AWS Lambda is a serverless computing platform that lets users run code without provisioning or managing servers. With Lambda, users can run code for virtually any type of app or backend service—all with zero administration. It takes of requirements to run and scale code with high availability.
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IBM DevOps Deploy
Score 9.0 out of 10
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A solution for continuous delivery of any application to any environment, and an application-release solution that infuses automation into the continuous delivery and continuous deployment (CI/CD) process and provides robust visibility, traceability and auditing capabilities.
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Pricing
AWS Lambda
IBM DevOps Deploy
Editions & Modules
128 MB
$0.0000000021
Per 1 ms
1024 MB
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10240 MB
$0.0000001667
Per 1 ms
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AWS Lambda
IBM DevOps Deploy
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AWS Lambda
IBM DevOps Deploy
Features
AWS Lambda
IBM DevOps Deploy
Access Control and Security
Comparison of Access Control and Security features of Product A and Product B
Lambda excels at event-driven, short-lived tasks, such as processing files or building simple APIs. However, it's less ideal for long-running, computationally intensive, or applications that rely on carrying the state between jobs. Cold starts and constant load can easily balloon the costs.
IBM UrbanCode Deploy is excellent for code deployments such as Java, .Net, C++, etc. It can also deploy and run SQLs reasonably well. Where it lacks is the ability for executables, Jars, WARs, EARs, etc.
Developing test cases for Lambda functions can be difficult. For functions that require some sort of input it can be tough to develop the proper payload and event for a test.
For the uninitiated, deploying functions with Infrastructure as Code tools can be a challenging undertaking.
Logging the output of a function feels disjointed from running the function in the console. A tighter integration with operational logging would be appreciated, perhaps being able to view function logs from the Lambda console instead of having to navigate over to CloudWatch.
Sometimes its difficult to determine the correct permissions needed for Lambda execution from other AWS services.
IBM UrbanCode Deploy does code deployments easy enough, but configurations or ex deployments are a little more complicated. I work on packaged systems, so most of the code I get is form a vendor that I have to deploy.
IBM UebanCode Deploy integration into the mainframe world would be ideal. My company uses Mainframe and OpenSystems technologies, and many times there are dependencies between the deployments.
I give it a seven is usability because it's AWS. Their UI's are always clunkier than the competition and their documentation is rather cumbersome. There's SO MUCH to dig through and it's a gamble if you actually end up finding the corresponding info if it will actually help. Like I said before, going to google with a specific problem is likely a better route because AWS is quite ubiquitous and chances are you're not the first to encounter the problem. That being said, using SAM (Serverless application model) and it's SAM Local environment makes running local instances of your Lambdas in dev environments painless and quite fun. Using Nodejs + Lambda + SAM Local + VS Code debugger = AWESOME.
It's challenging to get a working knowledge of the product without having someone show you the ropes. Linking components with applications and applications with resource trees and resource trees with application deploys is not intuitive. However, once past that learning curve, the possibilities open up, and things become easier to understand and allow for further granularity.
Amazon consistently provides comprehensive and easy-to-parse documentation of all AWS features and services. Most development team members find what they need with a quick internet search of the AWS documentation available online. If you need advanced support, though, you might need to engage an AWS engineer, and that could be an unexpected (or unwelcome) expense.
AWS Lambda is good for short running functions, and ideally in response to events within AWS. Google App Engine is a more robust environment which can have complex code running for long periods of time, and across more than one instance of hardware. Google App Engine allows for both front-end and back-end infrastructure, while AWS Lambda is only for small back-end functions
Positive - Only paying for when code is run, unlike virtual machines where you pay always regardless of processing power usage.
Positive - Scalability and accommodating larger amounts of demand is much cheaper. Instead of scaling up virtual machines and increasing the prices you pay for that, you are just increasing the number of times your lambda function is run.
Negative - Debugging/troubleshooting, and developing for lambda functions take a bit more time to get used to, and migrating code from virtual machines and normal processes to Lambda functions can take a bit of time.
