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.
$NaN
Per 1 ms
CoreOS rkt / Container Linux (project ended)
Score 7.0 out of 10
N/A
CoreOS rkt or Container Linux was a rival to Docker that was acquired by Red Hat, then given to the Cloud Native Computing Foundation (CNCF). The project has since been discontinued.
N/A
IBM Cloud Functions
Score 6.9 out of 10
N/A
IBM Cloud Functions is a PaaS platform based on Apache OpenWhisk. With it, developers write code (“actions”) that respond to external events. Actions are hosted, executed, and scaled on demand based on the number of events coming in. No servers or infrastructure to provision and manage.
$0
per second of execution
Pricing
AWS Lambda
CoreOS rkt / Container Linux (project ended)
IBM Cloud Functions
Editions & Modules
128 MB
$0.0000000021
Per 1 ms
1024 MB
$0.0000000167
Per 1 ms
10240 MB
$0.0000001667
Per 1 ms
No answers on this topic
Basic Cloud Functions Rate
$0.00017
per second of execution
API Gateway Rate
Free
Offerings
Pricing Offerings
AWS Lambda
CoreOS rkt / Container Linux (project ended)
IBM Cloud Functions
Free Trial
No
No
No
Free/Freemium Version
No
No
No
Premium Consulting/Integration Services
No
No
No
Entry-level Setup Fee
No setup fee
No setup fee
No setup fee
Additional Details
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More Pricing Information
Community Pulse
AWS Lambda
CoreOS rkt / Container Linux (project ended)
IBM Cloud Functions
Considered Multiple Products
AWS Lambda
No answer on this topic
CoreOS rkt / Container Linux (project ended)
No answer on this topic
IBM Cloud Functions
Verified User
Project Manager
Chose IBM Cloud Functions
AWS Lambda might be more suited for larger scaled companies looking to consistently access similar features at a higher volume/frequency, but for smaller teams with a limited budget, IBM's Cloud Functions are a competitive choice
AWS Lambda is 100 times more robust than IBM cloud functions. They essentially do the same thing, but AWS works. AWS is stable. we have had epic failures with cloud functions. Support was horrible. We literally had an open ticket with them for 2 months and it literally went …
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.
CoreOS rkt is well suited for any development environment where operating systems and hardware are not homogeneous. CoreOS rkt allows us to write code on one machine with the confidence that it will work on any other. This has been immensely helpful as our developers are often switching to the latest and greatest machines and operating systems. CoreOS rkt is less suited for environments that are not Software as a Service. There is often no need to bring the entire developer environment and associated dependencies when delivering a one time product. In these environments CoreOS rkt just adds unneeded overhead.
IBM Cloud Functions [is] not the worse product on the IBM cloud. I decided to write this review as I thought it would be balanced. I would still use functions to set up a serverless architecture where execution time is pretty quick and the code is relatively simple. I wouldn't use IBM Cloud Functions for async calls obviously, as costs could be higher. The functions documentation is lacking in terms of CI/CD, and there are unexplainable errors occurring - like the network connection that I mentioned. So I wouldn't just rely on IBM Cloud Functions too much for the entire system, but make sure it's diversified.
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.
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.
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
Docker, lxc, Ubuntu Snappy, partisan chroot+unshare Reformulating the problem and realizing a container is not necessary when a testing environment with clearly defined behavior.
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.