I am currently using it mostly for our APIs. We don't really use any instance or any soft host for it. We just use purely serverless for our API operations. 

Before that, my previous company used it mainly for step functions. Some of our processes would trigger step functions, which in turn would trigger a series of Lambda functions to process the data.

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I use it for event-based retrieval. This is just to process for trigger purposes.

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My primary use case for AWS Lambda involves real-time data and media processing. Specifically, I use it to process user-uploaded content, including filtering, resizing, and applying effects to media files like images and videos. Lambda triggers these operations immediately upon file upload, ensuring minimal latency. One of the key advantages of using Lambda is its automatic scaling, which adjusts based on the volume of uploads. This means it can handle spikes in traffic without the need for provisioning and managing dedicated infrastructure, allowing for cost-effective operations during periods of high demand. Additionally, by leveraging Lambda, we can quickly deploy new processing workflows without affecting the existing infrastructure.

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I have been using AWS Lambda for event-driven computing. I have implemented it for use cases such as serving API microservices, especially for workloads that are not very high frequency and do not require managing infrastructure 24/7. We have integrated it with other AWS services like API Gateway, DynamoDB, step functions, and SQS for seamless workflows. View full review »

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.

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

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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.

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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.

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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.

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We primarily use AWS Lambda for our APIs. Most of our transactions flow through the API, which is managed using API Gateway and AWS Lambda. Our development team works on the API.

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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. View full review »

We use AWS Lambda for various tasks, such as triggering reports, queueing calls for displays on our dashboard, and integrating with environmental variables for different outputs.

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

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We use it primarily for image resizing in batch and scheduled jobs. Additionally, one of our clients relies on Lambda for running a continuously active custom API, handling their ongoing API requests.

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There's a lot of use cases. The solution is used for immediate processing, so it's used everywhere. When data comes in, you process a Lambda function to tag it and categorize it. I've used Lambda functions to process inbound videos to make them smaller. I've used a Lambda function as a serverless backend for a customer-facing app, so I didn't have servers running.

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We have one or two use cases for real-time file processing. We use the event triggers to detect file arrival.

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Lambda is essentially a serverless computing service offered by AWS. It allows you to run your code or applications without needing to manage servers. You can deploy your IT code or applications, and it seamlessly integrates with other AWS services, like S3 for data storage. You don't need to purchase or manage any servers. So, Lambda offers a way to run applications without the complexity of infrastructure.

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We are building microservices in Lambda and exposing them via an API Gateway for the end consumer. These microservices talk to DynamoDB, or third-party APIs but can also be event-driven. These microservices carry out specific business functionalities.

We have plans to increase usage in the future. We have been modernizing our usage of SOA technology and rewriting those SOA services on the cloud using AWS Lambda. We are transitioning to a serverless environment using Lambda. 

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AWS Lambda is a serverless computing service provided by AWS. It is allowed to run the course without the provisioning or managing server and paying only for the compute time consumed during execution.

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I have to send daily reports. We have many child accounts in AWS Organizations. We need reports on the cost of the accounts. I use AWS Lambda because we have to run the code without provisioning the servers. AWS Lambda is a serverless computing service.

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Our primary use case for AWS Lambda is a backend service module. Thanks to this solution, we do not need to worry about hardware or resource utilization. It saves us time. 

It saves us a lot of setup time. And, because it's our code, we will use Lambda. Lambda is the best AWS product we can use for running our code and it is very quick for the developers. All they have to do is commit their codes and, once we set up our code pipeline once after committing the code and building and deploying to our Lambda, the project will be up and running in the market. This setup is better than managing on EC2. We don't need to use EC2 in this case. 

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Lambda is basically a serverless computation service. We are partners with AWS and I'm a consultant. 

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AWS Lambda is good because if you chop up your application into small pieces and that is something that can be accomplished as a Lambda-based solution on the cloud, you will save on the cost of your applications.

The reason Amazon AWS has provided AWS Lambda is to allow people to write small pieces of application tasks that will run on Amazon's own internal servers.

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We primarily use the solution for integration purposes. We use it very closely with Jira, for example. 

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I am using AWS Lambda for building web and mobile applications.

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I use AWS Lambda for serverless computing, and I am charged only for the AWS Lambda instances I use. I value its cost efficiency because it activates instances based on load and tasks initiated. View full review »

AWS Lambda is a serverless architecture. So, you can use three languages like Java, Python, and Node. You can write your code in any language. The main use is if you have a team and your team has one Java developer, one Python developer, or one JavaScript developer. You all want to work together and in a single project, you can use multiple languages, by any of the users.

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My prior company used the solution to perform event-based actions and notifications, process data in an S3 bucket, and send messages in the SQS queue. 

Our development team worked with 300 users across different departments to integrate the solution. 

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We use AWS Lambda extensively for our maintenance work, for our products, and in our daily actions. We try to move some data based on alerts in certain situations and events. For example, if we are using queues based on the queue methods, we prefer to trigger different Lambdas for different functions (to enable some functionality across products). There are also a few Lambdas for audits. There are a few Lambdas for backups and many other use cases. 

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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. 

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

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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.

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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.

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

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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.

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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.

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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.

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Essentially, I use AWS Lambda to run Python code. Usually, I set up triggers for other parts of AWS. It's really basic programming tasks.

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We are building a Twitter-like application in the boot camp. I have used Lamda for the integration of the post-confirmation page in the application. This will help you get your one-time password via mail. You can log in with the help of a post-confirmation page. We didn’t want to set up an instance specifically for confirmation. We used the Lambda function so that it goes back to sleep after pushing up.

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We're using AWS Lambda to trigger jobs and receive SQS messages. We're also using the tool as a single unit where you have computing, storage, and network.

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Our primary use case for the solution is automation. There was a scenario where we tried to reduce cost in our environment using Instance Scheduler, where the AWS Lambda function was also used during that process. So we use it for automation and tagging.

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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. 

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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.

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We have a DNS as a service and NAS as a service, which integrates, and we use Lambda functions there. We have a lot of applications that we use Lambda for. 

This solution is cloud-based. 

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We are primarily using AWS Lambda for real-time API services. We use AWS Redshift to support our Lambda code functions. 

This solution is cloud-based. 

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My primary use case for this solution is usually for event-driven architecture. Since it's AWS, it's cloud-based. 

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AWS Lambda has serverless programming, like Logic Apps from Azure. You just configure the run-time and then they start coding. It is event-driven. It started with my obtaining Salesforce. Salesforce is a low-code and non-code program and totally SAS. Everything starts from the event, from the trigger. You get the trigger and you work at the program. You have some other models, maybe faster or fancier models. But in my opinion, this kind of program is started by locating the system and identifying where the trigger and entry point of the program are. Then you get the full advantage of the program. You don't need to worry about any infrastructure.

I think this is the future. Compared with the EC2, you don't have to pay anything if you don't run it. Otherwise, with EC2 when our client provisions the system and the instances, you always have to pay. There are other tremendous advantages, like flexibility. After you provision EC2 you can write something that does not totally follow the cloud convention. You use it to provision the container. With the program you need to have those 10 principles of cloud computing. Especially recently, within the past four or five years, I have gotten away from DevOps, or the software development life cycle. Even though I researched the product portfolio from DevOps and then the life cycle for DevOps, I try to position myself as an architect with hands-on experience.

In my opinion, Lambda is very similar to Salesforce, which is the original for the SaaS platform and is an extremely low-code environment. With Microsoft and AWS you can say, "Okay. You can choose whatever language you need to make it even more flexible." 

Everything is the cloud. Lambda is a fully managed service. If you want to do it either as a private cloud or on-premise, I'm sure you can do that, too. But I don't know how to manage the pricing structure. But then you've lost the point of Lambda because if you do not use it, you do not pay. Again, I just want to emphasize, I'm not a Lambda expert. But, logically thinking, the big advantage of serverless programming for the customer is that you just use it and pay. Pay and go. You don't need to provision anything.

All my experience with AWS Azure is on the public cloud. We do not get too deep. In IBM we do. When we do sales training we always get the private cloud on-premise. There are many reasons for this. One reason is that IBM lost the battle for the public cloud so we get into it much deeper. We go to the enterprise and we can deploy programs to your data center and offices. But for the tech data for AWS and Azure, we are all using the public cloud as a showcase when we talk to the customer and to the retailer.

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We have used Lambda for

batch processing for specific schedules.

different bot functions that run regularly,

Application business logic

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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.

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We had to deploy some serverless Node.js applications.

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AWS Lambda is used for the whole surface, it does the backups, and schedules, and learns some of the core functionalities but it can depend on the topic or application. The solution is used to build APIs and many other functions 

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

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We use it for serverless functions.

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We use AWS Lambda for several things. We are using it, for instance, to do authentication of information from HTTP sites. We use it for alerting when monitoring the direct database infrastructure, and we use it for API transformation.

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Lambda can be used for automating AWS resources. 

It can also be used for automation outside of the cloud and for serverless applications. With Lambda, you can apply the code directly. 

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We primarily use the solution for our backup coding for one of the ABDI services. We had a big job for the callout function, therefore we implemented Lambda here.

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The primary use case is processing data. We use the pipeline multiple places to process the data. Whatever JSON files we get, we have to standardize, enrich, and also format to the application.

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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. 

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The product serves as a function as a service, a serverless environment, you can say. It's a serverless environment, or, as some people call it, function as a service, FaaS.

We have been using it as a mobile backend. We have a mobile frontend, a mobile application, which uses the AWS Lambda functions running in the cloud. It serves as an API backend for a mobile application that is running in the frontend.

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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.

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Our website is hosted on AWS, and we use Lambda to create trigger points.

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We mainly use AWS Lambda for data transformation and authentication.

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We are using the latest version.

We use the solution for the building of small applications.

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We use AWS Lambda to automatically manages some compute resources.

It triggers and screens some of my services.

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The product is primarily used to deploy code and provision a software solution to your clients when they don't have the time. You don't have to pay for the servers and the uptime.

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It is useful in many scenarios. For example, in a microservices architecture where serverless functionality is required, one can use Lambda.

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We have some APIs and we use some mechanisms to process these APIs. Normally, some APIs need to be hosted by some servers. However, with this product, we can compute everything serverless.

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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. 

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Primarily, I work with all my clients to provide them with solutions. We are a service company, so we work with clients to define and build applications that resolve their need for automation issues. I create the solutions, and then there is a delivery team of mine which works to deliver that solution to the client.  

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We have only used it for a few services. It's still in POC mode. We haven't done any production on it currently. 

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We use this solution for a mobile banking application with the ability to scale as per demand and to focus on core business functions rather than the platform.

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Our primary use case for Lambda is for serverless computing in our project. We have an environment in AWS with Lambda, EC2, S3, SQS, RDS, and Redshift. Our Lambda function is triggered whenever a new object is put into S3. It will validate, extract data from S3 then input metadata into MySQL and put the main data into Redshift as the data warehouse. An SQS message will be created so our Application in EC2 is alerted that there is new data to have processed.

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I back up my MongoDB, that database has user data. The performance of AWS Lambda has been fine.

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1. Use lambda function to trigger the jenkins jobs to make whole release pipeline.

2. Send the slack notification once the release with automated test is done.

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My primary use case for AWS Lambda involves completing tasks assigned by my teacher, developing projects with event-driven architecture, server management, auto-scaling, supporting multiple languages, and integrating with AWS services. I also use it for building APIs, file processing, data transformation, IoT applications, and scheduling tasks. View full review »

It is used for capturing data through an API.

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I am using AWS Lambda to set up real-time notifications and backup transfers.

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AWS Lambda is a Code as a Service product. It is used for running code.

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