AWS Lambda Reviews & Insights

We use AWS Lambda for a number of use-cases, such as an API endpoint behind AWS Cloudfront and also for data collection and evaluation before sending statistics on to our other internal systems. We have also used AWS Lambda to process messages on an SQS queue to allow a full serverless implementation of our microservices.

AWS Lambda is well suited for serverless microservices and can run independently of other services. AWS Lambda functions can be triggered by a number of external services, such as API calls, cron schedules, Cloudwatch event rules or via AWS Simple Notification Service (SNS) triggers. The service works well for API services however might be less suited to website hosting of systems like Wordpress.

-AWS Lambda is being used for cron job with AWS Event bridge.
-AWS Lambda can be also be used as a serverless function with api gateway.

-AWS Lambda can be used for cron job with AWS EventBridge.
-When we use AWS Lambda use with API Gateway to only transport data to another service without any business logic.

At my current place of work as well as for personal projects AWS Lamba is currently used for many different projects. A few examples are "image resizers", "data processing" and invalidating cache from a 3rd party webhook. It allows for quick, easy, and inexpensive setup and maintenance especially using 3rd party libraries like "Serverless".

A great example of using AWS Lambda is when your application needs to be able to render images for the user and have those images be resized and optimized on load. Using AWS Lambda you can create endpoints with a minimal amount of code that allows your applications to request the images and use query parameters to declare the height and width etc.

We use AWS Lambda (in conjunction with SAM) to produce a decentralized notification and delivery system for 3rd Party integrations to our SaaS product.

This allows us to keep throughput for messages in our app low, and scaling is nearly automatic and instantaneous. We needed a way for our app and data to be presented to a multitude of 3rd party applications and services and didn't want to make all these points of contact happen inside our main app. Therefore we chose a queue-based approach where our main app delivers messages to a queue and the Lambdas pick those messages up and process them until the queue is empty or more Lambdas are needed.

Lambdas have proven to be very cost-effective and prevents us from needing to incur uptime for other servers.

If you're not afraid to get your hands dirty in wiring things up yourselves or you can use AWS' own abstractions like Amplify or SAM, then Lambdas and their surrounding platform pairings (Like SQS, API gateway) are great tools that can help you create a backend or infrastructure for a relatively low cost. If you don't find yourself in that camp, but you know your way around full-stack JavaScript frameworks and tooling, you may be better served with a higher level abstraction like what Zeit Now or Netlify offers.

We use AWS Lambda to host our micro-services that don't need to worry about too much infrastructure. Lambdas are great at running pieces of code that don't necessarily have to belong in the main infrastructure. We have a few different lambdas that each have single responsibilities, such as creating and uploading files to S3, and running complex calculations.

Anywhere you have an isolated responsibility of your code, AWS Lambdas are well suited for. If you have something that has to perform an intensive calculation - it makes sense to offload that to something like an AWS Lambda. Or something that needs to send data and integrate with another service, it can be a good place for that interface/job to live.

It can be possible to build a larger architecture using a series of AWS Lambdas, but it could become hard to maintain and be hard to understand very quickly.

AWS Lambda is mostly being used to run our hourly/daily cron jobs. It is used across the entire organization. It helped us move data between external and internal data sources to the appropriate destinations.

AWS Lambda allows us to develop certain process without setting up a server. I would recommend AWS Lambda for a process that doesn't need to be real-time or needs to be always on.

It's good for a startup project because you do not need to spend too much money for the server and DevOps teams. With AWS Lambda, you do not need to worry about server managing, scaling, or managed infrastructure. It's free for 1 million requests per month, fewer developers are needed for the backend part, and it has fast deployment.

The engineering and data science teams at my organization use AWS Lambda to rapidly deliver features that are easy to maintain. We use Lambda with the Serverless framework, API Gateway, and DynamoDB to build managed micro-services that are easy to scale. We use Lambda with AWS Step Functions and S3 to build background jobs.

AWS Lambda is excellent for small organizations that want to focus on shipping features rather than maintaining infrastructure. Developers can iterate very rapidly using AWS Lambda, API Gateway, and the Serverless framework. AWS Lambda is not appropriate for some load patterns; services with uniformly high loads will be expensive to run on Lambda.

We use AWS Lambda to host our serverless lambda functions. It allows us to execute segments of our computational code that don't require actual virtual machines hosted on compute engines. This allows us to achieve very low costs for simple pieces of code that can be run quickly a large number of times.

AWS Lambda and Lambda functions, in general, are an amazing tool to run segments of code that don't need to be run actively. This allows us to save lots of money running code and only paying for it when it is actually run. Every company undoubtedly has code that can be run in this way for cost-saving. For companies that focus on computation, optimization, and other services, Lambda may not be the best, since it is unlikely that this code is executed quickly in "bursts".

AWS Lambdas are the true workhorses powering our ETL (Extract, Transform, and Load), Data Warehouse, and Business Intelligence solution. We integrate with dozens of registration platforms and third-party services, loading fully normalized customer data into RDS and Redshift, enabling machine learning, forecasting, CRM, marketing automation, business intelligence, and performance monitoring.

Well suited if:

• Your organization is fairly well established (see: runway)
• You're married to AWS Infrastructure
• You hate servers

Not well suited if:

• You aren't utilizing AWS's manages services
• Your organization is still in the boot-strapped stage (trying to run as lean as possible)