances its scalability.

I was previously manually creating models and deploying them to AWS S3 cloud buckets. This process was more time-consuming and took two to four days for large datasets.

The initial setup of AWS Lambda is easy. I would rate the initial setup process ten out of ten. It takes around one to two hours to learn and understand the use cases.

Only one or two people are necessary for the deployment and maintenance of AWS Lambda, as it involves data engineering and deployment only.

AWS Lambda pricing is moderate. You only pay for the services when they are used, unlike EC2 services. Sometimes AWS Lambda seems expensive, rating around five out of ten for pricing.

I have worked with Azure AI Studio and found Azure to have better performance for generative tasks compared to AWS.

AWS Lambda rates ten out of ten. For new users, gaining trial support and understanding the capabilities of AWS's integration are advised. I would give the overall solution a rating of 10 out of 10.

Public Cloud

Amazon Web Services (AWS)

I mostly use the solution for data dips or integrations with third-party applications.

The fact that it is serverless is really important. I can then deploy a microservices architecture. It is quite quick and nimble. The most useful feature for me is the API call, as well as the integration and its flexibility.

The runtime could be improved. There are certain use cases where I need a Lambda function to run longer. I don't know how many minutes it is, however, there's a threshold that I can't exceed. If that time was extendable, it would help in some areas, not all areas, yet some.

I have used the solution since Amazon Connect AWS launched, which I believe was in 2017.

The stability is high. I would rate it eight out of ten in terms of stability since nothing is perfect.

The scalability is ten out of ten. 

I have used their solutions for about ten years. They are pretty good at support.

Positive

The initial setup is fairly straightforward. If I am familiar and trained in an AWS environment, then yes. The AWS interface is not great, really. It can be improved, specifically the GUI, however, in general, if I am trained, I need to know how to use these tools. It is fairly straightforward.

I believe its pricing structure is good. On a scale of one to ten, its pricing structure deserves a nine, maybe.

Working with AWS and its solutions is definitely recommended. It is an integral piece of the puzzle, really. I'm basically a research site. I gather reviews of products, from AWS and also from all other vendors, so I can assure you there's no biased opinion here. You can actually find out about all other tools. I am really grateful that you benefited from our website and the IT community with your feedback about AWS Lambda. 

Overall, I would rate the product at nine out of ten.

Public Cloud

Amazon Web Services (AWS)

I use AWS to host our app space within my university, APU. My infrastructure is based on the AWS cloud, and most of my compute workloads are primarily based on Lambda. This setup facilitates all transactions that require engagement from multiple stakeholders, including staff, faculty, students, and administrative staff. I have in-house applications, both mobile and web-based, that engage with Lambda to provide the required scalability, especially during peak hours of university timings. Lambda is a highly utilized service for me.

The most important feature of Lambda for me is that I don't need to manage any resources. I don't have to set up an AWS stack or developer tools, as they are provided by AWS by default. This means that if I need to set up Lambda, it's a matter of minutes to deploy my code, which facilitates my CI/CD pipelines. Time is an important optimization for me, and Lambda is economical. Additionally, it is highly available and sustainable, unlike EC2, which is a single point of failure. Lambda saves me time in configuration and deployment and gives me confidence in its high availability and scalability. I have done my workload migration from an on-premises environment to the cloud, significantly reducing my capital expenditure. The automation improves my planning for product releases and hybrid connectivity. Also, content delivery has improved, and application latency has decreased.

I would say an increase in the current limits would be quite beneficial. Right now, the threshold is fifteen minutes. AWS has increased the limits from three to ten gigabytes, enhancing the productivity of different workloads. If this threshold is extended further, more workloads currently utilizing EC2 could potentially be executed using Lambda.

I have been working with Lambda since 2018.

Speaking of scalability, I would rate this aspect as close to nine out of ten. So far, I haven't had any issues with the usage of Lambda. Since AWS launched its region in Malaysia, the compute is happening right next to my location.

Support is of the highest quality, so I would give it a five out of five.

Positive

Before Lambda, I mainly used my on-premises environment.

The entire Lambda project took less than three months. I'm not the team lead for that setup, so the exact timeline isn't possible for me to provide. Currently, I'm mainly in the training division.

Maintenance of the overall cloud setup involved less than fifty people. They are not only for maintenance, as they are also involved with enhancing other solutions. I would include developers who are creating new products and releasing them. So overall, the staff is around fifty, but specifically for maintenance, there are fewer people.

Azure was one of the options during my evaluation, but I decided to go with AWS.

I would suggest newcomers go through the AWS Well-Architected Framework, especially the serverless application stack offered by AWS, such as Lambda. The cloud adoption framework by AWS is quite in-depth, providing many integration points for reskilling and handling workloads. It’s crucial to explore these frameworks before migrating workloads to the cloud. Additionally, looking into AWS CloudFormation for automating infrastructure provisioning is beneficial. I would rate AWS Lambda as nine out of ten.

Public Cloud

Amazon Web Services (AWS)

I use AWS Lambda for serverless services to avoid running applications on an instance. I use it for my personal website. When people want to view my website, the service is trigger-based, so it only operates when accessed, allowing for cost efficiency.

I like AWS Lambda as it is cheaper compared to other AWS services that require applications to be running continuously. Lambda is trigger-based, which means it only activates when accessed, ensuring cost savings. 

It's also serverless and makes processes easier despite the initial difficulty in setting it up. Being able to configure Lambda to work in a trigger-based manner is highly beneficial for my needs.

AWS Lambda is a bit difficult to set up if someone doesn't know how to code. It would be helpful to have a chatbot or resource that can guide users through the process, especially for those not proficient in coding. Setting up workflows that connect multiple AWS services can be challenging without guided support.

I've been using AWS Lambda for some months now.

There are no stability issues. AWS Lambda is very fast and secure, and I've not encountered any problems with it.

AWS Lambda's scalability depends on the number of users. It might not handle a high volume of requests efficiently, so it's better suited for small-scale operations where the number of users is limited.

The initial setup of AWS Lambda can be difficult, especially for those who do not know how to code. It took me about a week to set it up properly.

I am the only person maintaining the AWS Lambda setup.

I can't provide specific cost savings numbers, yet, due to the fact that AWS Lambda is trigger-based, it offers cost savings compared to instances that are always running.

AWS Lambda is cheaper compared to running an instance continuously. You only pay for what you use, making it cost-effective.

I would recommend AWS Lambda based on the use case. It's suitable for small applications where user access is limited. However, it's not ideal for large-scale platforms due to limitations in managing high traffic. 

I rate AWS Lambda a seven out of ten.

Public Cloud

Amazon Web Services (AWS)

For over a year, I worked in an IoT company, where we had some web applications. I used AWS Lambda in all of our devices for connection. During this time, I also taught AWS, using Lambda to help my students learn how the code of infrastructure works.

What I like most about AWS Lambda is that it's very easy to deploy. You basically have to click a couple of buttons, and suddenly you have a Lambda function that works. Obviously, then you have to write all the code, however, that it's a good service for beginners. 

Additionally, encapsulating code in Lambda functions makes applications easier to maintain and scale better than having one EC2 instance hosting everything. I think scalability and maintenance are major advantages. 

Also, the ability to work with different versions of Python is useful; sometimes I want to use different libraries unavailable in some versions.

The way Lambda works with HTTP headers is a bit odd. It needs some change as the configuration is somewhat hidden, so I don't really know what I'm changing. Managing the HTTP headers should be modified for clarity since, at least in my case, I found it challenging to configure and manage them initially. I would suggest that this is an area for improvement.

I have used Lambda for over a year.

I have never had issues with Lambda's stability. The first executions may have a bit of latency due to the cold start, however, once the Lambda is up, everything is much faster. It's a fast service, yet, in cases where the Lambda has been idle for a long time and has to restart, it's a bit slower. However, I think it's still something manageable.

I have found that Lambda has no problem with scalability. I can have many Lambdas working simultaneously. In case there is a traffic peak, AWS manages the scalability itself. I don't need to configure anything. It's all done by AWS, and for me, it has always worked perfectly. I have never seen any problem with that.

If I want a Lambda ready to go with no configurations, it's easy. I just click a few times, and it works. I don't need to know how it works underneath. However, if I want something more complex, for example, working with HTTP headers, it gets complex.

In the same way I used Lambda, I would say that no, I did not. It's a very unique service.

I would give it a nine out of ten.

The best thing to do is to pick a programming language you already know. Instead of starting with GitHub actions or workflows, it's better to make the Lambda public, start coding directly into the Lambda editor, and see how everything works. Try to understand how executions are logged into CloudTrail. 

When you gain more experience, obviously, Lambda should not be public, and you can make more complex things, like working with headers and authentication and connections to other AWS services.

AWS Lambda is primarily used for serverless architecture. I have used it for web applications, particularly during my time at a company where we developed an image processing tool. It helps to trigger events on a runtime basis. AWS Lambda also has various other use cases such as machine learning processes and handling big data tasks.

AWS Lambda's serverless architecture and event-driven programming are highly valuable. You pay only for the events triggered, optimizing costs. It is beneficial for deployment CI/CD tooling and for triggering pipelines for data processing. The cost model is pay-as-you-go, with a tier for free requests and computational time. Scalability and stability are also very efficient.

AWS Lambda could offer support for more programming languages beyond Python and Node.js. The process for creating Lambdas could be simplified for beginners with less expertise in AWS.

I have been working with AWS Lambda for almost eight years.

Deploying AWS Lambda is pretty quick if the process is known. It takes between 30 minutes to one hour to deploy once the code is ready.

AWS Lambda is very stable. Issues are typically related to the user’s code and can be debugged through logs such as CloudWatch.

AWS Lambda's auto-scaling features are crucial for operations and provide great scalability. I rate the scalability of AWS Lambda at eight out of ten.

The documentation for AWS Lambda is comprehensive and thorough, eliminating the need for frequent technical support. A 2000-page guide covers all aspects, from setup to advanced use.

Positive

The initial setup of AWS Lambda can be somewhat challenging for first-time users, primarily due to the learning curve involved. I would rate it a six on the ease scale.

AWS Lambda has a cost model that aligns with usage. There is an initial free tier, after which charges apply based on requests and compute time. Different architectures have distinct pricing models. Despite being a bit expensive initially, the overall cost effectiveness is notable.

AWS Lambda should be used when there is a need for event-driven programming on an as-needed basis. It might become expensive if used for continuous processes. In terms of the overall solution, I rate AWS Lambda at nine out of ten.

Public Cloud

Amazon Web Services (AWS)

I have used AWS Lambda for simple messaging for SQS, creating a cron job, and delay messaging. Creating a cron job ensures emails are automatically sent to users at a specific time we set. For example, a mail would be sent automatically instead of manually to a user whose subscription is about to expire.

AWS Lambda functions have many versions. Sometimes, it takes a little time to load and run the commands in AWS Lambda. We can write anything as code, but the solution will not give proper error information. It is a little time-consuming to understand where the error came from.

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

The solution's initial setup is easy. You can easily code in AWS Lambda if you have good coding experience in any coding language.

The solution is free of cost for the first year, and after that, it becomes expensive.

If you are using AWS Lambda for the first time, you need to know about AWS. You need good coding knowledge to work with AWS Lambda. If you want to code a node, you must know about node and node syntax. If we are using AWS Lambda with Python, you have to know about Python. It is easy to learn AWS Lambda if you have good coding knowledge.

Overall, I rate the solution a seven out of ten.

Public Cloud

Amazon Web Services (AWS)

I primarily use AWS Lambda for building serverless applications. I integrate it with services such as S3 and DynamoDB.

The capability of AWS Lambda to automatically scale thousands of requests simultaneously without requiring manual intervention is invaluable. 

Additionally, its integration with other AWS services such as Amazon S3 and DynamoDB has been very beneficial. The pay-as-you-go pricing model ensures that I only pay for the resources used, providing cost-effectiveness. 

The serverless computing feature eliminates the need to manage servers, provision, or scale.

AWS Lambda has a limitation where the execution time is capped at 15 minutes per task. Increasing this time would allow for handling heavier tasks more efficiently. 

While the documentation is satisfactory, beginners might encounter some challenges, so any improvements could be helpful.

I have been working with AWS Lambda for about six months.

AWS Lambda is quite stable, with a high level of availability and the ability to distribute functions across multiple availability zones. I'd rate it at nine out of ten in terms of stability.

AWS Lambda can automatically scale to handle increasing requests. For example, I used it in a personal project to integrate with a Simple Notification Service, which ensured I received real-time notifications during peak times without manual scaling.

I did not use a different solution before AWS Lambda.

The initial setup was moderately challenging, especially for beginners. Although the documentation is adequate, new users might find it not entirely straightforward and may face some challenges. It took me around five to ten minutes to deploy Lambda for the first time.

I deployed AWS Lambda by myself while learning about it and sought help from friends when necessary.

Lambda provides a cost-saving advantage because of its serverless design and automatic scaling without manual infrastructure needs. Its pay-as-you-go model means I only pay for what I use, which is financially beneficial for handling applications with fluctuating traffic levels.

AWS Lambda is cost-effective and flexible, particularly suitable for small applications. I am using the service within a free tier account, which influences the pricing experience.

I rate AWS Lambda overall at ten out of ten. New users should thoroughly read the documentation and start with small projects to understand the platform. It's important to be aware of AWS Lambda's limitations, take advantage of monitoring and debugging tools, and stay updated with new releases. Joining a community for learning and sharing is also recommended.

Public Cloud